pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone
Authors: Quang Minh Ha, Yves Deville, Quang Dung Pham, Minh Hoàng Hà
Date: 2018-12-21 00:00:00
Tasks: Diversity, Management, Traveling Salesman Problem

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in
which a truck and drone are used to deliver parcels to customers. The objective
of this problem is to either minimize the total operational cost (min-cost
TSP-D) or minimize the completion time for the truck and drone (min-time
TSP-D). This problem has gained a lot of attention in the last few years since
it is matched with the recent trends in a new delivery method among logistics
companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic
population management and adaptive diversity control based on a split
algorithm, problem-tailored crossover and local search operators, a new restore
method to advance the convergence and an adaptive penalization mechanism to
dynamically balance the search between feasible/infeasible solutions. The
computational results show that the proposed algorithm outperforms existing
methods in terms of solution quality and improves best known solutions found in
the literature. Moreover, various analyses on the impacts of crossover choice
and heuristic components have been conducted to analysis further their
sensitivity to the performance of our method.
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search
Authors: Grigor Aslanyan, Utkarsh Porwal
Date: 2018-12-21 00:00:00
Tasks: Learning-To-Rank, Position

The Unbiased Learning-to-Rank framework has been recently proposed as a general approach to systematically remove biases, such as position bias, from learning-to-rank models. The method takes two steps - estimating click propensities and using them to train unbiased models. Most common methods proposed in the literature for estimating propensities involve some degree of intervention in the live search engine. An alternative approach proposed recently uses an Expectation Maximization (EM) algorithm to estimate propensities by using ranking features for estimating relevances. In this work we propose a novel method to directly estimate propensities which does not use any intervention in live search or rely on modeling relevance. Rather, we take advantage of the fact that the same query-document pair may naturally change ranks over time. This typically occurs for eCommerce search because of change of popularity of items over time, existence of time dependent ranking features, or addition or removal of items to the index (an item getting sold or a new item being listed). However, our method is general and can be applied to any search engine for which the rank of the same document may naturally change over time for the same query. We derive a simple likelihood function that depends on propensities only, and by maximizing the likelihood we are able to get estimates of the propensities. We apply this method to eBay search data to estimate click propensities for web and mobile search and compare these with estimates using the EM method. We also use simulated data to show that the method gives reliable estimates of the "true" simulated propensities. Finally, we train an unbiased learning-to-rank model for eBay search using the estimated propensities and show that it outperforms both baselines - one without position bias correction and one with position bias correction using the EM method.
An Empirical Analysis of Deep Audio-Visual Models for Speech Recognition
Authors: Devesh Walawalkar, Yihui He, Rohit Pillai
Date: 2018-12-21 00:00:00
Tasks: Lip Reading, Sensitivity, speech-recognition, Speech Recognition

In this project, we worked on speech recognition, specifically predicting
individual words based on both the video frames and audio. Empowered by
convolutional neural networks, the recent speech recognition and lip reading
models are comparable to human level performance. We re-implemented and made
derivations of the state-of-the-art model. Then, we conducted rich experiments
including the effectiveness of attention mechanism, more accurate residual
network as the backbone with pre-trained weights and the sensitivity of our
model with respect to audio input with/without noise.
Cluster Loss for Person Re-Identification
Authors: Doney Alex, Zishan Sami, Sumandeep Banerjee, Subrat Panda
Date: 2018-12-21 00:00:00
Tasks: Clustering, Person Re-Identification, Triplet

Person re-identification (ReID) is an important problem in computer vision,
especially for video surveillance applications. The problem focuses on
identifying people across different cameras or across different frames of the
same camera. The main challenge lies in identifying the similarity of the same
person against large appearance and structure variations, while differentiating
between individuals. Recently, deep learning networks with triplet loss have
become a common framework for person ReID. However, triplet loss focuses on
obtaining correct orders on the training set. We demonstrate that it performs
inferior in a clustering task. In this paper, we design a cluster loss, which
can lead to the model output with a larger inter-class variation and a smaller
intra-class variation compared to the triplet loss. As a result, our model has
a better generalization ability and can achieve higher accuracy on the test set
especially for a clustering task. We also introduce a batch hard training
mechanism for improving the results and faster convergence of training.
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Authors: Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel GM Moons, Gary S Collins, John P. A. Ioannidis, Chris Holmes, Harry Hemingway
Date: 2018-12-21 00:00:00
Tasks: Ethics

Machine learning (ML), artificial intelligence (AI) and other modern
statistical methods are providing new opportunities to operationalize
previously untapped and rapidly growing sources of data for patient benefit.
Whilst there is a lot of promising research currently being undertaken, the
literature as a whole lacks: transparency; clear reporting to facilitate
replicability; exploration for potential ethical concerns; and, clear
demonstrations of effectiveness. There are many reasons for why these issues
exist, but one of the most important that we provide a preliminary solution for
here is the current lack of ML/AI- specific best practice guidance. Although
there is no consensus on what best practice looks in this field, we believe
that interdisciplinary groups pursuing research and impact projects in the
ML/AI for health domain would benefit from answering a series of questions
based on the important issues that exist when undertaking work of this nature.
Here we present 20 questions that span the entire project life cycle, from
inception, data analysis, and model evaluation, to implementation, as a means
to facilitate project planning and post-hoc (structured) independent
evaluation. By beginning to answer these questions in different settings, we
can start to understand what constitutes a good answer, and we expect that the
resulting discussion will be central to developing an international consensus
framework for transparent, replicable, ethical and effective research in
artificial intelligence (AI-TREE) for health.
Multiple topic identification in telephone conversations
Authors: Xavier Bost, Marc El Bèze, Renato de Mori
Date: 2018-12-21 00:00:00
Tasks: Document Classification

This paper deals with the automatic analysis of conversations between a
customer and an agent in a call centre of a customer care service. The purpose
of the analysis is to hypothesize themes about problems and complaints
discussed in the conversation. Themes are defined by the application
documentation topics. A conversation may contain mentions that are irrelevant
for the application purpose and multiple themes whose mentions may be
interleaved portions of a conversation that cannot be well defined. Two methods
are proposed for multiple theme hypothesization. One of them is based on a
cosine similarity measure using a bag of features extracted from the entire
conversation. The other method introduces the concept of thematic density
distributed around specific word positions in a conversation. In addition to
automatically selected words, word bi-grams with possible gaps between
successive words are also considered and selected. Experimental results show
that the results obtained with the proposed methods outperform the results
obtained with support vector machines on the same data. Furthermore, using the
theme skeleton of a conversation from which thematic densities are derived, it
will be possible to extract components of an automatic conversation report to
be used for improving the service performance. Index Terms: multi-topic audio
document classification, hu-man/human conversation analysis, speech analytics,
distance bigrams
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact
Authors: Maria De-Arteaga, Amanda Coston, William Herlands
Date: 2018-12-21 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of NeurIPS 2018 Workshop on Machine Learning for the
Developing World: Achieving Sustainable Impact, held in Montreal, Canada on
December 8, 2018
Transfer Learning in Astronomy: A New Machine-Learning Paradigm
Authors: Ricardo Vilalta
Date: 2018-12-20 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, Transfer Learning

The widespread dissemination of machine learning tools in science,
particularly in astronomy, has revealed the limitation of working with simple
single-task scenarios in which any task in need of a predictive model is looked
in isolation, and ignores the existence of other similar tasks. In contrast, a
new generation of techniques is emerging where predictive models can take
advantage of previous experience to leverage information from similar tasks.
The new emerging area is referred to as transfer learning. In this paper, I
briefly describe the motivation behind the use of transfer learning techniques,
and explain how such techniques can be used to solve popular problems in
astronomy. As an example, a prevalent problem in astronomy is to estimate the
class of an object (e.g., Supernova Ia) using a generation of photometric
light-curve datasets where data abounds, but class labels are scarce; such
analysis can benefit from spectroscopic data where class labels are known with
high confidence, but the data sample is small. Transfer learning provides a
robust and practical solution to leverage information from one domain to
improve the accuracy of a model built on a different domain. In the example
above, transfer learning would look to overcome the difficulty in the
compatibility of models between spectroscopic data and photometric data, since
data properties such as size, class priors, and underlying distributions, are
all expected to be significantly different.
Hierarchical Genetic Algorithms with evolving objective functions
Authors: Harshavardhan Kamarthi, Kousik Krishnan
Date: 2018-12-01 00:00:00
Tasks: regression

We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions. We solve a variant of Travelling Salesman Problem called \texttt{soft-TSP} and show that when the constraints on the overall objective function are changed the algorithm adapts to churn out solutions for the changed objective. We use this idea to speed up learning by systematically altering the constraints to find a more globally optimal solution. We also use this framework to solve polynomial regression where the actual objective function is unknown but searching over space of available objective functions yields a good approximate solution.
A multiple criteria methodology for priority based portfolio selection
Authors: Maria Barbati, Josè Rui Figueira, Salvatore Greco, Alessio Ishizaka, Simona Panaro
Date: 2018-12-13 00:00:00

This paper presents a new methodology that combines a multiple criteria sorting or ranking method with a project portfolio selection procedure. The multicriteria method permits to compare projects in terms of their priority assessed on the basis of a set of both qualitative and quantitative criteria. Then, a feasible set of projects, i.e. a portfolio, is selected according to the priority defined by the multiple criteria method. In addition, the portfolio must satisfy a set of resources constraints, e.g. budget available, as well as some logical constraints, e.g. related to projects to be selected together or projects mutually exclusive. The proposed portfolio selection methodology can be applied in different contexts. We present an application in the urban planning domain where our approach allows to select a set of urban projects on the basis of their priority, budgetary constraints and urban policy requirements. Given the increasing interest of historical cities to reuse their cultural heritage, we applied and tested our methodology in this context. In particular, we show how the methodology can support the prioritization of the interventions on buildings with some historical value in the historic city center of Naples (Italy), taking into account several points of view.
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
Authors: Zi Yin, Vin Sachidananda, Balaji Prabhakar
Date: 2018-12-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering, Domain Adaptation, Word Embeddings

Language is dynamic, constantly evolving and adapting with respect to time,
domain or topic. The adaptability of language is an active research area, where
researchers discover social, cultural and domain-specific changes in language
using distributional tools such as word embeddings. In this paper, we introduce
the global anchor method for detecting corpus-level language shifts. We show
both theoretically and empirically that the global anchor method is equivalent
to the alignment method, a widely-used method for comparing word embeddings, in
terms of detecting corpus-level language shifts. Despite their equivalence in
terms of detection abilities, we demonstrate that the global anchor method is
superior in terms of applicability as it can compare embeddings of different
dimensionalities. Furthermore, the global anchor method has implementation and
parallelization advantages. We show that the global anchor method reveals fine
structures in the evolution of language and domain adaptation. When combined
with the graph Laplacian technique, the global anchor method recovers the
evolution trajectory and domain clustering of disparate text corpora.
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Authors: Renato Stoffalette João, Pavlos Fafalios, Stefan Dietze
Date: 2018-12-13 00:00:00
Tasks: Articles, Entity Linking

Entity Linking (EL) is the task of automatically identifying entity mentions
in a piece of text and resolving them to a corresponding entity in a reference
knowledge base like Wikipedia. There is a large number of EL tools available
for different types of documents and domains, yet EL remains a challenging task
where the lack of precision on particularly ambiguous mentions often spoils the
usefulness of automated disambiguation results in real applications. A priori
approximations of the difficulty to link a particular entity mention can
facilitate flagging of critical cases as part of semi-automated EL systems,
while detecting latent factors that affect the EL performance, like
corpus-specific features, can provide insights on how to improve a system based
on the special characteristics of the underlying corpus. In this paper, we
first introduce a consensus-based method to generate difficulty labels for
entity mentions on arbitrary corpora. The difficulty labels are then exploited
as training data for a supervised classification task able to predict the EL
difficulty of entity mentions using a variety of features. Experiments over a
corpus of news articles show that EL difficulty can be estimated with high
accuracy, revealing also latent features that affect EL performance. Finally,
evaluation results demonstrate the effectiveness of the proposed method to
inform semi-automated EL pipelines.
Detecting weak and strong Islamophobic hate speech on social media
Authors: Bertie Vidgen, Taha Yasseri
Date: 2018-12-12 00:00:00
Tasks: Word Embeddings

Islamophobic hate speech on social media inflicts considerable harm on both
targeted individuals and wider society, and also risks reputational damage for
the host platforms. Accordingly, there is a pressing need for robust tools to
detect and classify Islamophobic hate speech at scale. Previous research has
largely approached the detection of Islamophobic hate speech on social media as
a binary task. However, the varied nature of Islamophobia means that this is
often inappropriate for both theoretically-informed social science and
effectively monitoring social media. Drawing on in-depth conceptual work we
build a multi-class classifier which distinguishes between non-Islamophobic,
weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and
balanced accuracy is 83%. We apply the classifier to a dataset of 109,488
tweets produced by far right Twitter accounts during 2017. Whilst most tweets
are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963
tweets) than strong (14,895 tweets). Our main input feature is a gloVe word
embeddings model trained on a newly collected corpus of 140 million tweets. It
outperforms a generic word embeddings model by 5.9 percentage points,
demonstrating the importan4ce of context. Unexpectedly, we also find that a
one-against-one multi class SVM outperforms a deep learning algorithm.
Word Embedding based on Low-Rank Doubly Stochastic Matrix Decomposition
Authors: Denis Sedov, Zhirong Yang
Date: 2018-12-12 00:00:00

Word embedding, which encodes words into vectors, is an important starting
point in natural language processing and commonly used in many text-based
machine learning tasks. However, in most current word embedding approaches, the
similarity in embedding space is not optimized in the learning. In this paper
we propose a novel neighbor embedding method which directly learns an embedding
simplex where the similarities between the mapped words are optimal in terms of
minimal discrepancy to the input neighborhoods. Our method is built upon
two-step random walks between words via topics and thus able to better reveal
the topics among the words. Experiment results indicate that our method,
compared with another existing word embedding approach, is more favorable for
various queries.
Hyperbolic Deep Learning for Chinese Natural Language Understanding
Authors: Marko Valentin Micic, Hugo Chu
Date: 2018-12-11 00:00:00
Tasks: Chinese Word Segmentation, Deep Learning, Natural Language Understanding

Recently hyperbolic geometry has proven to be effective in building
embeddings that encode hierarchical and entailment information. This makes it
particularly suited to modelling the complex asymmetrical relationships between
Chinese characters and words. In this paper we first train a large scale
hyperboloid skip-gram model on a Chinese corpus, then apply the character
embeddings to a downstream hyperbolic Transformer model derived from the
principles of gyrovector space for Poincare disk model. In our experiments the
character-based Transformer outperformed its word-based Euclidean equivalent.
To the best of our knowledge, this is the first time in Chinese NLP that a
character-based model outperformed its word-based counterpart, allowing the
circumvention of the challenging and domain-dependent task of Chinese Word
Segmentation (CWS).
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
Authors: Siddique Latif, Adnan Qayyum, Muhammad Usman, Junaid Qadir
Date: 2018-12-15 00:00:00
Tasks: Cross-corpus, Emotion Recognition, Speech Emotion Recognition

Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU---the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
Authors: Navid Rekabsaz, Robert West, James Henderson, Allan Hanbury
Date: 2018-12-13 00:00:00
Tasks: Word Embeddings

Text corpora are widely used resources for measuring societal biases and stereotypes. The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as genders). In this study, we show that, depending on what one aims to quantify as bias, this commonly-used approach can introduce non-relevant concepts into bias measurement. We propose an alternative approach to bias measurement utilizing the smoothed first-order co-occurrence relations between the word and the representative concept words, which we derive by reconstructing the co-occurrence estimates inherent in word embedding models. We compare these approaches by conducting several experiments on the scenario of measuring gender bias of occupational words, according to an English Wikipedia corpus. Our experiments show higher correlations of the measured gender bias with the actual gender bias statistics of the U.S. job market - on two collections and with a variety of word embedding models - using the first-order approach in comparison with the vector similarity-based approaches. The first-order approach also suggests a more severe bias towards female in a few specific occupations than the other approaches.
Urban-Rural Environmental Gradient in a Developing City: Testing ENVI GIS Functionality
Authors: Polina Lemenkova
Date: 2018-12-06 00:00:00

The research performs urban ecosystem analysis supported by ENVI GIS by
integrated studies on land cover types and geospatial modeling of Taipei city.
The paper deals with the role of anthropogenic pressure on the structure of the
landscape and change of land cover types. Methods included assessment of the
impact from anthropogenic activities on the natural ecosystems, evaluation of
the rate and scale of landscape dynamics using remote sensing data and GIS. The
research aims to assist environmentalists and city planners to evaluate
strategies for specific objectives of urban development in Taiwan, China.