pwc-archive/papers-with-abstracts huggingface dataset:
Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results
Authors: Grégoire Lafay, Emmanouil Benetos, Mathieu Lagrange
Date: 2017-11-15 00:00:00
Tasks: Event Detection, General Classification, Sound Event Detection

As part of the 2016 public evaluation challenge on Detection and
Classification of Acoustic Scenes and Events (DCASE 2016), the second task
focused on evaluating sound event detection systems using synthetic mixtures of
office sounds. This task, which follows the `Event Detection - Office
Synthetic' task of DCASE 2013, studies the behaviour of tested algorithms when
facing controlled levels of audio complexity with respect to background noise
and polyphony/density, with the added benefit of a very accurate ground truth.
This paper presents the task formulation, evaluation metrics, submitted
systems, and provides a statistical analysis of the results achieved, with
respect to various aspects of the evaluation dataset.
Detecting and assessing contextual change in diachronic text documents using context volatility
Authors: Christian Kahmann, Andreas Niekler, Gerhard Heyer
Date: 2017-11-15 00:00:00

Terms in diachronic text corpora may exhibit a high degree of semantic
dynamics that is only partially captured by the common notion of semantic
change. The new measure of context volatility that we propose models the degree
by which terms change context in a text collection over time. The computation
of context volatility for a word relies on the significance-values of its
co-occurrent terms and the corresponding co-occurrence ranks in sequential time
spans. We define a baseline and present an efficient computational approach in
order to overcome problems related to computational issues in the data
structure. Results are evaluated both, on synthetic documents that are used to
simulate contextual changes, and a real example based on British newspaper
texts.
Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals
Authors: Aaqib Saeed, Stojan Trajanovski
Date: 2017-11-15 00:00:00
Tasks: Management, Multi-Task Learning

Stress can be seen as a physiological response to everyday emotional, mental
and physical challenges. A long-term exposure to stressful situations can have
negative health consequences, such as increased risk of cardiovascular diseases
and immune system disorder. Therefore, a timely stress detection can lead to
systems for better management and prevention in future circumstances. In this
paper, we suggest a multi-task learning based neural network approach (with
hard parameter sharing of mutual representation and task-specific layers) for
personalized stress recognition using skin conductance and heart rate from
wearable devices. The proposed method is tested on multi-modal physiological
responses collected during real-world and simulator driving tasks.
A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis
Authors: Patrick Rodler, Wolfgang Schmid, Konstantin Schekotihin
Date: 2017-11-15 00:00:00
Tasks: Sequential Diagnosis

Model-Based Diagnosis deals with the identification of the real cause of a
system's malfunction based on a formal system model and observations of the
system behavior. When a malfunction is detected, there is usually not enough
information available to pinpoint the real cause and one needs to discriminate
between multiple fault hypotheses (called diagnoses). To this end, Sequential
Diagnosis approaches ask an oracle for additional system measurements.
  This work presents strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and show how query properties can be guaranteed
which existing methods do not provide. Evaluation results using real-world
problems indicate that the new method computes (virtually) optimal queries
instantly independently of the size and complexity of the considered diagnosis
problems and outperforms equally general methods not exploiting the proposed
theory by orders of magnitude.
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation
Authors: Geraldin Nanfack, Azeddine Elhassouny, Rachid Oulad Haj Thami
Date: 2017-11-15 00:00:00
Tasks: Autonomous Driving, Decoder, object-detection, Object Detection, Segmentation, Semantic Segmentation

The recent researches in Deep Convolutional Neural Network have focused their
attention on improving accuracy that provide significant advances. However, if
they were limited to classification tasks, nowadays with contributions from
Scientific Communities who are embarking in this field, they have become very
useful in higher level tasks such as object detection and pixel-wise semantic
segmentation. Thus, brilliant ideas in the field of semantic segmentation with
deep learning have completed the state of the art of accuracy, however this
architectures become very difficult to apply in embedded systems as is the case
for autonomous driving. We present a new Deep fully Convolutional Neural
Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The
architecture is based on Encoder-Decoder style. We use a SqueezeNet-like
encoder and a decoder formed by our proposed squeeze-decoder module and
upsample layer using downsample indices like in SegNet and we add a
deconvolution layer to provide final multi-channel feature map. On datasets
like Camvid or City-states, our net gets SegNet-level accuracy with less than
10 times fewer parameters than SegNet.
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
Authors: Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sellamanickam Sundararajan, Shravan Narayanamurthy, Rahul Kidambi
Date: 2017-11-15 00:00:00

For many applications, an ensemble of base classifiers is an effective
solution. The tuning of its parameters(number of classes, amount of data on
which each classifier is to be trained on, etc.) requires G, the generalization
error of a given ensemble. The efficient estimation of G is the focus of this
paper. The key idea is to approximate the variance of the class
scores/probabilities of the base classifiers over the randomness imposed by the
training subset by normal/beta distribution at each point x in the input
feature space. We estimate the parameters of the distribution using a small set
of randomly chosen base classifiers and use those parameters to give efficient
estimation schemes for G. We give empirical evidence for the quality of the
various estimators. We also demonstrate their usefulness in making design
choices such as the number of classifiers in the ensemble and the size of a
subset of data used for training that is needed to achieve a certain value of
generalization error. Our approach also has great potential for designing
distributed ensemble classifiers.
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
Authors: Cristina España-Bonet, Ádám Csaba Varga, Alberto Barrón-Cedeño, Josef van Genabith
Date: 2017-04-18 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation, Word Embeddings

End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate between many languages at almost no additional cost other than
training time. Furthermore, internal representations learned by the network
serve as a new semantic representation of words -or sentences- which, unlike
standard word embeddings, are learned in an essentially bilingual or even
multilingual context. In view of these properties, the contribution of the
present work is two-fold. First, we systematically study the NMT context
vectors, i.e. output of the encoder, and their power as an interlingua
representation of a sentence. We assess their quality and effectiveness by
measuring similarities across translations, as well as semantically related and
semantically unrelated sentence pairs. Second, as extrinsic evaluation of the
first point, we identify parallel sentences in comparable corpora, obtaining an
F1=98.2% on data from a shared task when using only NMT context vectors. Using
context vectors jointly with similarity measures F1 reaches 98.9%.
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics
Authors: Appina Balasubramanyam, Jalli Akshith, Battula Shanmukh Srinivas, Channappayya S Sumohana
Date: 2017-11-15 00:00:00
Tasks: Image Quality Assessment, Optical Flow Estimation, Video Quality Assessment

We present a no reference (NR) quality assessment algorithm for assessing the
perceptual quality of natural stereoscopic 3D (S3D) videos. This work is
inspired by our finding that the joint statistics of the subband coefficients
of motion (optical flow or motion vector magnitude) and depth (disparity map)
of natural S3D videos possess a unique signature. Specifically, we empirically
show that the joint statistics of the motion and depth subband coefficients of
S3D video frames can be modeled accurately using a Bivariate Generalized
Gaussian Distribution (BGGD). We then demonstrate that the parameters of the
BGGD model possess the ability to discern quality variations in S3D videos.
Therefore, the BGGD model parameters are employed as motion and depth quality
features. In addition to these features, we rely on a frame level spatial
quality feature that is computed using a robust off the shelf NR image quality
assessment (IQA) algorithm. These frame level motion, depth and spatial
features are consolidated and used with the corresponding S3D video's
difference mean opinion score (DMOS) labels for supervised learning using
support vector regression (SVR). The overall quality of an S3D video is
computed by averaging the frame level quality predictions of the constituent
video frames. The proposed algorithm, dubbed Video QUality Evaluation using
MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state of the
art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality
assessment databases.
A Convex Parametrization of a New Class of Universal Kernel Functions
Authors: Brendon K. Colbert, Matthew M. Peet
Date: 2017-11-15 00:00:00
Tasks: All

The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear parameterization (tractability); be dense in the set of all kernels (accuracy); and every member should be universal so that the hypothesis space is infinite-dimensional (scalability). Currently, there is no class of kernel that meets all three criteria - e.g. Gaussians are not tractable or accurate; polynomials are not scalable. We propose a new class that meet all three criteria - the Tessellated Kernel (TK) class. Specifically, the TK class: admits a linear parameterization using positive matrices; is dense in all kernels; and every element in the class is universal. This implies that the use of TK kernels for learning the kernel can obviate the need for selecting candidate kernels in algorithms such as SimpleMKL and parameters such as the bandwidth. Numerical testing on soft margin Support Vector Machine (SVM) problems show that algorithms using TK kernels outperform other kernel learning algorithms and neural networks. Furthermore, our results show that when the ratio of the number of training data to features is high, the improvement of TK over MKL increases significantly.
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Authors: Yannic Kilcher, Thomas Hofmann
Date: 2017-11-15 00:00:00

Black-Box attacks on machine learning models occur when an attacker, despite
having no access to the inner workings of a model, can successfully craft an
attack by means of model theft. The attacker will train an own substitute model
that mimics the model to be attacked. The substitute can then be used to design
attacks against the original model, for example by means of adversarial
samples. We put ourselves in the shoes of the defender and present a method
that can successfully avoid model theft by mounting a counter-attack.
Specifically, to any incoming query, we slightly perturb our output label
distribution in a way that makes substitute training infeasible. We demonstrate
that the perturbation does not affect the ordinary use of our model, but
results in an effective defense against attacks based on model theft.
Can clone detection support quality assessments of requirements specifications?
Authors: Elmar Juergens, Florian Deissenboeck, Martin Feilkas, Benjamin Hummel, Bernhard Schaetz, Stefan Wagner, Christoph Domann, Jonathan Streit
Date: 2017-11-15 00:00:00
Tasks: Clone Detection

Due to their pivotal role in software engineering, considerable effort is
spent on the quality assurance of software requirements specifications. As they
are mainly described in natural language, relatively few means of automated
quality assessment exist. However, we found that clone detection, a technique
widely applied to source code, is promising to assess one important quality
aspect in an automated way, namely redundancy that stems from copy&paste
operations. This paper describes a large-scale case study that applied clone
detection to 28 requirements specifications with a total of 8,667 pages. We
report on the amount of redundancy found in real-world specifications, discuss
its nature as well as its consequences and evaluate in how far existing code
clone detection approaches can be applied to assess the quality of requirements
specifications in practice.
Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation
Authors: Alex Groce, Josie Holmes
Date: 2017-11-05 00:00:00

Many methods for automated software test generation, including some that
explicitly use machine learning (and some that use ML more broadly conceived)
derive new tests from existing tests (often referred to as seeds). Often, the
seed tests from which new tests are derived are manually constructed, or at
least simpler than the tests that are produced as the final outputs of such
test generators. We propose annotation of generated tests with a provenance
(trail) showing how individual generated tests of interest (especially failing
tests) derive from seed tests, and how the population of generated tests
relates to the original seed tests. In some cases, post-processing of generated
tests can invalidate provenance information, in which case we also propose a
method for attempting to construct "pseudo-provenance" describing how the tests
could have been (partly) generated from seeds.
Tracking Typological Traits of Uralic Languages in Distributed Language Representations
Authors: Johannes Bjerva, Isabelle Augenstein
Date: 2017-11-15 00:00:00
Proceeding: WS 2018 1

Although linguistic typology has a long history, computational approaches
have only recently gained popularity. The use of distributed representations in
computational linguistics has also become increasingly popular. A recent
development is to learn distributed representations of language, such that
typologically similar languages are spatially close to one another. Although
empirical successes have been shown for such language representations, they
have not been subjected to much typological probing. In this paper, we first
look at whether this type of language representations are empirically useful
for model transfer between Uralic languages in deep neural networks. We then
investigate which typological features are encoded in these representations by
attempting to predict features in the World Atlas of Language Structures, at
various stages of fine-tuning of the representations. We focus on Uralic
languages, and find that some typological traits can be automatically inferred
with accuracies well above a strong baseline.
Aicyber's System for NLPCC 2017 Shared Task 2: Voting of Baselines
Authors: Du Steven, Xi Zhang
Date: 2017-11-15 00:00:00
Tasks: Deep Learning, Task 2

This paper presents Aicyber's system for NLPCC 2017 shared task 2. It is
formed by a voting of three deep learning based system trained on
character-enhanced word vectors and a well known bag-of-word model.
A Machine Learning Approach to Modeling Human Migration
Authors: Caleb Robinson, Bistra Dilkina
Date: 2017-11-15 00:00:00
Tasks: BIG-bench Machine Learning

Human migration is a type of human mobility, where a trip involves a person
moving with the intention of changing their home location. Predicting human
migration as accurately as possible is important in city planning applications,
international trade, spread of infectious diseases, conservation planning, and
public policy development. Traditional human mobility models, such as gravity
models or the more recent radiation model, predict human mobility flows based
on population and distance features only. These models have been validated on
commuting flows, a different type of human mobility, and are mainly used in
modeling scenarios where large amounts of prior ground truth mobility data are
not available. One downside of these models is that they have a fixed form and
are therefore not able to capture more complicated migration dynamics. We
propose machine learning models that are able to incorporate any number of
exogenous features, to predict origin/destination human migration flows. Our
machine learning models outperform traditional human mobility models on a
variety of evaluation metrics, both in the task of predicting migrations
between US counties as well as international migrations. In general, predictive
machine learning models of human migration will provide a flexible base with
which to model human migration under different what-if conditions, such as
potential sea level rise or population growth scenarios.
A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
Authors: Thomas Mosgaard Giselsson, Rasmus Nyholm Jørgensen, Peter Kryger Jensen, Mads Dyrmann, Henrik Skov Midtiby
Date: 2017-11-15 00:00:00
Tasks: General Classification

A database of images of approximately 960 unique plants belonging to 12
species at several growth stages is made publicly available. It comprises
annotated RGB images with a physical resolution of roughly 10 pixels per mm. To
standardise the evaluation of classification results obtained with the
database, a benchmark based on $f_{1}$ scores is proposed. The dataset is
available at https://vision.eng.au.dk/plant-seedlings-dataset
Hibikino-Musashi@Home 2017 Team Description Paper
Authors: Sansei Hori, Yutaro Ishida, Yuta Kiyama, Yuichiro Tanaka, Yuki Kuroda, Masataka Hisano, Yuto Imamura, Tomotaka Himaki, Yuma Yoshimoto, Yoshiya Aratani, Kouhei Hashimoto, Gouki Iwamoto, Hiroto Fujita, Takashi Morie, Hakaru Tamukoh
Date: 2017-11-15 00:00:00

Our team Hibikino-Musashi@Home was founded in 2010. It is based in Kitakyushu
Science and Research Park, Japan. Since 2010, we have participated in the
RoboCup@Home Japan open competition open-platform league every year. Currently,
the Hibikino-Musashi@Home team has 24 members from seven different laboratories
based in the Kyushu Institute of Technology. Our home-service robots are used
as platforms for both education and implementation of our research outcomes. In
this paper, we introduce our team and the technologies that we have implemented
in our robots.
Drift Analysis and Evolutionary Algorithms Revisited
Authors: Johannes Lengler, Angelika Steger
Date: 2016-08-10 00:00:00
Tasks: Evolutionary Algorithms

One of the easiest randomized greedy optimization algorithms is the following
evolutionary algorithm which aims at maximizing a boolean function $f:\{0,1\}^n
\to {\mathbb R}$. The algorithm starts with a random search point $\xi \in
\{0,1\}^n$, and in each round it flips each bit of $\xi$ with probability $c/n$
independently at random, where $c>0$ is a fixed constant. The thus created
offspring $\xi'$ replaces $\xi$ if and only if $f(\xi') \ge f(\xi)$. The
analysis of the runtime of this simple algorithm on monotone and on linear
functions turned out to be highly non-trivial. In this paper we review known
results and provide new and self-contained proofs of partly stronger results.
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Authors: Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice, Hank Liao, Ananda Theertha Suresh, Felix Yu
Date: 2017-11-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Recurrent neural network (RNN) language models (LMs) and Long Short Term
Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
traditional N-gram LMs on speech recognition tasks. However, these models are
computationally more expensive than N-gram LMs for decoding, and thus,
challenging to integrate into speech recognizers. Recent research has proposed
the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an
efficient strategy to integrate these models into a speech recognition system.
In this paper, we evaluate existing lattice rescoring algorithms along with new
variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs
reduces the word error rate (WER) for this task by 8\% relative to the WER
obtained using an N-gram LM.
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models
Authors: Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, Teodoro Laino
Date: 2017-11-13 00:00:00
Tasks: Translation

There is an intuitive analogy of an organic chemist's understanding of a
compound and a language speaker's understanding of a word. Consequently, it is
possible to introduce the basic concepts and analyze potential impacts of
linguistic analysis to the world of organic chemistry. In this work, we cast
the reaction prediction task as a translation problem by introducing a
template-free sequence-to-sequence model, trained end-to-end and fully
data-driven. We propose a novel way of tokenization, which is arbitrarily
extensible with reaction information. With this approach, we demonstrate
results superior to the state-of-the-art solution by a significant margin on
the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1%
without relying on auxiliary knowledge such as reaction templates. Also, 66.4%
accuracy is reached on a larger and noisier dataset.