pwc-archive/papers-with-abstracts huggingface dataset:
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
Authors: Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr
Date: 2019-04-17 00:00:00
Tasks: Evolutionary Algorithms

We analyze the performance of the 2-rate $(1+\lambda)$ Evolutionary Algorithm
(EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a
$(1+\lambda)$~EA variant using multiplicative update rules on the OneMax
problem. We compare their efficiency for offspring population sizes ranging up
to $\lambda=3,200$ and problem sizes up to $n=100,000$.
  Our empirical results show that the ranking of the algorithms is very
consistent across all tested dimensions, but strongly depends on the population
size. While for small values of $\lambda$ the 2-rate EA performs best, the
multiplicative updates become superior for starting for some threshold value of
$\lambda$ between 50 and 100. Interestingly, for population sizes around 50,
the $(1+\lambda)$~EA with static mutation rates performs on par with the best
of the self-adjusting algorithms.
  We also consider how the lower bound $p_{\min}$ for the mutation rate
influences the efficiency of the algorithms. We observe that for the 2-rate EA
and the EA with multiplicative update rules the more generous bound
$p_{\min}=1/n^2$ gives better results than $p_{\min}=1/n$ when $\lambda$ is
small. For both algorithms the situation reverses for large~$\lambda$.
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition
Authors: Jiabin Xue, Jiqing Han, Tieran Zheng, Jiaxing Guo, Boyong Wu
Date: 2019-04-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Multiple Instance Learning, speech-recognition, Speech Recognition

It is an effective way that improves the performance of the existing
Automatic Speech Recognition (ASR) systems by retraining with more and more new
training data in the target domain. Recently, Deep Neural Network (DNN) has
become a successful model in the ASR field. In the training process of the DNN
based methods, a back propagation of error between the transcription and the
corresponding annotated text is used to update and optimize the parameters.
Thus, the parameters are more influenced by the training samples with a big
propagation error than the samples with a small one. In this paper, we define
the samples with significant error as the hard samples and try to improve the
performance of the ASR system by adding many of them. Unfortunately, the hard
samples are sparse in the training data of the target domain, and manually
label them is expensive. Therefore, we propose a hard samples mining method
based on an enhanced deep multiple instance learning, which can find the hard
samples from unlabeled training data by using a small subset of the dataset
with manual labeling in the target domain. We applied our method to an End2End
ASR task and obtained the best performance.
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Authors: Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee
Date: 2019-04-17 00:00:00
Tasks: Clustering, Information Retrieval, Recommendation Systems

Industrial recommender systems usually consist of the matching stage and the
ranking stage, in order to handle the billion-scale of users and items. The
matching stage retrieves candidate items relevant to user interests, while the
ranking stage sorts candidate items by user interests. Thus, the most critical
ability is to model and represent user interests for either stage. Most of the
existing deep learning-based models represent one user as a single vector which
is insufficient to capture the varying nature of user's interests. In this
paper, we approach this problem from a different view, to represent one user
with multiple vectors encoding the different aspects of the user's interests.
We propose the Multi-Interest Network with Dynamic routing (MIND) for dealing
with user's diverse interests in the matching stage. Specifically, we design a
multi-interest extractor layer based on capsule routing mechanism, which is
applicable for clustering historical behaviors and extracting diverse
interests. Furthermore, we develop a technique named label-aware attention to
help learn a user representation with multiple vectors. Through extensive
experiments on several public benchmarks and one large-scale industrial dataset
from Tmall, we demonstrate that MIND can achieve superior performance than
state-of-the-art methods for recommendation. Currently, MIND has been deployed
for handling major online traffic at the homepage on Mobile Tmall App.
How to define co-occurrence in different domains of study?
Authors: Mathieu Roche
Date: 2019-04-16 00:00:00
Tasks: Position

This position paper presents a comparative study of co-occurrences. Some
similarities and differences in the definition exist depending on the research
domain (e.g. linguistics, NLP, computer science). This paper discusses these
points, and deals with the methodological aspects in order to identify
co-occurrences in a multidisciplinary paradigm.
Clustered Object Detection in Aerial Images
Authors: Fan Yang, Heng Fan, Peng Chu, Erik Blasch, Haibin Ling
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Object, object-detection, Object Detection, Object Detection In Aerial Images

Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues inspired by observing that these targets are often clustered. In particular, we propose a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet). Given an input image, CPNet produces object cluster regions and ScaleNet estimates object scales for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has several advantages over previous solutions: (1) it greatly reduces the number of chips for final object detection and hence achieves high running time efficiency, (2) the cluster-based scale estimation is more accurate than previously used single-object based ones, hence effectively improves the detection for small objects, and (3) the final DetecNet is dedicated for clustered regions and implicitly models the prior context information so as to boost detection accuracy. The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet achieves promising performance in comparison with state-of-the-art detectors. Code will be available in \url{https://github.com/fyangneil}.
SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula
Authors: Colin Wan, Zheng Li, Alicia Guo, Yue Zhao
Date: 2019-04-16 00:00:00
Tasks: Feature Engineering

Synthetic population generation is the process of combining multiple socioeconomic and demographic datasets from different sources and/or granularity levels, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this study, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) to fill the gap. SynC first removes potential outliers in the data and then fits the filtered data with a Gaussian copula model to correctly capture dependencies and marginal distributions of sampled survey data. Finally, SynC leverages predictive models to merge datasets into one and then scales them accordingly to match the marginal constraints. We make three key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 3) release an easy-to-use framework implementation for reproducibility, and 4) ensure the methodology is scalable at the production level and can easily incorporate new data.
A Systematic Study of Leveraging Subword Information for Learning Word Representations
Authors: Yi Zhu, Ivan Vulić, Anna Korhonen
Date: 2019-04-16 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dependency Parsing, Entity Typing, Representation Learning, Segmentation, Word Similarity

The use of subword-level information (e.g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning. Its importance is attested especially for morphologically rich languages which generate a large number of rare words. Despite a steadily increasing interest in such subword-informed word representations, their systematic comparative analysis across typologically diverse languages and different tasks is still missing. In this work, we deliver such a study focusing on the variation of two crucial components required for subword-level integration into word representation models: 1) segmentation of words into subword units, and 2) subword composition functions to obtain final word representations. We propose a general framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components, also including more advanced techniques based on position embeddings and self-attention. Using the unified framework, we run experiments over a large number of subword-informed word representation configurations (60 in total) on 3 tasks (general and rare word similarity, dependency parsing, fine-grained entity typing) for 5 languages representing 3 language types. Our main results clearly indicate that there is no "one-sizefits-all" configuration, as performance is both language- and task-dependent. We also show that configurations based on unsupervised segmentation (e.g., BPE, Morfessor) are sometimes comparable to or even outperform the ones based on supervised word segmentation.
Machine learning for early prediction of circulatory failure in the intensive care unit
Authors: Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Circulatory Failure

Intensive care clinicians are presented with large quantities of patient
information and measurements from a multitude of monitoring systems. The
limited ability of humans to process such complex information hinders
physicians to readily recognize and act on early signs of patient
deterioration. We used machine learning to develop an early warning system for
circulatory failure based on a high-resolution ICU database with 240 patient
years of data. This automatic system predicts 90.0% of circulatory failure
events (prevalence 3.1%), with 81.8% identified more than two hours in advance,
resulting in an area under the receiver operating characteristic curve of 94.0%
and area under the precision-recall curve of 63.0%. The model was externally
validated in a large independent patient cohort.
Query Expansion for Cross-Language Question Re-Ranking
Authors: Muhammad Mahbubur Rahman, Sorami Hisamoto, Kevin Duh
Date: 2019-04-16 00:00:00
Tasks: Community Question Answering, Question Answering, Re-Ranking, Word Embeddings

Community question-answering (CQA) platforms have become very popular forums
for asking and answering questions daily. While these forums are rich
repositories of community knowledge, they present challenges for finding
relevant answers and similar questions, due to the open-ended nature of
informal discussions. Further, if the platform allows questions and answers in
multiple languages, we are faced with the additional challenge of matching
cross-lingual information. In this work, we focus on the cross-language
question re-ranking shared task, which aims to find existing questions that may
be written in different languages. Our contribution is an exploration of query
expansion techniques for this problem. We investigate expansions based on Word
Embeddings, DBpedia concepts linking, and Hypernym, and show that they
outperform existing state-of-the-art methods.
Reducing Adversarial Example Transferability Using Gradient Regularization
Authors: George Adam, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg
Date: 2019-04-16 00:00:00

Deep learning algorithms have increasingly been shown to lack robustness to
simple adversarial examples (AdvX). An equally troubling observation is that
these adversarial examples transfer between different architectures trained on
different datasets. We investigate the transferability of adversarial examples
between models using the angle between the input-output Jacobians of different
models. To demonstrate the relevance of this approach, we perform case studies
that involve jointly training pairs of models. These case studies empirically
justify the theoretical intuitions for why the angle between gradients is a
fundamental quantity in AdvX transferability. Furthermore, we consider the
asymmetry of AdvX transferability between two models of the same architecture
and explain it in terms of differences in gradient norms between the models.
Lastly, we provide a simple modification to existing training setups that
reduces transferability of adversarial examples between pairs of models.
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
Authors: Soumyabrata Dev, Atul Nautiyal, Yee Hui Lee, Stefan Winkler
Date: 2019-04-16 00:00:00
Tasks: Cloud Detection, Image Segmentation, Segmentation, Semantic Segmentation

We analyze clouds in the earth's atmosphere using ground-based sky cameras.
An accurate segmentation of clouds in the captured sky/cloud image is
difficult, owing to the fuzzy boundaries of clouds. Several techniques have
been proposed that use color as the discriminatory feature for cloud detection.
In the existing literature, however, analysis of daytime and nighttime images
is considered separately, mainly because of differences in image
characteristics and applications. In this paper, we propose a light-weight
deep-learning architecture called CloudSegNet. It is the first that integrates
daytime and nighttime (also known as nychthemeron) image segmentation in a
single framework, and achieves state-of-the-art results on public databases.
Detection and Prediction of Cardiac Anomalies Using Wireless Body Sensors and Bayesian Belief Networks
Authors: Asim Darwaish, Farid Naït-Abdesselam, Ashfaq Khokhar
Date: 2019-04-16 00:00:00
Tasks: Diagnostic

Intricating cardiac complexities are the primary factor associated with
healthcare costs and the highest cause of death rate in the world. However,
preventive measures like the early detection of cardiac anomalies can prevent
severe cardiovascular arrests of varying complexities and can impose a
substantial impact on healthcare cost. Encountering such scenarios usually the
electrocardiogram (ECG or EKG) is the first diagnostic choice of a medical
practitioner or clinical staff to measure the electrical and muscular fitness
of an individual heart. This paper presents a system which is capable of
reading the recorded ECG and predict the cardiac anomalies without the
intervention of a human expert. The paper purpose an algorithm which read and
perform analysis on electrocardiogram datasets. The proposed architecture uses
the Discrete Wavelet Transform (DWT) at first place to perform preprocessing of
ECG data followed by undecimated Wavelet transform (UWT) to extract nine
relevant features which are of high interest to a cardiologist. The
probabilistic mode named Bayesian Network Classifier is trained using the
extracted nine parameters on UCL arrhythmia dataset. The proposed system
classifies a recorded heartbeat into four classes using Bayesian Network
classifier and Tukey's box analysis. The four classes for the prediction of a
heartbeat are (a) Normal Beat, (b) Premature Ventricular Contraction (PVC) (c)
Premature Atrial Contraction (PAC) and (d) Myocardial Infarction. The results
of experimental setup depict that the proposed system has achieved an average
accuracy of 96.6 for PAC\% 92.8\% for MI and 87\% for PVC, with an average
error rate of 3.3\% for PAC, 6\% for MI and 12.5\% for PVC on real
electrocardiogram datasets including Physionet and European ST-T Database
(EDB).
DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset
Authors: Lana Sinapayen, Atsushi Noda
Date: 2019-04-16 00:00:00

Is cognition a collection of loosely connected functions tuned to different
tasks, or can there be a general learning algorithm? If such an hypothetical
general algorithm did exist, tuned to our world, could it adapt seamlessly to a
world with different laws of nature? We consider the theory that predictive
coding is such a general rule, and falsify it for one specific neural
architecture known for high-performance predictions on natural videos and
replication of human visual illusions: PredNet. Our results show that PredNet's
high performance generalizes without retraining on a completely different
natural video dataset. Yet PredNet cannot be trained to reach even mediocre
accuracy on an artificial video dataset created with the rules of the Game of
Life (GoL). We also find that a submodule of PredNet, a Convolutional Neural
Network trained alone, reaches perfect accuracy on the GoL while being mediocre
for natural videos, showing that PredNet's architecture itself is responsible
for both the high performance on natural videos and the loss of performance on
the GoL. Just as humans cannot predict the dynamics of the GoL, our results
suggest that there might be a trade-off between high performance on sensory
inputs with different sets of rules.
Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?
Authors: Mubariz Zaffar, Ahmad Khaliq, Shoaib Ehsan, Michael Milford, Kostas Alexis, Klaus McDonald-Maier
Date: 2019-04-16 00:00:00
Tasks: Visual Place Recognition

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR.
Cross-Lingual Sentiment Quantification
Authors: Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Date: 2019-04-16 00:00:00
Tasks: Cross-Lingual Sentiment Classification, General Classification, Sentiment Analysis, Sentiment Classification

\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a population is often of higher interest than the analysis of individual instances. In this work we propose a method for \emph{Cross-Lingual Sentiment Quantification}, the task of performing sentiment quantification when training documents are available for a source language $\mathcal{S}$ but not for the target language $\mathcal{T}$ for which sentiment quantification needs to be performed. Cross-lingual sentiment quantification (and cross-lingual \emph{text} quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved. We present experimental results obtained on publicly available datasets for cross-lingual sentiment classification; the results show that the presented methods can perform cross-lingual sentiment quantification with a surprising level of accuracy.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
Authors: Wentai Zhang, Zhangsihao Yang, Haoliang Jiang, Suyash Nigam, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara
Date: 2019-04-16 00:00:00
Tasks: 3D Shape Representation, Decoder

We propose a data-driven 3D shape design method that can learn a generative
model from a corpus of existing designs, and use this model to produce a wide
range of new designs. The approach learns an encoding of the samples in the
training corpus using an unsupervised variational autoencoder-decoder
architecture, without the need for an explicit parametric representation of the
original designs. To facilitate the generation of smooth final surfaces, we
develop a 3D shape representation based on a distance transformation of the
original 3D data, rather than using the commonly utilized binary voxel
representation. Once established, the generator maps the latent space
representations to the high-dimensional distance transformation fields, which
are then automatically surfaced to produce 3D representations amenable to
physics simulations or other objective function evaluation modules. We
demonstrate our approach for the computational design of gliders that are
optimized to attain prescribed performance scores. Our results show that when
combined with genetic optimization, the proposed approach can generate a rich
set of candidate concept designs that achieve prescribed functional goals, even
when the original dataset has only a few or no solutions that achieve these
goals.
Semantic Characteristics of Schizophrenic Speech
Authors: Kfir Bar, Vered Zilberstein, Ido Ziv, Heli Baram, Nachum Dershowitz, Samuel Itzikowitz, Eiran Vadim Harel
Date: 2019-04-16 00:00:00
Proceeding: WS 2019 6

Natural language processing tools are used to automatically detect
disturbances in transcribed speech of schizophrenia inpatients who speak
Hebrew. We measure topic mutation over time and show that controls maintain
more cohesive speech than inpatients. We also examine differences in how
inpatients and controls use adjectives and adverbs to describe content words
and show that the ones used by controls are more common than the those of
inpatients. We provide experimental results and show their potential for
automatically detecting schizophrenia in patients by means only of their speech
patterns.