pwc-archive/papers-with-abstracts huggingface dataset:
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
Authors: Jared Dunnmon, Swetava Ganguli, Darren Hau, Brooke Husic
Date: 2019-02-13 00:00:00
Tasks: Crop Yield Prediction, Sentiment Analysis, Sentiment Classification, Transfer Learning

The ability to obtain accurate food security metrics in developing areas
where relevant data can be sparse is critically important for policy makers
tasked with implementing food aid programs. As a result, a great deal of work
has been dedicated to predicting important food security metrics such as annual
crop yields using a variety of methods including simulation, remote sensing,
weather models, and human expert input. As a complement to existing techniques
in crop yield prediction, this work develops neural network models for
predicting the sentiment of Twitter feeds from farming communities.
Specifically, we investigate the potential of both direct learning on a small
dataset of agriculturally-relevant tweets and transfer learning from larger,
well-labeled sentiment datasets from other domains (e.g.~politics) to
accurately predict agricultural sentiment, which we hope would ultimately serve
as a useful crop yield predictor. We find that direct learning from small,
relevant datasets outperforms transfer learning from large, fully-labeled
datasets, that convolutional neural networks broadly outperform recurrent
neural networks on Twitter sentiment classification, and that these models
perform substantially less well on ternary sentiment problems characteristic of
practical settings than on binary problems often found in the literature.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
Authors: Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He
Date: 2019-04-25 00:00:00
Tasks: Click-Through Rate Prediction, Meta-Learning

Click-through rate (CTR) prediction has been one of the most central problems
in computational advertising. Lately, embedding techniques that produce
low-dimensional representations of ad IDs drastically improve CTR prediction
accuracies. However, such learning techniques are data demanding and work
poorly on new ads with little logging data, which is known as the cold-start
problem.
  In this paper, we aim to improve CTR predictions during both the cold-start
phase and the warm-up phase when a new ad is added to the candidate pool. We
propose Meta-Embedding, a meta-learning-based approach that learns to generate
desirable initial embeddings for new ad IDs. The proposed method trains an
embedding generator for new ad IDs by making use of previously learned ads
through gradient-based meta-learning. In other words, our method learns how to
learn better embeddings. When a new ad comes, the trained generator initializes
the embedding of its ID by feeding its contents and attributes. Next, the
generated embedding can speed up the model fitting during the warm-up phase
when a few labeled examples are available, compared to the existing
initialization methods.
  Experimental results on three real-world datasets showed that Meta-Embedding
can significantly improve both the cold-start and warm-up performances for six
existing CTR prediction models, ranging from lightweight models such as
Factorization Machines to complicated deep models such as PNN and DeepFM. All
of the above apply to conversion rate (CVR) predictions as well.
Machine Learning For Distributed Acoustic Sensors, Classic versus Image and Deep Neural Networks Approach
Authors: Mugdim Bublin
Date: 2019-04-25 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Event Detection

Distributed Acoustic Sensing (DAS) using fiber optic cables is a promising
new technology for pipeline monitoring and protection. In this work, we applied
and compared two approaches for event detection using DAS: Classic machine
learning approach and the approach based on image processing and deep learning.
Although with both approaches acceptable performance can be achieved, the
preliminary results show that image based deep learning is more promising
approach, offering six times lower event detection delay and twelve times lower
execution time.
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Authors: Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick
Date: 2019-04-25 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: CCG Supertagging, Language Modeling, Language Modelling, Natural Language Inference, Negation, Sentence

We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.
Zap Q-Learning for Optimal Stopping Time Problems
Authors: Shuhang Chen, Adithya M. Devraj, Ana Bušić, Sean P. Meyn
Date: 2019-04-25 00:00:00
Tasks: Q-Learning, Reinforcement Learning

The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$. We build on the dynamic programming approach taken by Tsitsikilis and Van Roy, wherein they propose a Q-learning algorithm to estimate the optimal state-action value function, which then defines an optimal stopping rule. We provide insights as to why the convergence rate of this algorithm can be slow, and propose a fast-converging alternative, the "Zap-Q-learning" algorithm, designed to achieve optimal rate of convergence. For the first time, we prove the convergence of the Zap-Q-learning algorithm under the assumption of linear function approximation setting. We use ODE analysis for the proof, and the optimal asymptotic variance property of the algorithm is reflected via fast convergence in a finance example.
Decision Forest: A Nonparametric Approach to Modeling Irrational Choice
Authors: Yi-Chun Chen, Velibor V. Mišić
Date: 2019-04-25 00:00:00

Customer behavior is often assumed to follow weak rationality, which implies that adding a product to an assortment will not increase the choice probability of another product in that assortment. However, an increasing amount of research has revealed that customers are not necessarily rational when making decisions. In this paper, we propose a new nonparametric choice model that relaxes this assumption and can model a wider range of customer behavior, such as decoy effects between products. In this model, each customer type is associated with a binary decision tree, which represents a decision process for making a purchase based on checking for the existence of specific products in the assortment. Together with a probability distribution over customer types, we show that the resulting model -- a decision forest -- is able to represent any customer choice model, including models that are inconsistent with weak rationality. We theoretically characterize the depth of the forest needed to fit a data set of historical assortments and prove that with high probability, a forest whose depth scales logarithmically in the number of assortments is sufficient to fit most data sets. We also propose two practical algorithms -- one based on column generation and one based on random sampling -- for estimating such models from data. Using synthetic data and real transaction data exhibiting non-rational behavior, we show that the model outperforms both rational and non-rational benchmark models in out-of-sample predictive ability.
Face Video Generation from a Single Image and Landmarks
Authors: Kritaphat Songsri-in, Stefanos Zafeiriou
Date: 2019-04-25 00:00:00
Tasks: Image-to-Image Translation, Translation, Video Generation

In this paper we are concerned with the challenging problem of producing a
full image sequence of a deformable face given only an image and generic facial
motions encoded by a set of sparse landmarks. To this end we build upon recent
breakthroughs in image-to-image translation such as pix2pix, CycleGAN and
StarGAN which learn Deep Convolutional Neural Networks (DCNNs) that learn to
map aligned pairs or images between different domains (i.e., having different
labels) and propose a new architecture which is not driven any more by labels
but by spatial maps, facial landmarks. In particular, we propose the MotionGAN
which transforms an input face image into a new one according to a heatmap of
target landmarks. We show that it is possible to create very realistic face
videos using a single image and a set of target landmarks. Furthermore, our
method can be used to edit a facial image with arbitrary motions according to
landmarks (e.g., expression, speech, etc.). This provides much more flexibility
to face editing, expression transfer, facial video creation, etc. than models
based on discrete expressions, audios or action units.
A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones
Authors: Qun Yang, Zhepu Xu, Saravanan Gurupackiam, Ping Wang
Date: 2019-04-25 00:00:00
Tasks: Layout Design

Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Due to the difficulty in vehicle microscopic behaviour data acquisition, traditional traffic characteristic analysis mainly focuses on macroscopic characteristics. With the development of data acquisition technology, it becomes much easier to obtain a large amount of microscopic behaviour data nowadays, which lays a good foundation for analysing the traffic characteristics from a new point of view. This paper puts forward a method for expressing and displaying the vehicle behaviour distribution in maintenance work zones. Using portable vehicle microscopic behaviour data acquisition devices, lots of data can be obtained. Based on this data, an endpoint detection technology is used to automatically extract the segments in behaviour data with violent fluctuations, which are segments where vehicles take behaviours such as acceleration or turning. Using the support vector machine classification method, the specific types of behaviours of the segments extracted can be identified, and together with a data combination method, a total of ten types of behaviours can be identified. Then the kernel density analysis is used to cluster different types of behaviours of all passing vehicles to show the distribution on maps. By this method, how vehicles travel through maintenance work zones, and how different vehicle behaviours distribute in maintenance work zones can be displayed intuitively on maps, which is a novel traffic characteristic and can shed light to maintenance work zone related researches such as safety assessment and design method.
Attention-based Transfer Learning for Brain-computer Interface
Authors: Chuanqi Tan, Fuchun Sun, Tao Kong, Bin Fang, Wenchang Zhang
Date: 2019-04-25 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification, Transfer Learning

Different functional areas of the human brain play different roles in brain
activity, which has not been paid sufficient research attention in the
brain-computer interface (BCI) field. This paper presents a new approach for
electroencephalography (EEG) classification that applies attention-based
transfer learning. Our approach considers the importance of different brain
functional areas to improve the accuracy of EEG classification, and provides an
additional way to automatically identify brain functional areas associated with
new activities without the involvement of a medical professional. We
demonstrate empirically that our approach out-performs state-of-the-art
approaches in the task of EEG classification, and the results of visualization
indicate that our approach can detect brain functional areas related to a
certain task.
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
Authors: Thanh-Toan Do, Khoa Le, Tuan Hoang, Huu Le, Tam V. Nguyen, Ngai-Man Cheung
Date: 2019-04-24 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Representing images by compact hash codes is an attractive approach for
large-scale content-based image retrieval. In most state-of-the-art
hashing-based image retrieval systems, for each image, local descriptors are
first aggregated as a global representation vector. This global vector is then
subjected to a hashing function to generate a binary hash code. In previous
works, the aggregating and the hashing processes are designed independently.
Hence these frameworks may generate suboptimal hash codes. In this paper, we
first propose a novel unsupervised hashing framework in which feature
aggregating and hashing are designed simultaneously and optimized jointly.
Specifically, our joint optimization generates aggregated representations that
can be better reconstructed by some binary codes. This leads to more
discriminative binary hash codes and improved retrieval accuracy. In addition,
the proposed method is flexible. It can be extended for supervised hashing.
When the data label is available, the framework can be adapted to learn binary
codes which minimize the reconstruction loss w.r.t. label vectors. Furthermore,
we also propose a fast version of the state-of-the-art hashing method Binary
Autoencoder to be used in our proposed frameworks. Extensive experiments on
benchmark datasets under various settings show that the proposed methods
outperform state-of-the-art unsupervised and supervised hashing methods.
Machine Learning Tips and Tricks for Power Line Communications
Authors: Andrea M. Tonello, Nunzio A. Letizia, Davide Righini, Francesco Marcuzzi
Date: 2019-04-24 00:00:00
Tasks: BIG-bench Machine Learning

A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.
Predicting Student Performance in an Educational Game Using a Hidden Markov Model
Authors: Manie Tadayon, Greg Pottie
Date: 2019-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

Contributions: Prior studies on education have mostly followed the model of
the cross sectional study, namely, examining the pretest and the posttest
scores. This paper shows that students' knowledge throughout the intervention
can be estimated by time series analysis using a hidden Markov model.
Background: Analyzing time series and the interaction between the students and
the game data can result in valuable information that cannot be gained by only
cross sectional studies of the exams. Research Questions: Can a hidden Markov
model be used to analyze the educational games? Can a hidden Markov model be
used to make a prediction of the students' performance? Methodology: The study
was conducted on (N=854) students who played the Save Patch game. Students were
divided into class 1 and class 2. Class 1 students are those who scored lower
in the test than class 2 students. The analysis is done by choosing various
features of the game as the observations. Findings: The state trajectories can
predict the students' performance accurately for both class 1 and class 2.
Asynchronous "Events" are Better For Motion Estimation
Authors: Yuhu Guo, Han Xiao, Yidong Chen, Xiaodong Shi
Date: 2019-04-24 00:00:00
Tasks: Motion Estimation

Event-based camera is a bio-inspired vision sensor that records intensity
changes (called event) asynchronously in each pixel. As an instance of
event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a
standard camera and an event-based camera. However, traditional models could
not deal with the event stream asynchronously. To analyze the event stream
asynchronously, most existing approaches accumulate events within a certain
time interval and treat the accumulated events as a synchronous frame, which
wastes the intensity change information and weakens the advantages of DAVIS.
Therefore, in this paper, we present the first neural asynchronous approach to
process event stream for event-based camera. Our method asynchronously extracts
dynamic information from events by leveraging previous motion and critical
features of gray-scale frames. To our best knowledge, this is the first neural
asynchronous method to analyze event stream through a novel deep neural
network. Extensive experiments demonstrate that our proposed model achieves
remarkable improvements against the state-of-the-art baselines.
Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate
Authors: Hiroki Horino, Hirofumi Nonaka, Elisa Claire Alemán Carreón, Toru Hiraoka
Date: 2019-04-23 00:00:00
Tasks: feature selection

In recent years, data posted about real estate on the Internet is currently
increasing. In this study, in order to analyze user needs for real estate, we
focus on "Mansion Community" which is a Japanese bulletin board system
(hereinafter referred to as BBS) about Japanese real estate. In our study,
extraction of keywords is performed based on the calculation of the entropy
value of each word, and we used them as features in a machine learning
classifier to analyze 6 million posts at "Mansion Community". As a result, we
achieved a 0.69 F-measure and found that the customers are particularly
concerned about the facility of apartment, access, and price of an apartment.
Will this Course Increase or Decrease Your GPA? Towards Grade-aware Course Recommendation
Authors: Sara Morsy, George Karypis
Date: 2019-04-22 00:00:00
Tasks: Representation Learning

In order to help undergraduate students towards successfully completing their
degrees, developing tools that can assist students during the course selection
process is a significant task in the education domain. The optimal set of
courses for each student should include courses that help him/her graduate in a
timely fashion and for which he/she is well-prepared for so as to get a good
grade in. To this end, we propose two different grade-aware course
recommendation approaches to recommend to each student his/her optimal set of
courses. The first approach ranks the courses by using an objective function
that differentiates between courses that are expected to increase or decrease a
student's GPA. The second approach combines the grades predicted by grade
prediction methods with the rankings produced by course recommendation methods
to improve the final course rankings. To obtain the course rankings in the
first approach, we adapt two widely-used representation learning techniques to
learn the optimal temporal ordering between courses. Our experiments on a large
dataset obtained from the University of Minnesota that includes students from
23 different majors show that the grade-aware course recommendation methods can
do better on recommending more courses in which the students are expected to
perform well and recommending fewer courses in which they are expected not to
perform well in than grade-unaware course recommendation methods.
Sparse Neural Attentive Knowledge-based Models for Grade Prediction
Authors: Sara Morsy, George Karypis
Date: 2019-04-22 00:00:00

Grade prediction for future courses not yet taken by students is important as
it can help them and their advisers during the process of course selection as
well as for designing personalized degree plans and modifying them based on
their performance. One of the successful approaches for accurately predicting a
student's grades in future courses is Cumulative Knowledge-based Regression
Models (CKRM). CKRM learns shallow linear models that predict a student's
grades as the similarity between his/her knowledge state and the target course.
A student's knowledge state is built by linearly accumulating the learned
provided knowledge components of the courses he/she has taken in the past,
weighted by his/her grades in them. However, not all the prior courses
contribute equally to the target course. In this paper, we propose a novel
Neural Attentive Knowledge-based model (NAK) that learns the importance of each
historical course in predicting the grade of a target course. Compared to CKRM
and other competing approaches, our experiments on a large real-world dataset
consisting of $\sim$1.5 grades show the effectiveness of the proposed NAK model
in accurately predicting the students' grades. Moreover, the attention weights
learned by the model can be helpful in better designing their degree plans.
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
Authors: Zulifqar Ali, Ijaz Hussain, Muhammad Faisal, Hafiza Mamona Nazir, Tajammal Hussain, Muhammad Yousaf Shad, Alaa Mohamd Shoukry, Showkat Hussain Gani
Date: 2019-04-17 00:00:00
Tasks: Decision Making, Management, Time Series, Time Series Analysis

These days human beings are facing many environmental challenges due to
frequently occurring drought hazards. It may have an effect on the countrys
environment, the community, and industries. Several adverse impacts of drought
hazard are continued in Pakistan, including other hazards. However, early
measurement and detection of drought can provide guidance to water resources
management for employing drought mitigation policies. In this paper, we used a
multilayer perceptron neural network (MLPNN) algorithm for drought forecasting.
We applied and tested MLPNN algorithm on monthly time series data of
Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen
climatological stations located in Northern Area and KPK (Pakistan). We found
that MLPNN has potential capability for SPEI drought forecasting based on
performance measures (i.e., Mean Average Error (MAE), the coefficient of
correlation R, and Root Mean Square Error (RMSE). Water resources and
management planner can take necessary action in advance (e.g., in water
scarcity areas) by using MLPNN model as part of their decision making.