pwc-archive/papers-with-abstracts huggingface dataset:
Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks
Authors: Chie Hieida, Takato Horii, Takayuki Nagai
Date: 2018-08-25 00:00:00
Tasks: Decision Making

Emotions are very important for human intelligence. For example, emotions are
closely related to the appraisal of the internal bodily state and external
stimuli. This helps us to respond quickly to the environment. Another important
perspective in human intelligence is the role of emotions in decision-making.
Moreover, the social aspect of emotions is also very important. Therefore, if
the mechanism of emotions were elucidated, we could advance toward the
essential understanding of our natural intelligence. In this study, a model of
emotions is proposed to elucidate the mechanism of emotions through the
computational model. Furthermore, from the viewpoint of partner robots, the
model of emotions may help us to build robots that can have empathy for humans.
To understand and sympathize with people's feelings, the robots need to have
their own emotions. This may allow robots to be accepted in human society. The
proposed model is implemented using deep neural networks consisting of three
modules, which interact with each other. Simulation results reveal that the
proposed model exhibits reasonable behavior as the basic mechanism of emotion.
Inductive Learning of Answer Set Programs from Noisy Examples
Authors: Mark Law, Alessandra Russo, Krysia Broda
Date: 2018-08-25 00:00:00
Tasks: Common Sense Reasoning, Inductive Learning, Inductive logic programming

In recent years, non-monotonic Inductive Logic Programming has received
growing interest. Specifically, several new learning frameworks and algorithms
have been introduced for learning under the answer set semantics, allowing the
learning of common-sense knowledge involving defaults and exceptions, which are
essential aspects of human reasoning. In this paper, we present a
noise-tolerant generalisation of the learning from answer sets framework. We
evaluate our ILASP3 system, both on synthetic and on real datasets, represented
in the new framework. In particular, we show that on many of the datasets
ILASP3 achieves a higher accuracy than other ILP systems that have previously
been applied to the datasets, including a recently proposed differentiable
learning framework.
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
Authors: Zhong Zhou, Matthias Sperber, Alex Waibel
Date: 2018-08-25 00:00:00
Proceeding: ACL 2019 7
Tasks: Machine Translation, NMT, Sentence, TAG, Translation, Word Translation

Paraphrases, the rewordings of the same semantic meaning, are useful for improving generalization and translation. However, prior works only explore paraphrases at the word or phrase level, not at the sentence or corpus level. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We train on parallel paraphrases in multiple languages from various sources. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones. Combining both the source and the target paraphrases lifts performance further; combining paraphrases with multilingual data helps but has mixed performance. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.
Meta-Learning for Low-Resource Neural Machine Translation
Authors: Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, Victor O. K. Li
Date: 2018-08-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Meta-Learning, NMT, Transfer Learning, Translation

In this paper, we propose to extend the recently introduced model-agnostic
meta-learning algorithm (MAML) for low-resource neural machine translation
(NMT). We frame low-resource translation as a meta-learning problem, and we
learn to adapt to low-resource languages based on multilingual high-resource
language tasks. We use the universal lexical
representation~\citep{gu2018universal} to overcome the input-output mismatch
across different languages. We evaluate the proposed meta-learning strategy
using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt,
Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro,
Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach
significantly outperforms the multilingual, transfer learning based
approach~\citep{zoph2016transfer} and enables us to train a competitive NMT
system with only a fraction of training examples. For instance, the proposed
approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing
only 16,000 translated words (~600 parallel sentences).
A Tutorial on Modular Ontology Modeling with Ontology Design Patterns: The Cooking Recipes Ontology
Authors: Pascal Hitzler, Adila Krisnadhi
Date: 2018-08-25 00:00:00

We provide a detailed example for modular ontology modeling based on ontology
design patterns.
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media
Authors: Christian Abbet, Meryem M'hamdi, Athanasios Giannakopoulos, Robert West, Andreea Hossmann, Michael Baeriswyl, Claudiu Musat
Date: 2018-08-25 00:00:00
Proceeding: CONLL 2018 10
Tasks: Chatbot, Intent Detection, Word Embeddings

We propose a new method to detect when users express the intent to leave a
service, also known as churn. While previous work focuses solely on social
media, we show that this intent can be detected in chatbot conversations. As
companies increasingly rely on chatbots they need an overview of potentially
churny users. To this end, we crowdsource and publish a dataset of churn intent
expressions in chatbot interactions in German and English. We show that
classifiers trained on social media data can detect the same intent in the
context of chatbots.
  We introduce a classification architecture that outperforms existing work on
churn intent detection in social media. Moreover, we show that, using bilingual
word embeddings, a system trained on combined English and German data
outperforms monolingual approaches. As the only existing dataset is in English,
we crowdsource and publish a novel dataset of German tweets. We thus underline
the universal aspect of the problem, as examples of churn intent in English
help us identify churn in German tweets and chatbot conversations.
Analysis of adversarial attacks against CNN-based image forgery detectors
Authors: Diego Gragnaniello, Francesco Marra, Giovanni Poggi, Luisa Verdoliva
Date: 2018-08-25 00:00:00
Tasks: Image Forensics

With the ubiquitous diffusion of social networks, images are becoming a
dominant and powerful communication channel. Not surprisingly, they are also
increasingly subject to manipulations aimed at distorting information and
spreading fake news. In recent years, the scientific community has devoted
major efforts to contrast this menace, and many image forgery detectors have
been proposed. Currently, due to the success of deep learning in many
multimedia processing tasks, there is high interest towards CNN-based
detectors, and early results are already very promising. Recent studies in
computer vision, however, have shown CNNs to be highly vulnerable to
adversarial attacks, small perturbations of the input data which drive the
network towards erroneous classification. In this paper we analyze the
vulnerability of CNN-based image forensics methods to adversarial attacks,
considering several detectors and several types of attack, and testing
performance on a wide range of common manipulations, both easily and hardly
detectable.
Multiplayer bandits without observing collision information
Authors: Gabor Lugosi, Abbas Mehrabian
Date: 2018-08-25 00:00:00

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred and a more difficult setup when no collision information is available. We give the first theoretical guarantees for the second model: an algorithm with a logarithmic regret, and an algorithm with a square-root regret type that does not depend on the gaps between the means. For the first model, we give the first square-root regret bounds that do not depend on the gaps. Building on these ideas, we also give an algorithm for reaching approximate Nash equilibria quickly in stochastic anti-coordination games.
Unsupervised Hypergraph Feature Selection via a Novel Point-Weighting Framework and Low-Rank Representation
Authors: Ammar Gilani, Maryam Amirmazlaghani
Date: 2018-08-25 00:00:00
Tasks: feature selection

Feature selection methods are widely used in order to solve the 'curse of
dimensionality' problem. Many proposed feature selection frameworks, treat all
data points equally; neglecting their different representation power and
importance. In this paper, we propose an unsupervised hypergraph feature
selection method via a novel point-weighting framework and low-rank
representation that captures the importance of different data points. We
introduce a novel soft hypergraph with low complexity to model data. Then, we
formulate the feature selection as an optimization problem to preserve local
relationships and also global structure of data. Our approach for global
structure preservation helps the framework overcome the problem of
unavailability of data labels in unsupervised learning. The proposed feature
selection method treats with different data points based on their importance in
defining data structure and representation power. Moreover, since the
robustness of feature selection methods against noise and outlier is of great
importance, we adopt low-rank representation in our model. Also, we provide an
efficient algorithm to solve the proposed optimization problem. The
computational cost of the proposed algorithm is lower than many
state-of-the-art methods which is of high importance in feature selection
tasks. We conducted comprehensive experiments with various evaluation methods
on different benchmark data sets. These experiments indicate significant
improvement, compared with state-of-the-art feature selection methods.
How many labeled license plates are needed?
Authors: Changhao Wu, Shugong Xu, Guocong Song, Shunqing Zhang
Date: 2018-08-25 00:00:00
Tasks: Data Augmentation, License Plate Recognition

Training a good deep learning model often requires a lot of annotated data.
As a large amount of labeled data is typically difficult to collect and even
more difficult to annotate, data augmentation and data generation are widely
used in the process of training deep neural networks. However, there is no
clear common understanding on how much labeled data is needed to get
satisfactory performance. In this paper, we try to address such a question
using vehicle license plate character recognition as an example application. We
apply computer graphic scripts and Generative Adversarial Networks to generate
and augment a large number of annotated, synthesized license plate images with
realistic colors, fonts, and character composition from a small number of real,
manually labeled license plate images. Generated and augmented data are mixed
and used as training data for the license plate recognition network modified
from DenseNet. The experimental results show that the model trained from the
generated mixed training data has good generalization ability, and the proposed
approach achieves a new state-of-the-art accuracy on Dataset-1 and AOLP, even
with a very limited number of original real license plates. In addition, the
accuracy improvement caused by data generation becomes more significant when
the number of labeled images is reduced. Data augmentation also plays a more
significant role when the number of labeled images is increased.
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set
Authors: Radu Tudor Ionescu, Andrei M. Butnaru
Date: 2018-08-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialect Identification, General Classification, Language Identification, Native Language Identification, Sentiment Analysis, text-classification, Text Classification, Transductive Learning

Recently, string kernels have obtained state-of-the-art results in various
text classification tasks such as Arabic dialect identification or native
language identification. In this paper, we apply two simple yet effective
transductive learning approaches to further improve the results of string
kernels. The first approach is based on interpreting the pairwise string kernel
similarities between samples in the training set and samples in the test set as
features. Our second approach is a simple self-training method based on two
learning iterations. In the first iteration, a classifier is trained on the
training set and tested on the test set, as usual. In the second iteration, a
number of test samples (to which the classifier associated higher confidence
scores) are added to the training set for another round of training. However,
the ground-truth labels of the added test samples are not necessary. Instead,
we use the labels predicted by the classifier in the first training iteration.
By adapting string kernels to the test set, we report significantly better
accuracy rates in English polarity classification and Arabic dialect
identification.
How do Convolutional Neural Networks Learn Design?
Authors: Shailza Jolly, Brian Kenji Iwana, Ryohei Kuroki, Seiichi Uchida
Date: 2018-08-25 00:00:00
Tasks: image-classification, Image Classification, Text Detection

In this paper, we aim to understand the design principles in book cover
images which are carefully crafted by experts. Book covers are designed in a
unique way, specific to genres which convey important information to their
readers. By using Convolutional Neural Networks (CNN) to predict book genres
from cover images, visual cues which distinguish genres can be highlighted and
analyzed. In order to understand these visual clues contributing towards the
decision of a genre, we present the application of Layer-wise Relevance
Propagation (LRP) on the book cover image classification results. We use LRP to
explain the pixel-wise contributions of book cover design and highlight the
design elements contributing towards particular genres. In addition, with the
use of state-of-the-art object and text detection methods, insights about
genre-specific book cover designs are discovered.
Noiseprint: a CNN-based camera model fingerprint
Authors: Davide Cozzolino, Luisa Verdoliva
Date: 2018-08-25 00:00:00
Tasks: model

Forensic analyses of digital images rely heavily on the traces of in-camera
and out-camera processes left on the acquired images. Such traces represent a
sort of camera fingerprint. If one is able to recover them, by suppressing the
high-level scene content and other disturbances, a number of forensic tasks can
be easily accomplished. A notable example is the PRNU pattern, which can be
regarded as a device fingerprint, and has received great attention in
multimedia forensics. In this paper we propose a method to extract a camera
model fingerprint, called noiseprint, where the scene content is largely
suppressed and model-related artifacts are enhanced. This is obtained by means
of a Siamese network, which is trained with pairs of image patches coming from
the same (label +1) or different (label -1) cameras. Although noiseprints can
be used for a large variety of forensic tasks, here we focus on image forgery
localization. Experiments on several datasets widespread in the forensic
community show noiseprint-based methods to provide state-of-the-art
performance.
A Novel Deep Neural Network Architecture for Mars Visual Navigation
Authors: Jiang Zhang, Yuanqing Xia, Ganghui Shen
Date: 2018-08-25 00:00:00
Tasks: Autonomous Navigation, Visual Navigation

In this paper, emerging deep learning techniques are leveraged to deal with
Mars visual navigation problem. Specifically, to achieve precise landing and
autonomous navigation, a novel deep neural network architecture with double
branches and non-recurrent structure is designed, which can represent both
global and local deep features of Martian environment images effectively. By
employing this architecture, Mars rover can determine the optimal navigation
policy to the target point directly from original Martian environment images.
Moreover, compared with the existing state-of-the-art algorithm, the training
time is reduced by 45.8%. Finally, experiment results demonstrate that the
proposed deep neural network architecture achieves better performance and
faster convergence than the existing ones and generalizes well to unknown
environment.
Saliency Detection via Bidirectional Absorbing Markov Chain
Authors: Fengling Jiang, Bin Kong, Ahsan Adeel, Yun Xiao, Amir Hussain
Date: 2018-08-25 00:00:00
Tasks: Saliency Detection, Superpixels

Traditional saliency detection via Markov chain only considers boundaries
nodes. However, in addition to boundaries cues, background prior and foreground
prior cues play a complementary role to enhance saliency detection. In this
paper, we propose an absorbing Markov chain based saliency detection method
considering both boundary information and foreground prior cues. The proposed
approach combines both boundaries and foreground prior cues through
bidirectional Markov chain. Specifically, the image is first segmented into
superpixels and four boundaries nodes (duplicated as virtual nodes) are
selected. Subsequently, the absorption time upon transition node's random walk
to the absorbing state is calculated to obtain foreground possibility.
Simultaneously, foreground prior as the virtual absorbing nodes is used to
calculate the absorption time and obtain the background possibility. Finally,
two obtained results are fused to obtain the combined saliency map using cost
function for further optimization at multi-scale. Experimental results
demonstrate the outperformance of our proposed model on 4 benchmark datasets as
compared to 17 state-of-the-art methods.
MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction
Authors: Ossama Obeid, Salam Khalifa, Nizar Habash, Houda Bouamor, Wajdi Zaghouani, Kemal Oflazer
Date: 2018-08-25 00:00:00
Proceeding: LREC 2018 5
Tasks: Dialect Identification, LEMMA, Management, Spelling Correction

In this paper, we introduce MADARi, a joint morphological annotation and
spelling correction system for texts in Standard and Dialectal Arabic. The
MADARi framework provides intuitive interfaces for annotating text and managing
the annotation process of a large number of sizable documents. Morphological
annotation includes indicating, for a word, in context, its baseword, clitics,
part-of-speech, lemma, gloss, and dialect identification. MADARi has a suite of
utilities to help with annotator productivity. For example, annotators are
provided with pre-computed analyses to assist them in their task and reduce the
amount of work needed to complete it. MADARi also allows annotators to query a
morphological analyzer for a list of possible analyses in multiple dialects or
look up previously submitted analyses. The MADARi management interface enables
a lead annotator to easily manage and organize the whole annotation process
remotely and concurrently. We describe the motivation, design and
implementation of this interface; and we present details from a user study
working with this system.
Fusion++: Volumetric Object-Level SLAM
Authors: John McCormac, Ronald Clark, Michael Bloesch, Andrew J. Davison, Stefan Leutenegger
Date: 2018-08-25 00:00:00
Tasks: Loop Closure Detection, Object

We propose an online object-level SLAM system which builds a persistent and
accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera
browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to
initialise compact per-object Truncated Signed Distance Function (TSDF)
reconstructions with object size-dependent resolutions and a novel 3D
foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose
graph which is our only persistent map representation. Objects are
incrementally refined via depth fusion, and are used for tracking,
relocalisation and loop closure detection. Loop closures cause adjustments in
the relative pose estimates of object instances, but no intra-object warping.
Each object also carries semantic information which is refined over time and an
existence probability to account for spurious instance predictions. We
demonstrate our approach on a hand-held RGB-D sequence from a cluttered office
scene with a large number and variety of object instances, highlighting how the
system closes loops and makes good use of existing objects on repeated loops.
We quantitatively evaluate the trajectory error of our system against a
baseline approach on the RGB-D SLAM benchmark, and qualitatively compare
reconstruction quality of discovered objects on the YCB video dataset.
Performance evaluation shows our approach is highly memory efficient and runs
online at 4-8Hz (excluding relocalisation) despite not being optimised at the
software level.
NavigationNet: A Large-scale Interactive Indoor Navigation Dataset
Authors: He Huang, Yujing Shen, Jiankai Sun, Cewu Lu
Date: 2018-08-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scene Understanding

Indoor navigation aims at performing navigation within buildings. In scenes
like home and factory, most intelligent mobile devices require an functionality
of routing to guide itself precisely through indoor scenes to complete various
tasks in order to serve human. In most scenarios, we expected an intelligent
device capable of navigating itself in unseen environment. Although several
solutions have been proposed to deal with this issue, they usually require
pre-installed beacons or a map pre-built with SLAM, which means that they are
not capable of working in novel environments. To address this, we proposed
NavigationNet, a computer vision dataset and benchmark to allow the utilization
of deep reinforcement learning on scene-understanding-based indoor navigation.
We also proposed and formalized several typical indoor routing problems that
are suitable for deep reinforcement learning.
Parameter-wise co-clustering for high-dimensional data
Authors: M. P. B. Gallaugher, C. Biernacki, P. D. McNicholas
Date: 2018-08-25 00:00:00
Tasks: Clustering, Dimensionality Reduction, Model Selection, parameter estimation, Vocal Bursts Intensity Prediction

In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for data modelled via continuous random variables, is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony achieved by traditional co-clustering. A stochastic expectation-maximization (SEM) algorithm along with a Gibbs sampler is used for parameter estimation and an integrated complete log-likelihood criterion is used for model selection. Simulated and real datasets are used for illustration and comparison with traditional co-clustering.