pwc-archive/papers-with-abstracts huggingface dataset:
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
Authors: Oindrila Saha, Rachana Sathish, Debdoot Sheet
Date: 2019-02-07 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Diabetic retinopathy (DR) is the most common form of diabetic eye disease.
Retinopathy can affect all diabetic patients and becomes particularly
dangerous, increasing the risk of blindness, if it is left untreated. The
success rate of its curability solemnly depends on diagnosis at an early stage.
The development of automated computer aided disease diagnosis tools could help
in faster detection of symptoms with a wider reach and reasonable cost. This
paper proposes a method for the automated segmentation of retinal lesions and
optic disk in fundus images using a deep fully convolutional neural network for
semantic segmentation. This trainable segmentation pipeline consists of an
encoder network, a corresponding decoder network followed by pixel-wise
classification to segment microaneurysms, hemorrhages, hard exudates, soft
exudates, optic disk from background. The network was trained using Binary
cross entropy criterion with Sigmoid as the last layer, while during an
additional SoftMax layer was used for boosting response of single class. The
performance of the proposed method is evaluated using sensitivity, positive
prediction value (PPV) and accuracy as the metrices. Further, the position of
the Optic disk is localised using the segmented output map.
High-performance stock index trading: making effective use of a deep LSTM neural network
Authors: Chariton Chalvatzis, Dimitrios Hristu-Varsakelis
Date: 2019-02-07 00:00:00

We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model's predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading strategy it is used with, and vise versa. This highlights the difficulty in developing models and strategies which are jointly optimal, but also points to avenues of investigation which are broader than prevailing approaches. Our LSTM model is structurally simple and generates predictions based on price observations over a modest number of past trading days. The model's architecture is tuned to promote profitability, as opposed to accuracy, under a strategy that does not trade simply based on whether the price is predicted to rise or fall, but rather takes advantage of the distribution of predicted returns, and the fact that a prediction's position within that distribution carries useful information about the expected profitability of a trade. The proposed model and trading strategy were tested on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russel 2000 stock indices, and achieved cumulative returns of 340%, 185%, 371% and 360%, respectively, over 2010-2018, far outperforming the benchmark buy-and-hold strategy as well as other recent efforts.
Dual-Reference Design for Holographic Coherent Diffraction Imaging
Authors: David A. Barmherzig, Ju Sun, Emmanuel J. Candès, T. J. Lane, Po-Nan Li
Date: 2019-02-07 00:00:00
Tasks: Retrieval

A new reference design is introduced for holographic coherent diffraction imaging. This consists in two references - "block" and "pinhole" shaped regions - placed adjacent to the imaging specimen. An efficient recovery algorithm is provided for the resulting holographic phase retrieval problem, which is based on solving a structured, overdetermined linear system. Analysis of the expected recovery error on noisy data, which is contaminated by Poisson shot noise, shows that this simple modification synergizes the individual references and hence leads to uniformly superior performance over single-reference schemes. Numerical experiments on simulated data confirm the theoretical prediction, and the proposed dual-reference scheme achieves a smaller recovery error than leading single-reference schemes.
Towards Automatic Concept-based Explanations
Authors: Amirata Ghorbani, James Wexler, James Zou, Been Kim
Date: 2019-02-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Feature Importance

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explanation methods provide explanations through feature importance scores, which identify features that are important for each individual input. However, how to systematically summarize and interpret such per sample feature importance scores itself is challenging. In this work, we propose principles and desiderata for \emph{concept} based explanation, which goes beyond per-sample features to identify higher-level human-understandable concepts that apply across the entire dataset. We develop a new algorithm, ACE, to automatically extract visual concepts. Our systematic experiments demonstrate that \alg discovers concepts that are human-meaningful, coherent and important for the neural network's predictions.
Efficient order picking methods in robotic mobile fulfillment systems
Authors: Lin Xie, Nils Thieme, Ruslan Krenzler, Hanyi Li
Date: 2019-01-31 00:00:00

Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing
system, which has received more attention recently, due to increasing growth in
the e-commerce sector. Instead of sending pickers to the inventory area to
search for and pick the ordered items, robots carry shelves (called "pods")
including ordered items from the inventory area to picking stations. In the
picking stations, human pickers put ordered items into totes; then these items
are transported by a conveyor to the packing stations. This type of warehousing
system relieves the human pickers and improves the picking process. In this
paper, we concentrate on decisions about the assignment of pods to stations and
orders to stations to fulfill picking for each incoming customer's order. In
previous research for an RMFS with multiple picking stations, these decisions
are made sequentially. Instead, we present a new integrated model. To improve
the system performance even more, we extend our model by splitting orders. This
means parts of an order are allowed to be picked at different stations. To the
best of the authors' knowledge, this is the first publication on split orders
in an RMFS. We analyze different performance metrics, such as pile-on,
pod-station visits, robot moving distance and order turn-over time. We compare
the results of our models in different instances with the sequential method in
our open-source simulation framework RAWSim-O.
Towards a Characterization of Explainable Systems
Authors: Dimitri Bohlender, Maximilian A. Köhl
Date: 2019-01-31 00:00:00
Tasks: General Classification

Building software-driven systems that are easily understood becomes a
challenge, with their ever-increasing complexity and autonomy. Accordingly,
recent research efforts strive to aid in designing explainable systems.
Nevertheless, a common notion of what it takes for a system to be explainable
is still missing. To address this problem, we propose a characterization of
explainable systems that consolidates existing research. By providing a unified
terminology, we lay a basis for the classification of both existing and future
research, and the formulation of precise requirements towards such systems.
Detecting Local Community Structures in Social Networks Using Concept Interestingness
Authors: Mohamed-Hamza Ibrahim, Rokia Missaoui, Abir Messaoudi
Date: 2019-02-05 00:00:00
Tasks: Community Detection

One key challenge in Social Network Analysis is to design an efficient and
accurate community detection procedure as a means to discover intrinsic
structures and extract relevant information. In this paper, we introduce a
novel strategy called (COIN), which exploits COncept INterestingness measures
to detect communities based on the concept lattice construction of the network.
Thus, unlike off-the-shelf community detection algorithms, COIN leverages
relevant conceptual characteristics inherited from Formal Concept Analysis to
discover substantial local structures. On the first stage of COIN, we extract
the formal concepts that capture all the cliques and bridges in the social
network. On the second stage, we use the stability index to remove noisy
bridges between communities and then percolate relevant adjacent cliques. Our
experiments on several real-world social networks show that COIN can quickly
detect communities more accurately than existing prominent algorithms such as
Edge betweenness, Fast greedy modularity, and Infomap.
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques
Authors: Sima Sharifirad, Borna Jafarpour, Stan Matwin
Date: 2019-01-28 00:00:00
Tasks: Sentiment Analysis

Online social platforms have been the battlefield of users with different
emotions and attitudes toward each other in recent years. While sexism has been
considered as a category of hateful speech in the literature, there is no
comprehensive definition and category of sexism attracting natural language
processing techniques. Categorizing sexism as either benevolent or hostile
sexism is so broad that it easily ignores the other categories of sexism on
social media. Sharifirad S and Matwin S 2018 proposed a well-defined category
of sexism including indirect harassment, information threat, sexual harassment
and physical harassment, inspired from social science for the purpose of
natural language processing techniques. In this article, we take advantage of a
newly released dataset in SemEval-2018 task1: Affect in tweets, to show the
type of emotion and intensity of emotion in each category. We train, test and
evaluate different classification methods on the SemEval- 2018 dataset and
choose the classifier with highest accuracy for testing on each category of
sexist tweets to know the mental state and the affectual state of the user who
tweets in each category. It is a nice avenue to explore because not all the
tweets are directly sexist and they carry different emotions from the users.
This is the first work experimenting on affect detection this in depth on
sexist tweets. Based on our best knowledge they are all new contributions to
the field; we are the first to demonstrate the power of such in-depth sentiment
analysis on the sexist tweets.
A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter
Authors: Laure Delisle, Alfredo Kalaitzis, Krzysztof Majewski, Archy de Berker, Milena Marin, Julien Cornebise
Date: 2019-01-31 00:00:00

We report the first, to the best of our knowledge, hand-in-hand collaboration
between human rights activists and machine learners, leveraging crowd-sourcing
to study online abuse against women on Twitter. On a technical front, we
carefully curate an unbiased yet low-variance dataset of labeled tweets,
analyze it to account for the variability of abuse perception, and establish
baselines, preparing it for release to community research efforts. On a social
impact front, this study provides the technical backbone for a media campaign
aimed at raising public and deciders' awareness and elevating the standards
expected from social media companies.
A semi-supervised approach to message stance classification
Authors: Georgios Giasemidis, Nikolaos Kaplis, Ioannis Agrafiotis, Jason R. C. Nurse
Date: 2019-01-29 00:00:00
Tasks: Classification, General Classification, Rumour Detection, Stance Classification

Social media communications are becoming increasingly prevalent; some useful,
some false, whether unwittingly or maliciously. An increasing number of rumours
daily flood the social networks. Determining their veracity in an autonomous
way is a very active and challenging field of research, with a variety of
methods proposed. However, most of the models rely on determining the
constituent messages' stance towards the rumour, a feature known as the "wisdom
of the crowd". Although several supervised machine-learning approaches have
been proposed to tackle the message stance classification problem, these have
numerous shortcomings. In this paper we argue that semi-supervised learning is
more effective than supervised models and use two graph-based methods to
demonstrate it. This is not only in terms of classification accuracy, but
equally important, in terms of speed and scalability. We use the Label
Propagation and Label Spreading algorithms and run experiments on a dataset of
72 rumours and hundreds of thousands messages collected from Twitter. We
compare our results on two available datasets to the state-of-the-art to
demonstrate our algorithms' performance regarding accuracy, speed and
scalability for real-time applications.
Measuring Long-term Impact of Ads on LinkedIn Feed
Authors: Jinyun Yan, Birjodh Tiwana, Souvik Ghosh, Haishan Liu, Shaunak Chatterjee
Date: 2019-01-29 00:00:00

Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting objectives. Hence, it is important to design a good Revenue-Engagement Tradeoff (RENT) mechanism to blend ads in the feed. In this paper, we design experiments to understand how members' behavior evolve over time given different ads experiences. These experiences vary on ads density, while the quality of ads (ensured by relevance models) is held constant. Our experiments have been conducted on randomized member buckets and we use two experimental designs to measure the short term and long term effects of the various treatments. Based on the first three months' data, we observe that the long term impact is at a much smaller scale than the short term impact in our application. Furthermore, we observe different member cohorts (based on user activity level) adapt and react differently over time.
Exact Inference with Latent Variables in an Arbitrary Domain
Authors: Chuyang Ke, Jean Honorio
Date: 2019-01-28 00:00:00

We analyze the necessary and sufficient conditions for exact inference of a latent model. In latent models, each entity is associated with a latent variable following some probability distribution. The challenging question we try to solve is: can we perform exact inference without observing the latent variables, even without knowing what the domain of the latent variables is? We show that exact inference can be achieved using a semidefinite programming (SDP) approach without knowing either the latent variables or their domain. Our analysis predicts the experimental correctness of SDP with high accuracy, showing the suitability of our focus on the Karush-Kuhn-Tucker (KKT) conditions and the spectrum of a properly defined matrix. As a byproduct of our analysis, we also provide concentration inequalities with dependence on latent variables, both for bounded moment generating functions as well as for the spectra of matrices. To the best of our knowledge, these results are novel and could be useful for many other problems.
Identifying and Analyzing Cryptocurrency Manipulations in Social Media
Authors: Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan
Date: 2019-02-04 00:00:00

Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as "pump and dump" scams---where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
High-resolution home location prediction from tweets using deep learning with dynamic structure
Authors: Meysam Ghaffari, Ashok Srinivasan, Xiuwen Liu
Date: 2019-02-03 00:00:00
Tasks: Disaster Response

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a deep-learning solution that uses a two-phase dynamic structure to deal with sparse and noisy social media data. In the first phase, high recall is achieved using a random forest, producing more balanced home location candidates. Then two deep neural networks are used to detect home locations with high accuracy. We obtained over 90% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach.
Sliced Wasserstein Auto-Encoders
Authors: Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder. We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified. In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation. We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.
Causal Discovery with Attention-Based Convolutional Neural Networks
Authors: Meike Nauta, Doina Bucur, Christin Seifert
Date: 2019-01-07 00:00:00
Proceeding: Machine Learning and Knowledge Extraction 2019 1
Tasks: Causal Discovery, Decision Making, Time Series, Time Series Analysis

Having insight into the causal associations in a complex system facilitates decision making, e.g., for medical treatments, urban infrastructure improvements or financial investments. The amount of observational data grows, which enables the discovery of causal relationships between variables from observation of their behaviour in time. Existing methods for causal discovery from time series data do not yet exploit the representational power of deep learning. We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data. TCDF uses attention-based convolutional neural networks combined with a causal validation step. By interpreting the internal parameters of the convolutional networks, TCDF can also discover the time delay between a cause and the occurrence of its effect. Our framework learns temporal causal graphs, which can include confounders and instantaneous effects. Experiments on financial and neuroscientific benchmarks show state-of-the-art performance of TCDF on discovering causal relationships in continuous time series data. Furthermore, we show that TCDF can circumstantially discover the presence of hidden confounders. Our broadly applicable framework can be used to gain novel insights into the causal dependencies in a complex system, which is important for reliable predictions, knowledge discovery and data-driven decision making.
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Authors: Sajad Mousavi, Fatemeh Afghah
Date: 2018-12-09 00:00:00
Proceeding: arXiv:1812.07421 2018 12
Tasks: Arrhythmia Detection, Classification, Heartbeat Classification, Sensitivity

Electrocardiogram (ECG) signal is a common and powerful tool to study heart
function and diagnose several abnormal arrhythmia. While there have been
remarkable improvements in cardiac arrhythmia classification methods, they
still cannot offer an acceptable performance in detecting different heart
conditions, especially when dealing with imbalanced datasets. In this paper, we
propose a solution to address this limitation of current classification
approaches by developing an automatic heartbeat classification method using
deep convolutional neural networks and sequence to sequence models. We
evaluated the proposed method on the MIT-BIH arrhythmia database, considering
the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The
evaluation results for both paradigms show that our method achieves the best
performance in the literature (a positive predictive value of 96.46% and
sensitivity of 100% for the category S, and a positive predictive value of
98.68% and sensitivity of 97.40% for the category F for the intra-patient
scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the
category S, and a positive predictive value of 99.50% and sensitivity of 99.94%
for the category V for the inter-patient scheme.)
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction
Authors: Natasha Jaques, Sara Taylor, Akane Sano, Rosalind W. Picard
Date: 2017-10-26 00:00:00
Tasks: Denoising

To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use. Such systems are likely to encounter frequent data loss, e.g. when a phone loses location access, or when a sensor is recharging. Lost data can handicap classifiers trained with all modalities present in the data. This paper describes a new technique for handling missing multimodal data using a specialized denoising autoencoder: the Multimodal Autoencoder (MMAE). Empirical results from over 200 participants and 5500 days of data demonstrate that the MMAE is able to predict the feature values from multiple missing modalities more accurately than reconstruction methods such as principal components analysis (PCA). We discuss several practical benefits of the MMAE's encoding and show that it can provide robust mood prediction even when up to three quarters of the data sources are lost.