pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs
Authors: Tianlin Liu, Arvid Kappas
Date: 2018-05-02 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we describe our approach for the OMG- Emotion Challenge 2018.
The goal is to produce utterance-level valence and arousal estimations for
videos of approximately 1 minute length. We tackle this problem by first
extracting facial expressions features of videos as time series data, and then
using Recurrent Neural Networks of the Echo State Network type to model the
correspondence between the time series data and valence-arousal values.
Experimentally we show that the proposed approach surpasses the baseline
methods provided by the organizers.
Learnable PINs: Cross-Modal Embeddings for Person Identity
Authors: Arsha Nagrani, Samuel Albanie, Andrew Zisserman
Date: 2018-05-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Cross-Modal Retrieval, Retrieval

We propose and investigate an identity sensitive joint embedding of face and
voice. Such an embedding enables cross-modal retrieval from voice to face and
from face to voice. We make the following four contributions: first, we show
that the embedding can be learnt from videos of talking faces, without
requiring any identity labels, using a form of cross-modal self-supervision;
second, we develop a curriculum learning schedule for hard negative mining
targeted to this task, that is essential for learning to proceed successfully;
third, we demonstrate and evaluate cross-modal retrieval for identities unseen
and unheard during training over a number of scenarios and establish a
benchmark for this novel task; finally, we show an application of using the
joint embedding for automatically retrieving and labelling characters in TV
dramas.
An Evaluation of Classification and Outlier Detection Algorithms
Authors: Victoria J. Hodge, Jim Austin
Date: 2018-05-02 00:00:00
Tasks: Classification, General Classification, Outlier Detection, Time Series, Time Series Analysis

This paper evaluates algorithms for classification and outlier detection
accuracies in temporal data. We focus on algorithms that train and classify
rapidly and can be used for systems that need to incorporate new data
regularly. Hence, we compare the accuracy of six fast algorithms using a range
of well-known time-series datasets. The analyses demonstrate that the choice of
algorithm is task and data specific but that we can derive heuristics for
choosing. Gradient Boosting Machines are generally best for classification but
there is no single winner for outlier detection though Gradient Boosting
Machines (again) and Random Forest are better. Hence, we recommend running
evaluations of a number of algorithms using our heuristics.
Markov Chain Neural Networks
Authors: Maren Awiszus, Bodo Rosenhahn
Date: 2018-05-02 00:00:00

In this work we present a modified neural network model which is capable to
simulate Markov Chains. We show how to express and train such a network, how to
ensure given statistical properties reflected in the training data and we
demonstrate several applications where the network produces non-deterministic
outcomes. One example is a random walker model, e.g. useful for simulation of
Brownian motions or a natural Tic-Tac-Toe network which ensures
non-deterministic game behavior.
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
Authors: Toon Van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel
Date: 2018-05-02 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Clustering is ubiquitous in data analysis, including analysis of time series.
It is inherently subjective: different users may prefer different clusterings
for a particular dataset. Semi-supervised clustering addresses this by allowing
the user to provide examples of instances that should (not) be in the same
cluster. This paper studies semi-supervised clustering in the context of time
series. We show that COBRAS, a state-of-the-art semi-supervised clustering
method, can be adapted to this setting. We refer to this approach as COBRAS-TS.
An extensive experimental evaluation supports the following claims: (1)
COBRAS-TS far outperforms the current state of the art in semi-supervised
clustering for time series, and thus presents a new baseline for the field; (2)
COBRAS-TS can identify clusters with separated components; (3) COBRAS-TS can
identify clusters that are characterized by small local patterns; (4) a small
amount of semi-supervision can greatly improve clustering quality for time
series; (5) the choice of the clustering algorithm matters (contrary to earlier
claims in the literature).
Aspect Term Extraction with History Attention and Selective Transformation
Authors: Xin Li, Lidong Bing, Piji Li, Wai Lam, Zhimou Yang
Date: 2018-05-02 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Prediction, Sentence, Sentiment Analysis, Term Extraction

Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment
Analysis, aims to extract explicit aspect expressions from online user reviews.
We present a new framework for tackling ATE. It can exploit two useful clues,
namely opinion summary and aspect detection history. Opinion summary is
distilled from the whole input sentence, conditioned on each current token for
aspect prediction, and thus the tailor-made summary can help aspect prediction
on this token. Another clue is the information of aspect detection history, and
it is distilled from the previous aspect predictions so as to leverage the
coordinate structure and tagging schema constraints to upgrade the aspect
prediction. Experimental results over four benchmark datasets clearly
demonstrate that our framework can outperform all state-of-the-art methods.
From the Periphery to the Center: Information Brokerage in an Evolving Network
Authors: Bo Yan, Yiping Liu, Jiamou Liu, Yijin Cai, Hongyi Su, Hong Zheng
Date: 2018-05-02 00:00:00

Interpersonal ties are pivotal to individual efficacy, status and performance
in an agent society. This paper explores three important and interrelated
themes in social network theory: the center/periphery partition of the network;
network dynamics; and social integration of newcomers. We tackle the question:
How would a newcomer harness information brokerage to integrate into a dynamic
network going from periphery to center? We model integration as the interplay
between the newcomer and the dynamics network and capture information brokerage
using a process of relationship building. We analyze theoretical guarantees for
the newcomer to reach the center through tactics; proving that a winning tactic
always exists for certain types of network dynamics. We then propose three
tactics and show their superior performance over alternative methods on four
real-world datasets and four network models. In general, our tactics place the
newcomer to the center by adding very few new edges on dynamic networks with
approximately 14000 nodes.
CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal
Authors: Kunal Swami, Saikat Kumar Das
Date: 2018-01-09 00:00:00
Tasks: Single Image Haze Removal

Single image haze removal is a very challenging and ill-posed problem. The
existing haze removal methods in literature, including the recently introduced
deep learning methods, model the problem of haze removal as that of estimating
intermediate parameters, viz., scene transmission map and atmospheric light.
These are used to compute the haze-free image from the hazy input image. Such
an approach only focuses on accurate estimation of intermediate parameters,
while the aesthetic quality of the haze-free image is unaccounted for in the
optimization framework. Thus, errors in the estimation of intermediate
parameters often lead to generation of inferior quality haze-free images. In
this paper, we present CANDY (Conditional Adversarial Networks based Dehazing
of hazY images), a fully end-to-end model which directly generates a clean
haze-free image from a hazy input image. CANDY also incorporates the visual
quality of haze-free image into the optimization function; thus, generating a
superior quality haze-free image. To the best of our knowledge, this is the
first work in literature to propose a fully end-to-end model for single image
haze removal. Also, this is the first work to explore the newly introduced
concept of generative adversarial networks for the problem of single image haze
removal. The proposed model CANDY was trained on a synthetically created haze
image dataset, while evaluation was performed on challenging synthetic as well
as real haze image datasets. The extensive evaluation and comparison results of
CANDY reveal that it significantly outperforms existing state-of-the-art haze
removal methods in literature, both quantitatively as well as qualitatively.
KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction
Authors: Hengyi Cai, Xingguang Ji, Yonghao Song, Yan Jin, Yang Zhang, Mairgup Mansur, Xiaofang Zhao
Date: 2018-05-02 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Chinese pinyin input methods are very important for Chinese language
processing. Actually, users may make typos inevitably when they input pinyin.
Moreover, pinyin typo correction has become an increasingly important task with
the popularity of smartphones and the mobile Internet. How to exploit the
knowledge of users typing behaviors and support the typo correction for acronym
pinyin remains a challenging problem. To tackle these challenges, we propose
KNPTC, a novel approach based on neural machine translation (NMT). In contrast
to previous work, KNPTC is able to integrate explicit knowledge into NMT for
pinyin typo correction, and is able to learn to correct a variety of typos
without the guidance of manually selected constraints or languagespecific
features. In this approach, we first obtain the transition probabilities
between adjacent letters based on large-scale real-life datasets. Then, we
construct the "ground-truth" alignments of training sentence pairs by utilizing
these probabilities. Furthermore, these alignments are integrated into NMT to
capture sensible pinyin typo correction patterns. KNPTC is applied to correct
typos in real-life datasets, which achieves 32.77% increment on average in
accuracy rate of typo correction compared against the state-of-the-art system.
Exploring Emoji Usage and Prediction Through a Temporal Variation Lens
Authors: Francesco Barbieri, Luis Marujo, Pradeep Karuturi, William Brendel, Horacio Saggion
Date: 2018-05-02 00:00:00

The frequent use of Emojis on social media platforms has created a new form
of multimodal social interaction. Developing methods for the study and
representation of emoji semantics helps to improve future multimodal
communication systems. In this paper, we explore the usage and semantics of
emojis over time. We compare emoji embeddings trained on a corpus of different
seasons and show that some emojis are used differently depending on the time of
the year. Moreover, we propose a method to take into account the time
information for emoji prediction systems, outperforming state-of-the-art
systems. We show that, using the time information, the accuracy of some emojis
can be significantly improved.
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Authors: Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, Sebastian Risi
Date: 2018-05-02 00:00:00
Tasks: Generative Adversarial Network, SNES Games

Generative Adversarial Networks (GANs) are a machine learning approach
capable of generating novel example outputs across a space of provided training
examples. Procedural Content Generation (PCG) of levels for video games could
benefit from such models, especially for games where there is a pre-existing
corpus of levels to emulate. This paper trains a GAN to generate levels for
Super Mario Bros using a level from the Video Game Level Corpus. The approach
successfully generates a variety of levels similar to one in the original
corpus, but is further improved by application of the Covariance Matrix
Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions
are used to discover levels within the latent space of the GAN that maximize
desired properties. Simple static properties are optimized, such as a given
distribution of tile types. Additionally, the champion A* agent from the 2009
Mario AI competition is used to assess whether a level is playable, and how
many jumping actions are required to beat it. These fitness functions allow for
the discovery of levels that exist within the space of examples designed by
experts, and also guide the search towards levels that fulfill one or more
specified objectives.
Joint Surgical Gesture and Task Classification with Multi-Task and Multimodal Learning
Authors: Duygu Sarikaya, Khurshid A. Guru, Jason J. Corso
Date: 2018-05-02 00:00:00
Tasks: General Classification, Multi-Task Learning

We propose a novel multi-modal and multi-task architecture for simultaneous
low level gesture and surgical task classification in Robot Assisted Surgery
(RAS) videos.Our end-to-end architecture is based on the principles of a long
short-term memory network (LSTM) that jointly learns temporal dynamics on rich
representations of visual and motion features, while simultaneously classifying
activities of low-level gestures and surgical tasks. Our experimental results
show that our approach is superior compared to an ar- chitecture that
classifies the gestures and surgical tasks separately on visual cues and motion
cues respectively. We train our model on a fixed random set of 1200 gesture
video segments and use the rest 422 for testing. This results in around 42,000
gesture frames sampled for training and 14,500 for testing. For a 6 split
experimentation, while the conventional approach reaches an Average Precision
(AP) of only 29% (29.13%), our architecture reaches an AP of 51% (50.83%) for 3
tasks and 14 possible gesture labels, resulting in an improvement of 22%
(21.7%). Our architecture learns temporal dynamics on rich representations of
visual and motion features that compliment each other for classification of
low-level gestures and surgical tasks. Its multi-task learning nature makes use
of learned joint re- lationships and combinations of shared and task-specific
representations. While benchmark studies focus on recognizing gestures that
take place under specific tasks, we focus on recognizing common gestures that
reoccur across different tasks and settings and significantly perform better
compared to conventional architectures.
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
Authors: Firoj Alam, Shafiq Joty, Muhammad Imran
Date: 2018-05-02 00:00:00
Tasks: General Classification, Humanitarian

During time-critical situations such as natural disasters, rapid
classification of data posted on social networks by affected people is useful
for humanitarian organizations to gain situational awareness and to plan
response efforts. However, the scarcity of labeled data in the early hours of a
crisis hinders machine learning tasks thus delays crisis response. In this
work, we propose to use an inductive semi-supervised technique to utilize
unlabeled data, which is often abundant at the onset of a crisis event, along
with fewer labeled data. Specif- ically, we adopt a graph-based deep learning
framework to learn an inductive semi-supervised model. We use two real-world
crisis datasets from Twitter to evaluate the proposed approach. Our results
show significant improvements using unlabeled data as compared to only using
labeled data.
Utilizing Device-level Demand Forecasting for Flexibility Markets - Full Version
Authors: Bijay Neupane, Torben Bach Pedersen, Bo Thiesson
Date: 2018-05-02 00:00:00
Tasks: Demand Forecasting, Scheduling

The uncertainty in the power supply due to fluctuating Renewable Energy
Sources (RES) has severe (financial and other) implications for energy market
players. In this paper, we present a device-level Demand Response (DR) scheme
that captures the atomic (all available) flexibilities in energy demand and
provides the largest possible solution space to generate demand/supply
schedules that minimize market imbalances. We evaluate the effectiveness and
feasibility of widely used forecasting models for device-level flexibility
analysis. In a typical device-level flexibility forecast, a market player is
more concerned with the \textit{utility} that the demand flexibility brings to
the market, rather than the intrinsic forecast accuracy. In this regard, we
provide comprehensive predictive modeling and scheduling of demand flexibility
from household appliances to demonstrate the (financial and otherwise)
viability of introducing flexibility-based DR in the Danish/Nordic market.
Further, we investigate the correlation between the potential utility and the
accuracy of the demand forecast model. Furthermore, we perform a number of
experiments to determine the data granularity that provides the best financial
reward to market players for adopting the proposed DR scheme. A cost-benefit
analysis of forecast results shows that even with somewhat low forecast
accuracy, market players can achieve regulation cost savings of 54% of the
theoretically optimal.
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Authors: Noha Radwan, Abhinav Valada, Wolfram Burgard
Date: 2018-04-23 00:00:00
Tasks: Outdoor Localization, Scene Understanding, State Estimation, Visual Localization

Semantic understanding and localization are fundamental enablers of robot
autonomy that have for the most part been tackled as disjoint problems. While
deep learning has enabled recent breakthroughs across a wide spectrum of scene
understanding tasks, its applicability to state estimation tasks has been
limited due to the direct formulation that renders it incapable of encoding
scene-specific constrains. In this work, we propose the VLocNet++ architecture
that employs a multitask learning approach to exploit the inter-task
relationship between learning semantics, regressing 6-DoF global pose and
odometry, for the mutual benefit of each of these tasks. Our network overcomes
the aforementioned limitation by simultaneously embedding geometric and
semantic knowledge of the world into the pose regression network. We propose a
novel adaptive weighted fusion layer to aggregate motion-specific temporal
information and to fuse semantic features into the localization stream based on
region activations. Furthermore, we propose a self-supervised warping technique
that uses the relative motion to warp intermediate network representations in
the segmentation stream for learning consistent semantics. Finally, we
introduce a first-of-a-kind urban outdoor localization dataset with pixel-level
semantic labels and multiple loops for training deep networks. Extensive
experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc
dataset demonstrate that our approach exceeds the state-of-the-art
outperforming local feature-based methods while simultaneously performing
multiple tasks and exhibiting substantial robustness in challenging scenarios.
Compressed Dictionary Learning
Authors: Karin Schnass, Flavio Teixeira
Date: 2018-05-02 00:00:00
Tasks: Dictionary Learning, Dimensionality Reduction, LEMMA

In this paper we show that the computational complexity of the Iterative Thresholding and K-residual-Means (ITKrM) algorithm for dictionary learning can be significantly reduced by using dimensionality-reduction techniques based on the Johnson-Lindenstrauss lemma. The dimensionality reduction is efficiently carried out with the fast Fourier transform. We introduce the Iterative compressed-Thresholding and K-Means (IcTKM) algorithm for fast dictionary learning and study its convergence properties. We show that IcTKM can locally recover an incoherent, overcomplete generating dictionary of $K$ atoms from training signals of sparsity level $S$ with high probability. Fast dictionary learning is achieved by embedding the training data and the dictionary into $m < d$ dimensions, and recovery is shown to be locally stable with an embedding dimension which scales as low as $m = O(S \log^4 S \log^3 K)$. The compression effectively shatters the data dimension bottleneck in the computational cost of ITKrM, reducing it by a factor $O(m/d)$. Our theoretical results are complemented with numerical simulations which demonstrate that IcTKM is a powerful, low-cost algorithm for learning dictionaries from high-dimensional data sets.
SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?
Authors: Oscar Mendez, Simon Hadfield, Nicolas Pugeault, Richard Bowden
Date: 2017-09-05 00:00:00

How does a person work out their location using a floorplan? It is probably
safe to say that we do not explicitly measure depths to every visible surface
and try to match them against different pose estimates in the floorplan. And
yet, this is exactly how most robotic scan-matching algorithms operate.
Similarly, we do not extrude the 2D geometry present in the floorplan into 3D
and try to align it to the real-world. And yet, this is how most vision-based
approaches localise.
  Humans do the exact opposite. Instead of depth, we use high level semantic
cues. Instead of extruding the floorplan up into the third dimension, we
collapse the 3D world into a 2D representation. Evidence of this is that many
of the floorplans we use in everyday life are not accurate, opting instead for
high levels of discriminative landmarks.
  In this work, we use this insight to present a global localisation approach
that relies solely on the semantic labels present in the floorplan and
extracted from RGB images. While our approach is able to use range measurements
if available, we demonstrate that they are unnecessary as we can achieve
results comparable to state-of-the-art without them.
Text to Image Synthesis Using Generative Adversarial Networks
Authors: Cristian Bodnar
Date: 2018-05-02 00:00:00
Tasks: Conditional Image Generation, Image Generation, Sentence

Generating images from natural language is one of the primary applications of
recent conditional generative models. Besides testing our ability to model
conditional, highly dimensional distributions, text to image synthesis has many
exciting and practical applications such as photo editing or computer-aided
content creation. Recent progress has been made using Generative Adversarial
Networks (GANs). This material starts with a gentle introduction to these
topics and discusses the existent state of the art models. Moreover, I propose
Wasserstein GAN-CLS, a new model for conditional image generation based on the
Wasserstein distance which offers guarantees of stability. Then, I show how the
novel loss function of Wasserstein GAN-CLS can be used in a Conditional
Progressive Growing GAN. In combination with the proposed loss, the model
boosts by 7.07% the best Inception Score (on the Caltech birds dataset) of the
models which use only the sentence-level visual semantics. The only model which
performs better than the Conditional Wasserstein Progressive Growing GAN is the
recently proposed AttnGAN which uses word-level visual semantics as well.