pwc-archive/papers-with-abstracts huggingface dataset:
Improving the Modularity of AUV Control Systems using Behaviour Trees
Authors: Christopher Iliffe Sprague, Özer Özkahraman, Andrea Munafo, Rachel Marlow, Alexander Phillips, Petter Ögren
Date: 2018-11-01 00:00:00

In this paper, we show how behaviour trees (BTs) can be used to design
modular, versatile, and robust control architectures for mission-critical
systems. In particular, we show this in the context of autonomous underwater
vehicles (AUVs). Robustness, in terms of system safety, is important since
manual recovery of AUVs is often extremely difficult. Further more, versatility
is important to be able to execute many different kinds of missions. Finally,
modularity is needed to achieve a combination of robustness and versatility, as
the complexity of a versatile systems needs to be encapsulated in modules, in
order to create a simple overall structure enabling robustness analysis. The
proposed design is illustrated using a typical AUV mission.
Distributed ReliefF based Feature Selection in Spark
Authors: Raul-Jose Palma-Mendoza, Daniel Rodriguez, Luis de-Marcos
Date: 2018-11-01 00:00:00
Tasks: feature selection

Feature selection (FS) is a key research area in the machine learning and
data mining fields, removing irrelevant and redundant features usually helps to
reduce the effort required to process a dataset while maintaining or even
improving the processing algorithm's accuracy. However, traditional algorithms
designed for executing on a single machine lack scalability to deal with the
increasing amount of data that has become available in the current Big Data
era. ReliefF is one of the most important algorithms successfully implemented
in many FS applications. In this paper, we present a completely redesigned
distributed version of the popular ReliefF algorithm based on the novel Spark
cluster computing model that we have called DiReliefF. Spark is increasing its
popularity due to its much faster processing times compared with Hadoop's
MapReduce model implementation. The effectiveness of our proposal is tested on
four publicly available datasets, all of them with a large number of instances
and two of them with also a large number of features. Subsets of these datasets
were also used to compare the results to a non-distributed implementation of
the algorithm. The results show that the non-distributed implementation is
unable to handle such large volumes of data without specialized hardware, while
our design can process them in a scalable way with much better processing times
and memory usage.
Multiplicative Latent Force Models
Authors: Daniel J. Tait, Bruce J. Worton
Date: 2018-11-01 00:00:00

Bayesian modelling of dynamic systems must achieve a compromise between
providing a complete mechanistic specification of the process while retaining
the flexibility to handle those situations in which data is sparse relative to
model complexity, or a full specification is hard to motivate. Latent force
models achieve this dual aim by specifying a parsimonious linear evolution
equation which an additive latent Gaussian process (GP) forcing term. In this
work we extend the latent force framework to allow for multiplicative
interactions between the GP and the latent states leading to more control over
the geometry of the trajectories. Unfortunately inference is no longer
straightforward and so we introduce an approximation based on the method of
successive approximations and examine its performance using a simulation study.
Dilated DenseNets for Relational Reasoning
Authors: Antreas Antoniou, Agnieszka Słowik, Elliot J. Crowley, Amos Storkey
Date: 2018-11-01 00:00:00
Tasks: Relational Reasoning

Despite their impressive performance in many tasks, deep neural networks
often struggle at relational reasoning. This has recently been remedied with
the introduction of a plug-in relational module that considers relations
between pairs of objects. Unfortunately, this is combinatorially expensive. In
this extended abstract, we show that a DenseNet incorporating dilated
convolutions excels at relational reasoning on the Sort-of-CLEVR dataset,
allowing us to forgo this relational module and its associated expense.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Authors: Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria
Date: 2018-11-01 00:00:00
Tasks: Emotion Classification, Emotion Recognition in Conversation, General Classification, Multimodal Emotion Recognition

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently, systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state of the art by a significant margin on two different datasets.
Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models
Authors: Herman Kamper
Date: 2018-11-01 00:00:00
Tasks: Decoder, Word Embeddings

We investigate unsupervised models that can map a variable-duration speech
segment to a fixed-dimensional representation. In settings where unlabelled
speech is the only available resource, such acoustic word embeddings can form
the basis for "zero-resource" speech search, discovery and indexing systems.
Most existing unsupervised embedding methods still use some supervision, such
as word or phoneme boundaries. Here we propose the encoder-decoder
correspondence autoencoder (EncDec-CAE), which, instead of true word segments,
uses automatically discovered segments: an unsupervised term discovery system
finds pairs of words of the same unknown type, and the EncDec-CAE is trained to
reconstruct one word given the other as input. We compare it to a standard
encoder-decoder autoencoder (AE), a variational AE with a prior over its latent
embedding, and downsampling. EncDec-CAE outperforms its closest competitor by
24% relative in average precision on two languages in a word discrimination
task.
Excessive Invariance Causes Adversarial Vulnerability
Authors: Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
Date: 2018-11-01 00:00:00
Proceeding: ICLR 2019 5

Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.
Continuous-time Intensity Estimation Using Event Cameras
Authors: Cedric Scheerlinck, Nick Barnes, Robert Mahony
Date: 2018-11-01 00:00:00

Event cameras provide asynchronous, data-driven measurements of local
temporal contrast over a large dynamic range with extremely high temporal
resolution. Conventional cameras capture low-frequency reference intensity
information. These two sensor modalities provide complementary information. We
propose a computationally efficient, asynchronous filter that continuously
fuses image frames and events into a single high-temporal-resolution,
high-dynamic-range image state. In absence of conventional image frames, the
filter can be run on events only. We present experimental results on
high-speed, high-dynamic-range sequences, as well as on new ground truth
datasets we generate to demonstrate the proposed algorithm outperforms existing
state-of-the-art methods.
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
Authors: Rudra Murthy V, Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2018-11-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, NMT, Sentence, Transfer Learning, Translation

Transfer learning approaches for Neural Machine Translation (NMT) train a NMT
model on the assisting-target language pair (parent model) which is later
fine-tuned for the source-target language pair of interest (child model), with
the target language being the same. In many cases, the assisting language has a
different word order from the source language. We show that divergent word
order adversely limits the benefits from transfer learning when little to no
parallel corpus between the source and target language is available. To bridge
this divergence, We propose to pre-order the assisting language sentence to
match the word order of the source language and train the parent model. Our
experiments on many language pairs show that bridging the word order gap leads
to significant improvement in the translation quality.
Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network
Authors: Hitesh Golchha, Deepak Gupta, Asif Ekbal, Pushpak Bhattacharyya
Date: 2018-11-01 00:00:00
Tasks: Sentiment Analysis, Suggestion mining

Suggestion mining is increasingly becoming an important task along with
sentiment analysis. In today's cyberspace world, people not only express their
sentiments and dispositions towards some entities or services, but they also
spend considerable time sharing their experiences and advice to fellow
customers and the product/service providers with two-fold agenda: helping
fellow customers who are likely to share a similar experience, and motivating
the producer to bring specific changes in their offerings which would be more
appreciated by the customers. In our current work, we propose a hybrid deep
learning model to identify whether a review text contains any suggestion. The
model employs semi-supervised learning to leverage the useful information from
the large amount of unlabeled data. We evaluate the performance of our proposed
model on a benchmark customer review dataset, comprising of the reviews of
Hotel and Electronics domains. Our proposed approach shows the F-scores of
65.6% and 65.5% for the Hotel and Electronics review datasets, respectively.
These performances are significantly better compared to the existing
state-of-the-art system.
Bi-GANs-ST for Perceptual Image Super-resolution
Authors: Xiaotong Luo, Rong Chen, Yuan Xie, Yanyun Qu, Cuihua Li
Date: 2018-11-01 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

Image quality measurement is a critical problem for image super-resolution
(SR) algorithms. Usually, they are evaluated by some well-known objective
metrics, e.g., PSNR and SSIM, but these indices cannot provide suitable results
in accordance with the perception of human being. Recently, a more reasonable
perception measurement has been proposed in [1], which is also adopted by the
PIRM-SR 2018 challenge. In this paper, motivated by [1], we aim to generate a
high-quality SR result which balances between the two indices, i.e., the
perception index and root-mean-square error (RMSE). To do so, we design a new
deep SR framework, dubbed Bi-GANs-ST, by integrating two complementary
generative adversarial networks (GAN) branches. One is memory residual SRGAN
(MR-SRGAN), which emphasizes on improving the objective performance, such as
reducing the RMSE. The other is weight perception SRGAN (WP-SRGAN), which
obtains the result that favors better subjective perception via a two-stage
adversarial training mechanism. Then, to produce final result with excellent
perception scores and RMSE, we use soft-thresholding method to merge the
results generated by the two GANs. Our method performs well on the perceptual
image super-resolution task of the PIRM 2018 challenge. Experimental results on
five benchmarks show that our proposal achieves highly competent performance
compared with other state-of-the-art methods.
Latent Variable Model for Multi-modal Translation
Authors: Iacer Calixto, Miguel Rios, Wilker Aziz
Date: 2018-11-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Machine Translation, model, Multimodal Machine Translation, Multi-Task Learning, Translation

In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and K\'ad\'ar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the minimum amount of information encoded in the latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).
How2: A Large-scale Dataset for Multimodal Language Understanding
Authors: Ramon Sanabria, Ozan Caglayan, Shruti Palaskar, Desmond Elliott, Loïc Barrault, Lucia Specia, Florian Metze
Date: 2018-11-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

In this paper, we introduce How2, a multimodal collection of instructional
videos with English subtitles and crowdsourced Portuguese translations. We also
present integrated sequence-to-sequence baselines for machine translation,
automatic speech recognition, spoken language translation, and multimodal
summarization. By making available data and code for several multimodal natural
language tasks, we hope to stimulate more research on these and similar
challenges, to obtain a deeper understanding of multimodality in language
processing.
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
Authors: Ying Tai, Yicong Liang, Xiaoming Liu, Lei Duan, Jilin Li, Chengjie Wang, Feiyue Huang, Yu Chen
Date: 2018-11-01 00:00:00
Tasks: Face Alignment, Pose Estimation, Quantization, regression, Vocal Bursts Intensity Prediction

In recent years, heatmap regression based models have shown their
effectiveness in face alignment and pose estimation. However, Conventional
Heatmap Regression (CHR) is not accurate nor stable when dealing with
high-resolution facial videos, since it finds the maximum activated location in
heatmaps which are generated from rounding coordinates, and thus leads to
quantization errors when scaling back to the original high-resolution space. In
this paper, we propose a Fractional Heatmap Regression (FHR) for
high-resolution video-based face alignment. The proposed FHR can accurately
estimate the fractional part according to the 2D Gaussian function by sampling
three points in heatmaps. To further stabilize the landmarks among continuous
video frames while maintaining the precise at the same time, we propose a novel
stabilization loss that contains two terms to address time delay and non-smooth
issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets
clearly demonstrate that the proposed method is more accurate and stable than
the state-of-the-art models.
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Authors: Qin Zou, Yanling Wang, Qian Wang, Yi Zhao, Qingquan Li
Date: 2018-11-01 00:00:00
Tasks: Deep Learning, Gait Recognition, Person Identification

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5\% and 93.7\% accuracies in person identification and authentication, respectively.
Applications of Deep Learning to Nuclear Fusion Research
Authors: Diogo R. Ferreira on behalf of JET Contributors
Date: 2018-11-01 00:00:00
Tasks: Deep Learning, Diagnostic

Nuclear fusion is the process that powers the sun, and it is one of the best
hopes to achieve a virtually unlimited energy source for the future of
humanity. However, reproducing sustainable nuclear fusion reactions here on
Earth is a tremendous scientific and technical challenge. Special devices --
called tokamaks -- have been built around the world, with JET (Joint European
Torus, in the UK) being the largest tokamak currently in operation. Such
devices confine matter and heat it up to extremely high temperatures, creating
a plasma where fusion reactions begin to occur. JET has over one hundred
diagnostic systems to monitor what happens inside the plasma, and each
30-second experiment (or pulse) generates about 50 GB of data. In this work, we
show how convolutional neural networks (CNNs) can be used to reconstruct the 2D
plasma profile inside the device based on data coming from those diagnostics.
We also discuss how recurrent neural networks (RNNs) can be used to predict
plasma disruptions, which are one of the major problems affecting tokamaks
today. Training of such networks is done on NVIDIA GPUs.
Asymmetric Bilateral Phase Correlation for Optical Flow Estimation in the Frequency Domain
Authors: Vasileios Argyriou
Date: 2018-11-01 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

We address the problem of motion estimation in images operating in the
frequency domain. A method is presented which extends phase correlation to
handle multiple motions present in an area. Our scheme is based on a novel
Bilateral-Phase Correlation (BLPC) technique that incorporates the concept and
principles of Bilateral Filters retaining the motion boundaries by taking into
account the difference both in value and distance in a manner very similar to
Gaussian convolution. The optical flow is obtained by applying the proposed
method at certain locations selected based on the present motion differences
and then performing non-uniform interpolation in a multi-scale iterative
framework. Experiments with several well-known datasets with and without
ground-truth show that our scheme outperforms recently proposed
state-of-the-art phase correlation based optical flow methods.
Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method
Authors: Emna Krichene, Wael Ouarda, Habib Chabchoub, Adel M. ALIMI
Date: 2018-11-01 00:00:00

A newly introduced method called Taylor-based Optimized Recursive Extended
Exponential Smoothed Neural Networks Forecasting method is applied and extended
in this study to forecast numerical values. Unlike traditional forecasting
techniques which forecast only future values, our proposed method provides a
new extension to correct the predicted values which is done by forecasting the
estimated error. Experimental results demonstrated that the proposed method has
a high accuracy both in training and testing data and outperform the
state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map
datasets.
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
Authors: Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
Date: 2018-11-01 00:00:00

In this paper, we introduce the notion of liquid time-constant (LTC)
recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with
varying neuronal time-constant realized by their nonlinear synaptic
transmission model. This feature is inspired by the communication principles in
the nervous system of small species. It enables the model to approximate
continuous mapping with a small number of computational units. We show that any
finite trajectory of an $n$-dimensional continuous dynamical system can be
approximated by the internal state of the hidden units and $n$ output units of
an LTC network. Here, we also theoretically find bounds on their neuronal
states and varying time-constant.
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking
Authors: Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
Date: 2018-11-01 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Defining a multi-target motion model, which is an important step of tracking algorithms, can be very challenging. Using fixed models (as in several generative Bayesian algorithms, such as Kalman filters) can fail to accurately predict sophisticated target motions. On the other hand, sequential learning of the motion model (for example, using recurrent neural networks) can be computationally complex and difficult due to the variable unknown number of targets. In this paper, we propose a multi-target filtering and tracking algorithm which learns the motion model, simultaneously for all targets, from an implicitly represented state map and performs spatio-temporal data prediction. To this end, the multi-target state is modelled over a continuous hypothetical target space, using random finite sets and Gaussian mixture probability hypothesis density formulations. The prediction step is recursively performed using a deep convolutional recurrent neural network with a long short-term memory architecture, which is trained as a regression block, on the fly, over "probability density difference" maps. Our approach is evaluated over widely used pedestrian tracking benchmarks, remarkably outperforming state-of-the-art multi-target filtering algorithms, while giving competitive results when compared with other tracking approaches: The proposed approach generates an average 40.40 and 62.29 optimal sub-pattern assignment (OSPA) errors on MOT15 and MOT16/17 datasets, respectively, while producing 62.0%, 70.0% and 66.9% multi-object tracking accuracy (MOTA) on MOT16/17, PNNL Parking Lot and PETS09 pedestrian tracking datasets, respectively, when publicly available detectors are used.