pwc-archive/papers-with-abstracts huggingface dataset:
When a Period Is Not a Full Stop: Light Curve Structure Reveals Fundamental Parameters of Cepheid and RR Lyrae Stars
Authors: Earl P. Bellinger, Shashi M. Kanbur, Anupam Bhardwaj, Marcella Marconi
Date: 2019-11-25 00:00:00

The period of pulsation and the structure of the light curve for Cepheid and RR Lyrae variables depend on the fundamental parameters of the star: mass, radius, luminosity, and effective temperature. Here we train artificial neural networks on theoretical pulsation models to predict the fundamental parameters of these stars based on their period and light curve structure. We find significant improvements to estimates of these parameters made using light curve structure and period over estimates made using only the period. Given that the models are able to reproduce most observables, we find that the fundamental parameters of these stars can be estimated up to 60% more accurately when light curve structure is taken into consideration. We quantify which aspects of light curve structure are most important in determining fundamental parameters, and find for example that the second Fourier amplitude component of RR Lyrae light curves is even more important than period in determining the effective temperature of the star. We apply this analysis to observations of hundreds Cepheids in the Large Magellanic Cloud and thousands of RR Lyrae in the Magellanic Clouds and Galactic bulge to produce catalogs of estimated masses, radii, luminosities, and other parameters of these stars. As an example application, we estimate Wesenheit indices and use those to derive distance moduli to the Magellanic Clouds of $\mu_{\text{LMC},\text{CEP}} = 18.688 \pm 0.093$, $\mu_{\text{LMC},\text{RRL}} = 18.52 \pm 0.14$, and $\mu_{\text{SMC},\text{RRL}} = 18.88 \pm 0.17$ mag.
Neural Percussive Synthesis Parameterised by High-Level Timbral Features
Authors: António Ramires, Pritish Chandna, Xavier Favory, Emilia Gómez, Xavier Serra
Date: 2019-11-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.
Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation Task -- AI Regression Approach for Dementia Onset Prediction in Aging Societies
Authors: Tomasz M. Rutkowski, Masato S. Abe, Marcin Koculak, Mihoko Otake-Matsuura
Date: 2019-11-25 00:00:00

We present a practical health-theme machine learning (ML) application concerning `AI for social good' domain for `Producing Good Outcomes' track. In particular, the solution is concerning the problem of a potential elderly adult dementia onset prediction in aging societies. The paper discusses our attempt and encouraging preliminary study results of behavioral responses analysis in a working memory-based emotional evaluation experiment. We focus on the development of digital biomarkers for dementia progress detection and monitoring. We present a behavioral data collection concept for a subsequent AI-based application together with a range of regression encouraging results of Montreal Cognitive Assessment (MoCA) scores in the leave-one-subject-out cross-validation setup. The regressor input variables include experimental subject's emotional valence and arousal recognition responses, as well as reaction times, together with self-reported education levels and ages, obtained from a group of twenty older adults taking part in the reported data collection project. The presented results showcase the potential social benefits of artificial intelligence application for elderly and establish a step forward to develop ML approaches, for the subsequent application of simple behavioral objective testing for dementia onset diagnostics replacing subjective MoCA.
Classification of Single-lead Electrocardiograms: TDA Informed Machine Learning
Authors: Paul Samuel Ignacio, David Uminsky, Christopher Dunstan, Esteban Escobar, Luke Trujillo
Date: 2019-11-25 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Atrial Fibrillation is a heart condition characterized by erratic heart rhythms caused by chaotic propagation of electrical impulses in the atria, leading to numerous health complications. State-of-the-art models employ complex algorithms that extract expert-informed features to improve diagnosis. In this note, we demonstrate how topological features can be used to help accurately classify single lead electrocardiograms. Via delay embeddings, we map electrocardiograms onto high-dimensional point-clouds that convert periodic signals to algebraically computable topological signatures. We derive features from persistent signatures, input them to a simple machine learning algorithm, and benchmark its performance against winning entries in the 2017 Physionet Computing in Cardiology Challenge.
Robot Affect: the Amygdala as Bloch Sphere
Authors: Johan F. Hoorn, Johnny K. W. Ho
Date: 2019-11-22 00:00:00

In the design of artificially sentient robots, an obstacle always has been that conventional computers cannot really process information in parallel, whereas the human affective system is capable of producing experiences of emotional concurrency (e.g., happy and sad). Another schism that has been in the way is the persistent Cartesian divide between cognition and affect, whereas people easily can reflect on their emotions or have feelings about a thought. As an essentially theoretical exercise, we posit that quantum physics at the basis of neurology explains observations in cognitive emotion psychology from the belief that the construct of reality is partially imagined (Im) in the complex coordinate space C^3. We propose a quantum computational account to mixed states of reflection and affect, while transforming known psychological dimensions into the actual quantum dynamics of electromotive forces. As a precursor to actual simulations, we show examples of possible robot behaviors, using Einstein-Podolsky-Rosen circuits. Keywords: emotion, reflection, modelling, quantum computing
Asymmetric Correntropy for Robust Adaptive Filtering
Authors: Badong Chen, Yuqing Xie, Zhuang Li, Yingsong Li, Pengju Ren
Date: 2019-11-21 00:00:00

In recent years, correntropy has been seccessfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random variables is symmetrically distributed around zero. For the case of asymmetric error distribution, the symmetric Gaussian kernel is however inappropriate and cannot adapt to the error distribution well. To address this problem, in this brief we propose a new variant of correntropy, named asymmetric correntropy, which uses an asymmetric Gaussian model as the kernel function. In addition, a robust adaptive filtering algorithm based on asymmetric correntropy is developed and its steady-state convergence performance is analyzed. Simulations are provided to confirm the theoretical results and good performance of the proposed algorithm.
Unstructured point cloud semantic labelingusing deep segmentation networks
Authors: A. Boulch, B. Le Saux and N. Audebert
Date: 2017-04-23 00:00:00
Proceeding: Eurographics 3DOR 2017 2017 4
Tasks: Semantic Segmentation

In this work, we describe a new, general, and efficient method for unstructured point cloud labeling. As the question of efficiently using deep Convolutional Neural Networks (CNNs) on 3D data is still a pending issue, we propose a framework which applies CNNs on multiple 2D image views (or snapshots) of the point cloud. The approach consists in three core ideas. (i) We pick many suitable snapshots of the point cloud. We generate two types of images: a Red-Green-Blue  (RGB) view and a  depth composite view containing geometric features. (ii) We then perform a pixel-wise labeling of each pair of 2D snapshots using fully convolutional networks. Different architectures are tested to achieve a profitable fusion of our heterogeneous inputs. (iii) Finally, we perform fast back-projection of the label predictions in the 3D space using efficient buffering to label every 3D point. Experiments show that our method is suitable for various types of point clouds such as Lidar or photogrammetric data.
Fast semantic segmentation of 3d point clouds with strongly varying density
Authors: Timo Hackel, Jan D. Wegner, Konrad Schindler
Date: 2016-03-07 00:00:00
Proceeding: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2016 3
Tasks: Classification, General Classification, Semantic Segmentation

We describe an effective and efficient method for point-wise semantic classification of  3D point clouds.   The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogrammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations.  By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute.  We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.
Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers
Authors: Zhaoyu Liu, Brian Mak
Date: 2019-11-26 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Cloning

We investigate a novel cross-lingual multi-speaker text-to-speech synthesis approach for generating high-quality native or accented speech for native/foreign seen/unseen speakers in English and Mandarin. The system consists of three separately trained components: an x-vector speaker encoder, a Tacotron-based synthesizer and a WaveNet vocoder. It is conditioned on 3 kinds of embeddings: (1) speaker embedding so that the system can be trained with speech from many speakers will little data from each speaker; (2) language embedding with shared phoneme inputs; (3) stress and tone embedding which improves naturalness of synthesized speech, especially for a tonal language like Mandarin. By adjusting the various embeddings, MOS results show that our method can generate high-quality natural and intelligible native speech for native/foreign seen/unseen speakers. Intelligibility and naturalness of accented speech is low as expected. Speaker similarity is good for native speech from native speakers. Interestingly, speaker similarity is also good for accented speech from foreign speakers. We also find that normalizing speaker embedding x-vectors by L2-norm normalization or whitening improves output quality a lot in many cases, and the WaveNet performance seems to be language-independent: our WaveNet is trained with Cantonese speech and can be used to generate Mandarin and English speech very well.
Machine Learning Approach for Reliability Assessment of Open Source Software
Authors: Ranjan Kumar Behera, Santanu Kumar Rath, Sanjay Misra, Marcelo Leon, Adewole Adewumi(
Date: 2019-06-29 00:00:00
Proceeding: International Conference on Computational Science and Its Applications 2019 6
Tasks: BIG-bench Machine Learning

Some of the quality parameters for any successful open
source software may be attributed to affordability, availability of source
code, re-distributability, and modifiability etc. Quality of software can be
further improvised subsequently by either users or associated developers
by constantly monitoring some of the reliability aspects. Since multiple
users are allowed to modify the code there is a potential threat for security,
which might degrade the reliability of software. Bug tracking systems
are often considered to monitor various software faults, detected mostly
in open source software projects. Various authors have made research
in this direction by applying different techniques in order to improve
the reliability of open source software projects. In this work, an various
machine learning models have been implemented to examine the reliability
of the software. An extensive numerical illustration has also been
presented for bug data recorded on bug tracking system. The effectiveness
of machine learning models for estimating the level of faults associated
with the systems has been verified by comparing it with similar
approaches as available in the literature.
Talking about what is not there: Generating indefinite referring expressions in Minecraft
Authors: Arne K{\"o}hn, Alex Koller, er
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Minecraft, Object

When generating technical instructions, it is often necessary to describe an object that does not exist yet. For example, an NLG system which explains how to build a house needs to generate sentences like {``}build *a wall of height five to your left*{''} and {``}now build *a wall on the other side*.{''} Generating (indefinite) referring expressions to objects that do not exist yet is fundamentally different from generating the usual definite referring expressions, because the new object must be distinguished from an infinite set of possible alternatives. We formalize this problem and present an algorithm for generating such expressions, in the context of generating building instructions within the Minecraft video game.
Generating Quantified Referring Expressions with Perceptual Cost Pruning
Authors: Gordon Briggs, Hillary Harner
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10

We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases.
A case study on context-bound referring expression generation
Authors: Maurice Langner
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Referring Expression, Referring expression generation

In recent years, Bayesian models of referring expression generation have gained prominence in order to produce situationally more adequate referring expressions. Basically, these models enable the integration of different parameters into the decision process for using a specific referring expression like the cardinality of the object set, the configuration and complexity of the visual field, and the discriminatory power of available attributes that need to be combined with visual salience and personal preference. This paper describes and discusses the results of an empirical study on the production of referring expressions in visual fields with different object configurations of varying complexity and different contextual premises for using a referring expression. The visual fields are set up using data from the TUNA experiment with plain random or pragmatically enriched configurations which allow for target inference. Different categories of the situational contexts, in which the referring expressions are produced, provide different degrees of cooperativeness, so that generation quality and its relations to contextual user intention can be observed. The results of the study suggest that Bayesian approaches must integrate individual generation preference and the cooperativeness of the situational task in order to model the broad variance between speakers more adequately.
Choosing between Long and Short Word Forms in Mandarin
Authors: Lin Li, Kees Van Deemter, Denis Paperno, Jingyu Fan
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Form

Between 80{\%} and 90{\%} of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013). Consequently, the choice between long and short forms is a key problem for lexical choice across NLP and NLG. Following an earlier work on abbreviations in English (Mahowald et al, 2013), we bring a probabilistic perspective to these questions, using both a behavioral and a corpus-based approach. We hypothesized that there is a higher probability of choosing short form in supportive context than in neutral context in Mandarin. Consistent with our prediction, our findings revealed that predictability of contexts makes effect on speakers{'} long and short form choice.
Computational Argumentation Synthesis as a Language Modeling Task
Authors: Roxanne El Baff, Henning Wachsmuth, Khalid Al Khatib, Manfred Stede, Benno Stein
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Language Modeling, Language Modelling

Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates. Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations. In this paper, we approach such an argumentation synthesis as a language modeling task. In our language model, argumentative discourse units are the {``}words{''}, and arguments represent the {``}sentences{''}. Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units{'} argumentative roles, and finally {``}phrases{''} an argument by instantiating the structure with semantically coherent units from the pool. Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.
A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog
Authors: Jinfeng Rao, Kartikeya Upasani, Anusha Balakrishnan, Michael White, Anuj Kumar, Rajen Subba
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Sentence

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Sequence-to-sequence models on flat meaning representations (MR) have been dominant in this task, for example in the E2E NLG Challenge. Previous work has shown that a tree-structured MR can improve the model for better discourse-level structuring and sentence-level planning. In this work, we propose a tree-to-sequence model that uses a tree-LSTM encoder to leverage the tree structures in the input MR, and further enhance the decoding by a structure-enhanced attention mechanism. In addition, we explore combining these enhancements with constrained decoding to improve semantic correctness. Our experiments not only show significant improvements over standard seq2seq baselines, but also is more data-efficient and generalizes better to hard scenarios.
Multiple News Headlines Generation using Page Metadata
Authors: Kango Iwama, Yoshinobu Kano
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Headline Generation

Multiple headlines of a newspaper article have an important role to express the content of the article accurately and concisely. A headline depends on the content and intent of their article. While a single headline expresses the whole corresponding article, each of multiple headlines expresses different information individually. We suggest automatic generation method of such a diverse multiple headlines in a newspaper. Our generation method is based on the Pointer-Generator Network, using page metadata on a newspaper which can change headline generation behavior. This page metadata includes headline location, headline size, article page number, etc. In a previous related work, ensemble of three different generation models was performed to obtain a single headline, where each generation model generates a single headline candidate. In contrast, we use a single model to generate multiple headlines. We conducted automatic evaluations for generated headlines. The results show that our method improved ROUGE-1 score by 4.32 points higher than baseline. These results suggest that our model using page metadata can generate various multiple headlines for an article In better performance.
Neural Question Generation using Interrogative Phrases
Authors: Yuichi Sasazawa, Sho Takase, Naoaki Okazaki
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Question Answering, Question Generation, Question-Generation

Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.
Generating Text from Anonymised Structures
Authors: Emilie Colin, Claire Gardent
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 10
Tasks: Text Generation

Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model{'}s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.