pwc-archive/papers-with-abstracts huggingface dataset:
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
Authors: Khe Chai Sim, Françoise Beaufays, Arnaud Benard, Dhruv Guliani, Andreas Kabel, Nikhil Khare, Tamar Lucassen, Petr Zadrazil, Harry Zhang, Leif Johnson, Giovanni Motta, Lillian Zhou
Date: 2019-12-14 00:00:00
Tasks: speech-recognition, Speech Recognition

We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ in the amounts of user effort required to provide supervision, and are evaluated on how they impact speech recognition performance. We propose using keyword-dependent precision and recall metrics to measure vocabulary acquisition performance. We evaluate the algorithms on a dataset that we designed to contain names of persons that are difficult to recognize. Therefore, the baseline recall rate for proper names in this dataset is very low: 2.4%. A data synthesis approach we developed brings it to 48.6%, with no need for speech input from the user. With speech input, if the user corrects only the names, the name recall rate improves to 64.4%. If the user corrects all the recognition errors, we achieve the best recall of 73.5%. To eliminate the need to upload user data and store personalized models on a server, we focus on performing the entire personalization workflow on a mobile device.
Callisto: Entropy based test generation and data quality assessment for Machine Learning Systems
Authors: Sakshi Udeshi, Xingbin Jiang, Sudipta Chattopadhyay
Date: 2019-12-11 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) has seen massive progress in the last decade and as a result, there is a pressing need for validating ML-based systems. To this end, we propose, design and evaluate CALLISTO - a novel test generation and data quality assessment framework. To the best of our knowledge, CALLISTO is the first blackbox framework to leverage the uncertainty in the prediction and systematically generate new test cases for ML classifiers. Our evaluation of CALLISTO on four real world data sets reveals thousands of errors. We also show that leveraging the uncertainty in prediction can increase the number of erroneous test cases up to a factor of 20, as compared to when no such knowledge is used for testing. CALLISTO has the capability to detect low quality data in the datasets that may contain mislabelled data. We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.
Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms
Authors: Diego Pinheiro, Ryan Hartman, Erick Romero, Ronaldo Menezes, Martin Cadeiras
Date: 2019-12-08 00:00:00
Tasks: Community Detection

A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the existent heterogeneity of communities extracted by different community detection algorithms, a comparative analysis of community detection algorithms for optimal HSA delineation is lacking. In this work, we compared HSA delineations produced by community detection algorithms using a large-scale dataset containing different types of hospital-patient discharges spanning a 7-year period in US. Our results replicated the heterogeneity among community detection algorithms found in previous works, the improved HSA delineation obtained by a network-based, and suggested that Infomap may be a more suitable community detection for HSA delineation since it finds a high number of HSAs with high localization index and a low network conductance.
Real-time 2019 Portuguese Parliament Election Results Dataset
Authors: Nuno Moniz
Date: 2019-12-05 00:00:00
Tasks: regression

This paper presents a data set describing the evolution of results in the Portuguese Parliamentary Elections of October 6$^{th}$ 2019. The data spans a time interval of 4 hours and 25 minutes, in intervals of 5 minutes, concerning the results of the 27 parties involved in the electoral event. The data set is tailored for predictive modelling tasks, mostly focused on numerical forecasting tasks. Regardless, it allows for other tasks such as ordinal regression or learn-to-rank.
Rumor Detection and Classification for Twitter Data
Authors: Sardar Hamidian, Mona T Diab
Date: 2019-11-25 00:00:00
Tasks: Classification, General Classification, Misinformation

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.
Hyperbolic Multiplex Network Embedding with Maps of Random Walk
Authors: Peiyuan Sun
Date: 2019-11-23 00:00:00
Tasks: Community Detection, Network Embedding

Recent research on network embedding in hyperbolic space have proven successful in several applications. However, nodes in real world networks tend to interact through several distinct channels. Simple aggregation or ignorance of this multiplexity will lead to misleading results. On the other hand, there exists redundant information between different interaction patterns between nodes. Recent research reveals the analogy between the community structure and the hyperbolic coordinate. To learn each node's effective embedding representation while reducing the redundancy of multiplex network, we then propose a unified framework combing multiplex network hyperbolic embedding and multiplex community detection. The intuitive rationale is that high order node embedding approach is expected to alleviate the observed network's sparse and noisy structure which will benefit the community detection task. On the contrary, the improved community structure will also guide the node embedding task. To incorporate the common features between channels while preserving unique features, a random walk approach which traversing in latent multiplex hyperbolic space is proposed to detect the community across channels and bridge the connection between node embedding and community detection. The proposed framework is evaluated on several network tasks using different real world dataset. The results demonstrates that our framework is effective and efficiency compared with state-of-the-art approaches.
Machine learning models show similar performance to Renewables.ninja for generation of long-term wind power time series even without location information
Authors: Johann Baumgartner, Katharina Gruber, Sofia Simoes, Yves-Marie Saint-Drenan, Johannes Schmidt
Date: 2019-12-09 00:00:00
Tasks: Time Series, Time Series Analysis

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation despite their need for accurate location information as well as for bias correction, and their insufficient replication of extreme events and short-term power ramps. We assess how time series generated by machine learning models (MLM) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we apply neural networks to one MERRA2 reanalysis wind speed input dataset with no location information and one with basic location information. The resulting time series and the RN time series are compared with actual generation. Both MLM time series feature equal or even better time series quality than RN depending on the characteristics considered. We conclude that MLM models can, even when reducing information on turbine locations and turbine types, produce time series of at least equal quality to RN.
Proceedings of the twelfth Workshop on Answer Set Programming and Other Computing Paradigms 2019
Authors: Jorge Fandinno, Johannes Fichte
Date: 2019-11-21 00:00:00

This is the Proceedings of the twelfth Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP) 2019, which was held in Philadelphia, USA, June 3rd , 2019.
Classification des S{é}ries Temporelles Incertaines par Transformation Shapelet
Authors: Michael Mbouopda, Engelbert Mephu Nguifo
Date: 2019-12-11 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time serie classification is used in a diverse range of domain such as meteorology, medicine and physics. It aims to classify chronological data. Many accurate approaches have been built during the last decade and shapelet transformation is one of them. However, none of these approaches does take data uncertainty into account. Using uncertainty propagation techiniques, we propose a new dissimilarity measure based on euclidean distance. We also show how to use this new measure to adapt shapelet transformation to uncertain time series classification. An experimental assessment of our contribution is done on some state of the art datasets.
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network
Authors: Seonghyeon Lee, Chanyoung Park, Hwanjo Yu
Date: 2019-11-26 00:00:00
Tasks: Network Embedding, Node Classification, Relation, Vocal Bursts Type Prediction

The goal of network embedding is to transform nodes in a network to a low-dimensional embedding vectors. Recently, heterogeneous network has shown to be effective in representing diverse information in data. However, heterogeneous network embedding suffers from the imbalance issue, i.e. the size of relation types (or the number of edges in the network regarding the type) is imbalanced. In this paper, we devise a new heterogeneous network embedding method, called BHIN2vec, which considers the balance among all relation types in a network. We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network. After splitting the skip-gram loss into multiple losses corresponding to different tasks, we propose a novel random-walk strategy to focus on the tasks with high loss values by considering the relative training ratio. Unlike previous random walk strategies, our proposed random-walk strategy generates training samples according to the relative training ratio among different tasks, which results in a balanced training for the node embedding. Our extensive experiments on node classification and recommendation demonstrate the superiority of BHIN2vec compared to the state-of-the-art methods. Also, based on the relative training ratio, we analyze how much each relation type is represented in the embedding space.
Early Detection of Research Trends
Authors: Angelo Antonio Salatino
Date: 2019-11-20 00:00:00
Tasks: Community Detection

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this dissertation, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the 'ancestors' of the new topic. Based on this understanding, we developed Augur, a novel approach to effectively detecting the emergence of new research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 timeframe and outperformed four alternative approaches in terms of both precision and recall.
Column generation for the discrete Unit Commitment problem with min-stop ramping constraints
Authors: Nicolas Dupin
Date: 2019-12-13 00:00:00

The discrete unit commitment problem with min-stop ramping constraints optimizes the daily production of thermal power plants (coal, gas, fuel units). For this problem, compact Integer Linear Programming (ILP) formulations have been designed to solve exactly small instances and heuristically real-size instances. This paper investigates whether Dantzig-Wolfe reformulation allows to improve the previous exact method and matheuristics. The extended ILP formulation is presented with the column generation algorithm to solve its linear relaxation. The experimental results show that the Dantzig-Wolfe reformulation does not improve the quality of the linear relaxation of the tightest compact ILP formulations. Computational experiments suggest also a conjecture which would explain such result: the compact ILP formulation of min-stop ramping constraints would be tight. Such results validate the quality of the exact methods and matheuristics based on compact ILP formulations previously designed.
Graphlets in Multiplex Networks
Authors: Tamara Dimitrova, Kristijan Petrovski, Ljupco Kocarev
Date: 2019-11-17 00:00:00

We develop graphlet analysis for multiplex networks and discuss how this analysis can be extended to multilayer and multilevel networks as well as to graphs with node and/or link categorical attributes. The analysis has been adapted for two typical examples of multiplexes: economic trade data represented as a 957-plex network and 75 social networks each represented as a 12-plex network. We show that wedges (open triads) occur more often in economic trade networks than in social networks, indicating the tendency of a country to produce/trade of a product in local structure of triads which are not closed. Moreover, our analysis provides evidence that the countries with small diversity tend to form correlated triangles. Wedges also appear in the social networks, however the dominant graphlets in social networks are triangles (closed triads). If a multiplex structure indicates a strong tie, the graphlet analysis provides another evidence for the concepts of strong/weak ties and structural holes. In contrast to Granovetter's seminal work on the strength of weak ties, in which it has been documented that the wedges with only strong ties are absent, here we show that for the analyzed 75 social networks, the wedges with only strong ties are not only present but also significantly correlated.
SeriesNet:A Generative Time Series Forecasting Model
Authors: Zhipeng Shen, Yuanming Zhang ∗, Jiawei Lu, Jun Xu, Gang Xiao
Date: 2018-08-23 00:00:00
Proceeding: 2018 International Joint Conference on Neural Networks (IJCNN) 2018 8
Tasks: model, Time Series, Time Series Analysis, Time Series Forecasting

Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.
Dilated Convolutional Neural Networks for Time Series Forecasting
Authors: Anastasia Borovykh ∗ Sander Bohte † Cornelis W. Oosterlee
Date: 2018-01-02 00:00:00
Proceeding: Journal of Computational Finance, Forthcoming 2018 1
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

We present a method for conditional time series forecasting based on an adaptation of the recent deep
convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that
allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning
is performed by applying multiple convolutional filters in parallel to separate time series which allows for
the fast processing of data and the exploitation of the correlation structure between the multivariate time
series. We test and analyze the performance of the convolutional network both unconditionally as well
as conditionally for financial time series forecasting using the S&P500, the volatility index, the CBOE
interest rate and several exchange rates and extensively compare it to the performance of the well-known
autoregressive model and a long-short term memory network. We show that a convolutional network is
well-suited for regression-type problems and is able to effectively learn dependencies in and between the
series without the need for long historical time series, is a time-efficient and easy to implement alternative
to recurrent-type networks and tends to outperform linear and recurrent models.
Projecting Temporal Properties, Events and Actions
Authors: Fern, Tim o
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

Temporal notions based on a finite set \textit{A} of properties are represented in strings, on which projections are defined that vary the granularity \textit{A}. The structure of properties in \textit{A} is elaborated to describe statives, events and actions, subject to a distinction in meaning (advocated by Levin and Rappaport Hovav) between what the lexicon prescribes and what a context of use supplies. The projections proposed are deployed as labels for records and record types amenable to finite-state methods.
A Semantic Annotation Scheme for Quantification
Authors: Harry Bunt
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

This paper describes in brief the proposal called {`}QuantML{'} which was accepted by the International Organisation for Standards (ISO) last February as a starting point for developing a standard for the interoperable annotation of quantification phenomena in natural language, as part of the ISO 24617 Semantic Annotation Framework. The proposal, firmly rooted in the theory of generalised quantifiers, neo-Davidsonian semantics, and DRT, covers a wide range of quantification phenomena. The QuantML scheme consists of (1) an abstract syntax which defines {`}annotation structures{'} as triples and other set-theoretic constructs; (b) a compositional semantics of annotation structures; (3) an XML representation of annotation structures.
A Semantic Ontology of Danish Adjectives
Authors: Eckhard Bick
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

This paper presents a semantic annotation scheme for Danish adjectives, focusing both on prototypical semantic content and semantic collocational restrictions on an adjective{'}s head noun. The core type set comprises about 110 categories ordered in a shallow hierarchy with 14 primary and 25 secondary umbrella categories. In addition, domain information and binary sentiment tags are provided, as well as VerbNet-derived frames and semantic roles for those adjectives governing arguments. The scheme has been almost fully implemented on the lexicon of the Danish VISL parser, DanGram, containing 14,000 adjectives. We discuss the annotation scheme and its applicational perspectives, and present a statistical breakdown and coverage evaluation for three Danish reference corpora.
Towards a Compositional Analysis of German Light Verb Constructions (LVCs) Combining Lexicalized Tree Adjoining Grammar (LTAG) with Frame Semantics
Authors: Jens Fleischhauer, Thomas Gamerschlag, Laura Kallmeyer, Simon Petitjean
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Semantic Composition

Complex predicates formed of a semantically {`}light{'} verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as {`}light verb constructions{'} (LVCs). In the paper, we present a case study of LVCs with the German posture verb stehen {`}stand{'}. In our account, we model the syntactic as well as semantic composition of such LVCs by combining Lexicalized Tree Adjoining Grammar (LTAG) with frames. Starting from the analysis of the literal uses of posture verbs, we show how the meaning components of the literal uses are systematically exploited in the interpretation of stehen-LVCs. The paper constitutes an important step towards a compositional and computational analysis of LVCs. We show that LTAG allows us to separate constructional from lexical meaning components and that frames enable elegant generalizations over event types and related constraints.
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs
Authors: Paula Czarnowska, Guy Emerson, Ann Copestake
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Learning Word Embeddings, Word Embeddings

Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.
Ambiguity in Explicit Discourse Connectives
Authors: Bonnie Webber, Rashmi Prasad, Alan Lee
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

Discourse connectives are known to be subject to both usage and sense ambiguity, as has already been discussed in the literature. But discourse connectives are no different from other linguistic expressions in being subject to other types of ambiguity as well. Four are illustrated and discussed here.