pwc-archive/papers-with-abstracts huggingface dataset:
Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification
Authors: Aral Sarrafi, Zhu Mao, Christopher Niezrecki, Peyman Poozesh
Date: 2018-03-30 00:00:00
Tasks: Motion Estimation, Motion Magnification, Structural Health Monitoring

Vibration-based Structural Health Monitoring (SHM) techniques are among the
most common approaches for structural damage identification. The presence of
damage in structures may be identified by monitoring the changes in dynamic
behavior subject to external loading, and is typically performed by using
experimental modal analysis (EMA) or operational modal analysis (OMA). These
tools for SHM normally require a limited number of physically attached
transducers (e.g. accelerometers) in order to record the response of the
structure for further analysis. Signal conditioners, wires, wireless receivers
and a data acquisition system (DAQ) are also typical components of traditional
sensing systems used in vibration-based SHM. However, instrumentation of
lightweight structures with contact sensors such as accelerometers may induce
mass-loading effects, and for large-scale structures, the instrumentation is
labor intensive and time consuming. Achieving high spatial measurement
resolution for a large-scale structure is not always feasible while working
with traditional contact sensors, and there is also the potential for a lack of
reliability associated with fixed contact sensors in outliving the life-span of
the host structure. Among the state-of-the-art non-contact measurements,
digital video cameras are able to rapidly collect high-density spatial
information from structures remotely. In this paper, the subtle motions from
recorded video (i.e. a sequence of images) are extracted by means of
Phase-based Motion Estimation (PME) and the extracted information is used to
conduct damage identification on a 2.3-meter long Skystream wind turbine blade
(WTB). The PME and phased-based motion magnification approach estimates the
structural motion from the captured sequence of images for both a baseline and
damaged test cases on a wind turbine blade.
A Novel Framework for Online Supervised Learning with Feature Selection
Authors: Lizhe Sun, Mingyuan Wang, Adrian Barbu
Date: 2018-03-30 00:00:00
Tasks: feature selection

Current online learning methods suffer issues such as lower convergence rates and limited capability to recover the support of the true features compared to their offline counterparts. In this paper, we present a novel framework for online learning based on running averages and introduce a series of online versions of popular offline methods such as Elastic Net, Minimax Concave Penalty, and Feature Selection with Annealing. The framework can handle an arbitrarily large number of observations with the restriction that the data dimension is not too large, e.g. p<50,000. We prove the equivalence between our online methods and their offline counterparts and give theoretical true feature recovery and convergence guarantees for some of them. In contrast to existing online methods, the proposed methods can extract models with any desired sparsity level at any time. Numerical experiments indicate that our new methods enjoy high true feature recovery accuracy and a fast convergence rate, compared with standard online and offline algorithms. We also show how the running averages framework can be used for model adaptation in the presence of model drift. Finally, we present applications to large datasets where again the proposed framework shows competitive results compared to popular online and offline algorithms.
A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
Authors: Umang Gupta, Ankush Chatterjee, Radhakrishnan Srikanth, Puneet Agrawal
Date: 2017-07-21 00:00:00

Emotions are physiological states generated in humans in reaction to internal
or external events. They are complex and studied across numerous fields
including computer science. As humans, on reading "Why don't you ever text me!"
we can either interpret it as a sad or angry emotion and the same ambiguity
exists for machines. Lack of facial expressions and voice modulations make
detecting emotions from text a challenging problem. However, as humans
increasingly communicate using text messaging applications, and digital agents
gain popularity in our society, it is essential that these digital agents are
emotion aware, and respond accordingly.
  In this paper, we propose a novel approach to detect emotions like happy, sad
or angry in textual conversations using an LSTM based Deep Learning model. Our
approach consists of semi-automated techniques to gather training data for our
model. We exploit advantages of semantic and sentiment based embeddings and
propose a solution combining both. Our work is evaluated on real-world
conversations and significantly outperforms traditional Machine Learning
baselines as well as other off-the-shelf Deep Learning models.
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
Authors: Egor Lakomkin, Chandrakant Bothe, Stefan Wermter
Date: 2018-03-30 00:00:00

The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity
values of tweet messages. Given the text of a tweet and its emotion category
(anger, joy, fear, and sadness), the participants were asked to build a system
that assigns emotion intensity values. Emotion intensity estimation is a
challenging problem given the short length of the tweets, the noisy structure
of the text and the lack of annotated data. To solve this problem, we developed
an ensemble of two neural models, processing input on the character. and
word-level with a lexicon-driven system. The correlation scores across all four
emotions are averaged to determine the bottom-line competition metric, and our
system ranks place forth in full intensity range and third in 0.5-1 range of
intensity among 23 systems at the time of writing (June 2017).
Reusing Neural Speech Representations for Auditory Emotion Recognition
Authors: Egor Lakomkin, Cornelius Weber, Sven Magg, Stefan Wermter
Date: 2018-03-30 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Emotion Recognition, General Classification, Transfer Learning

Acoustic emotion recognition aims to categorize the affective state of the
speaker and is still a difficult task for machine learning models. The
difficulties come from the scarcity of training data, general subjectivity in
emotion perception resulting in low annotator agreement, and the uncertainty
about which features are the most relevant and robust ones for classification.
In this paper, we will tackle the latter problem. Inspired by the recent
success of transfer learning methods we propose a set of architectures which
utilize neural representations inferred by training on large speech databases
for the acoustic emotion recognition task. Our experiments on the IEMOCAP
dataset show ~10% relative improvements in the accuracy and F1-score over the
baseline recurrent neural network which is trained end-to-end for emotion
recognition.
Automatically augmenting an emotion dataset improves classification using audio
Authors: Egor Lakomkin, Cornelius Weber, Stefan Wermter
Date: 2018-03-30 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, Emotion Classification, General Classification, Sentiment Analysis

In this work, we tackle a problem of speech emotion classification. One of
the issues in the area of affective computation is that the amount of annotated
data is very limited. On the other hand, the number of ways that the same
emotion can be expressed verbally is enormous due to variability between
speakers. This is one of the factors that limits performance and
generalization. We propose a simple method that extracts audio samples from
movies using textual sentiment analysis. As a result, it is possible to
automatically construct a larger dataset of audio samples with positive,
negative emotional and neutral speech. We show that pretraining recurrent
neural network on such a dataset yields better results on the challenging
EmotiW corpus. This experiment shows a potential benefit of combining textual
sentiment analysis with vocal information.
Predicting Future Instance Segmentation by Forecasting Convolutional Features
Authors: Pauline Luc, Camille Couprie, Yann Lecun, Jakob Verbeek
Date: 2018-03-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Forecasting, Video Prediction

Anticipating future events is an important prerequisite towards intelligent
behavior. Video forecasting has been studied as a proxy task towards this goal.
Recent work has shown that to predict semantic segmentation of future frames,
forecasting at the semantic level is more effective than forecasting RGB frames
and then segmenting these. In this paper we consider the more challenging
problem of future instance segmentation, which additionally segments out
individual objects. To deal with a varying number of output labels per image,
we develop a predictive model in the space of fixed-sized convolutional
features of the Mask R-CNN instance segmentation model. We apply the "detection
head'" of Mask R-CNN on the predicted features to produce the instance
segmentation of future frames. Experiments show that this approach
significantly improves over strong baselines based on optical flow and
repurposed instance segmentation architectures.
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
Authors: Alexander Grabner, Peter M. Roth, Vincent Lepetit
Date: 2018-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Pose Estimation, Metric Learning, Pose Estimation, Retrieval

We propose a scalable, efficient and accurate approach to retrieve 3D models
for objects in the wild. Our contribution is twofold. We first present a 3D
pose estimation approach for object categories which significantly outperforms
the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior
to retrieve 3D models which accurately represent the geometry of objects in RGB
images. For this purpose, we render depth images from 3D models under our
predicted pose and match learned image descriptors of RGB images against those
of rendered depth images using a CNN-based multi-view metric learning approach.
In this way, we are the first to report quantitative results for 3D model
retrieval on Pascal3D+, where our method chooses the same models as human
annotators for 50% of the validation images on average. In addition, we show
that our method, which was trained purely on Pascal3D+, retrieves rich and
accurate 3D models from ShapeNet given RGB images of objects in the wild.
Minimax Estimation of Quadratic Fourier Functionals
Authors: Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos
Date: 2018-03-30 00:00:00
Tasks: Translation

We study estimation of (semi-)inner products between two nonparametric
probability distributions, given IID samples from each distribution. These
products include relatively well-studied classical $\mathcal{L}^2$ and Sobolev
inner products, as well as those induced by translation-invariant reproducing
kernels, for which we believe our results are the first. We first propose
estimators for these quantities, and the induced (semi)norms and
(pseudo)metrics. We then prove non-asymptotic upper bounds on their mean
squared error, in terms of weights both of the inner product and of the two
distributions, in the Fourier basis. Finally, we prove minimax lower bounds
that imply rate-optimality of the proposed estimators over Fourier ellipsoids.
Reconstruction Network for Video Captioning
Authors: Bairui Wang, Lin Ma, Wei zhang, Wei Liu
Date: 2018-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Sentence, Video Captioning

In this paper, the problem of describing visual contents of a video sequence
with natural language is addressed. Unlike previous video captioning work
mainly exploiting the cues of video contents to make a language description, we
propose a reconstruction network (RecNet) with a novel
encoder-decoder-reconstructor architecture, which leverages both the forward
(video to sentence) and backward (sentence to video) flows for video
captioning. Specifically, the encoder-decoder makes use of the forward flow to
produce the sentence description based on the encoded video semantic features.
Two types of reconstructors are customized to employ the backward flow and
reproduce the video features based on the hidden state sequence generated by
the decoder. The generation loss yielded by the encoder-decoder and the
reconstruction loss introduced by the reconstructor are jointly drawn into
training the proposed RecNet in an end-to-end fashion. Experimental results on
benchmark datasets demonstrate that the proposed reconstructor can boost the
encoder-decoder models and leads to significant gains in video caption
accuracy.
A Rule for Committee Selection with Soft Diversity Constraints
Authors: Haris Aziz
Date: 2018-03-30 00:00:00
Tasks: Diversity

Committee selection with diversity or distributional constraints is a
ubiquitous problem. However, many of the formal approaches proposed so far have
certain drawbacks including (1) computationally intractability in general, and
(2) inability to suggest a solution for certain instances where the hard
constraints cannot be met. We propose a practical and polynomial-time algorithm
for diverse committee selection that draws on the idea of using soft bounds and
satisfies natural axioms.
Observer-based Adaptive Optimal Output Containment Control problem of Linear Heterogeneous Multi-agent Systems with Relative Output Measurements
Authors: Majid Mazouchi, Mohammad Bagher Naghibi-Sistani, Seyed Kamal Hosseini Sani, Farzaneh Tatari, Hamidreza Modares
Date: 2018-03-30 00:00:00
Tasks: Reinforcement Learning

This paper develops an optimal relative output-feedback based solution to the
containment control problem of linear heterogeneous multi-agent systems. A
distributed optimal control protocol is presented for the followers to not only
assure that their outputs fall into the convex hull of the leaders' output
(i.e., the desired or safe region), but also optimizes their transient
performance. The proposed optimal control solution is composed of a feedback
part, depending of the followers' state, and a feed-forward part, depending on
the convex hull of the leaders' state. To comply with most real-world
applications, the feedback and feed-forward states are assumed to be
unavailable and are estimated using two distributed observers. That is, since
the followers cannot directly sense their absolute states, a distributed
observer is designed that uses only relative output measurements with respect
to their neighbors (measured for example by using range sensors in robotic) and
the information which is broadcasted by their neighbors to estimate their
states. Moreover, another adaptive distributed observer is designed that uses
exchange of information between followers over a communication network to
estimate the convex hull of the leaders' state. The proposed observer relaxes
the restrictive requirement of knowing the complete knowledge of the leaders'
dynamics by all followers. An off-policy reinforcement learning algorithm on an
actor-critic structure is next developed to solve the optimal containment
control problem online, using relative output measurements and without
requirement of knowing the leaders' dynamics by all followers. Finally, the
theoretical results are verified by numerical simulations.
The Resistance to Label Noise in K-NN and DNN Depends on its Concentration
Authors: Amnon Drory, Oria Ratzon, Shai Avidan, Raja Giryes
Date: 2018-03-30 00:00:00
Tasks: General Classification, Multi-class Classification

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a realizable analytic expression that approximates the multi-class K-NN classification error in the presence of label noise, which is of independent importance. We then suggest that the expression for K-NN may serve as a first-order approximation for the DNN error. Finally, we demonstrate empirically the proximity of the developed expression to the observed performance of K-NN and DNN classifiers. Our result may explain the already observed surprising resistance of DNN to some types of label noise. It also characterizes an important factor of it showing that the more concentrated the noise the greater is the degradation in performance.
Guided Machine Learning for power grid segmentation
Authors: Antoine Marot, Sami Tazi, Benjamin Donnot, Patrick Panciatici
Date: 2017-11-13 00:00:00
Tasks: BIG-bench Machine Learning, Community Detection, Segmentation

The segmentation of large scale power grids into zones is crucial for control
room operators when managing the grid complexity near real time. In this paper
we propose a new method in two steps which is able to automatically do this
segmentation, while taking into account the real time context, in order to help
them handle shifting dynamics. Our method relies on a "guided" machine learning
approach. As a first step, we define and compute a task specific "Influence
Graph" in a guided manner. We indeed simulate on a grid state chosen
interventions, representative of our task of interest (managing active power
flows in our case). For visualization and interpretation, we then build a
higher representation of the grid relevant to this task by applying the graph
community detection algorithm \textit{Infomap} on this Influence Graph. To
illustrate our method and demonstrate its practical interest, we apply it on
commonly used systems, the IEEE-14 and IEEE-118. We show promising and original
interpretable results, especially on the previously well studied RTS-96 system
for grid segmentation. We eventually share initial investigation and results on
a large-scale system, the French power grid, whose segmentation had a
surprising resemblance with RTE's historical partitioning.
Neural Network Architecture for Credibility Assessment of Textual Claims
Authors: Nurendra Choudhary, Rajat Singh, Ishita Bindlish, Manish Shrivastava
Date: 2018-03-28 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity

Text articles with false claims, especially news, have recently become aggravating for the Internet users. These articles are in wide circulation and readers face difficulty discerning fact from fiction. Previous work on credibility assessment has focused on factual analysis and linguistic features. The task's main challenge is the distinction between the features of true and false articles. In this paper, we propose a novel approach called Credibility Outcome (CREDO) which aims at scoring the credibility of an article in an open domain setting. CREDO consists of different modules for capturing various features responsible for the credibility of an article. These features includes credibility of the article's source and author, semantic similarity between the article and related credible articles retrieved from a knowledge base, and sentiments conveyed by the article. A neural network architecture learns the contribution of each of these modules to the overall credibility of an article. Experiments on Snopes dataset reveals that CREDO outperforms the state-of-the-art approaches based on linguistic features.
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
Authors: Filippos Kokkinos, Stamatios Lefkimmiatis
Date: 2018-03-14 00:00:00
Proceeding: ECCV 2018 9
Tasks: Demosaicking, Denoising

Demosaicking and denoising are among the most crucial steps of modern digital
camera pipelines and their joint treatment is a highly ill-posed inverse
problem where at-least two-thirds of the information are missing and the rest
are corrupted by noise. This poses a great challenge in obtaining meaningful
reconstructions and a special care for the efficient treatment of the problem
is required. While there are several machine learning approaches that have been
recently introduced to deal with joint image demosaicking-denoising, in this
work we propose a novel deep learning architecture which is inspired by
powerful classical image regularization methods and large-scale convex
optimization techniques. Consequently, our derived network is more transparent
and has a clear interpretation compared to alternative competitive deep
learning approaches. Our extensive experiments demonstrate that our network
outperforms any previous approaches on both noisy and noise-free data. This
improvement in reconstruction quality is attributed to the principled way we
design our network architecture, which also requires fewer trainable parameters
than the current state-of-the-art deep network solution. Finally, we show that
our network has the ability to generalize well even when it is trained on small
datasets, while keeping the overall number of trainable parameters low.
Learning Structure and Strength of CNN Filters for Small Sample Size Training
Authors: Rohit Keshari, Mayank Vatsa, Richa Singh, Afzel Noore
Date: 2018-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Recognition

Convolutional Neural Networks have provided state-of-the-art results in
several computer vision problems. However, due to a large number of parameters
in CNNs, they require a large number of training samples which is a limiting
factor for small sample size problems. To address this limitation, we propose
SSF-CNN which focuses on learning the structure and strength of filters. The
structure of the filter is initialized using a dictionary-based filter learning
algorithm and the strength of the filter is learned using the small sample
training data. The architecture provides the flexibility of training with both
small and large training databases and yields good accuracies even with small
size training data. The effectiveness of the algorithm is first demonstrated on
MNIST, CIFAR10, and NORB databases, with a varying number of training samples.
The results show that SSF-CNN significantly reduces the number of parameters
required for training while providing high accuracies the test databases. On
small sample size problems such as newborn face recognition and Omniglot, it
yields state-of-the-art results. Specifically, on the IIITD Newborn Face
Database, the results demonstrate improvement in rank-1 identification accuracy
by at least 10%.