pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces
Authors: Kuan-Jung Chiang, Chun-Shu Wei, Masaki Nakanishi, Tzyy-Ping Jung
Date: 2018-10-05 00:00:00
Tasks: SSVEP, Subject Transfer, Transfer Learning

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces
(BCIs) have shown its robustness in facilitating high-efficiency communication.
State-of-the-art training-based SSVEP decoding methods such as extended
Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA)
are the major players that elevate the efficiency of the SSVEP-based BCIs
through a calibration process. However, due to notable human variability across
individuals and within individuals over time, calibration (training) data
collection is non-negligible and often laborious and time-consuming,
deteriorating the practicality of SSVEP BCIs in a real-world context. This
study aims to develop a cross-subject transferring approach to reduce the need
for collecting training data from a test user with a newly proposed
least-squares transformation (LST) method. Study results show the capability of
the LST in reducing the number of training templates required for a 40-class
SSVEP BCI. The LST method may lead to numerous real-world applications using
near-zero-training/plug-and-play high-speed SSVEP BCIs.
Training Complex Models with Multi-Task Weak Supervision
Authors: Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré
Date: 2018-10-05 00:00:00
Tasks: Matrix Completion, Natural Language Inference, Paraphrase Identification, Semantic Textual Similarity, Sentiment Analysis

Snorkel MeTaL: A framework for training models with multi-task weak supervision
Scaling Submodular Optimization Approaches for Control Applications in Networked Systems
Authors: Arun V. Sathanur
Date: 2018-10-05 00:00:00

Often times, in many design problems, there is a need to select a small set
of informative or representative elements from a large ground set of entities
in an optimal fashion. Submodular optimization that provides for a formal way
to solve such problems, has recently received significant attention from the
controls community where such subset selection problems are abound. However,
scaling these approaches to large systems can be challenging because of the
high computational complexity of the overall flow, in-part due to the
high-complexity compute-oracles used to determine the objective function
values. In this work, we explore a well-known paradigm, namely leader-selection
in a multi-agent networked environment to illustrate strategies for scalable
submodular optimization. We study the performance of the state-of-the-art
stochastic and distributed greedy algorithms as well as explore techniques that
accelerate the computation oracles within the optimization loop. We finally
present results combining accelerated greedy algorithms with accelerated
computation oracles and demonstrate significant speedups with little loss of
optimality when compared to the baseline ordinary greedy algorithm.
A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
Authors: L. A. Marcomini, A. L. Cunha
Date: 2018-10-05 00:00:00
Tasks: All

The objective of this paper is to compare the performance of three
background-modeling algorithms in segmenting and detecting vehicles in highway
traffic videos. All algorithms are available in OpenCV and were all coded in
Python. We analyzed seven videos, totaling 2 hours of recording. To compare the
algorithms, we created 35 ground-truth images, five from each video, and we
used three different metrics: accuracy rate, precision rate, and processing
time. By using accuracy and precision, we aim to identify how well the
algorithms perform in detection and segmentation, while using the processing
time to evaluate the impact on the computational system. Results indicate that
all three algorithms had more than 90% of precision rate, while obtaining an
average of 80% on accuracy. The algorithm with the lowest impact on processing
time allowed the computation of 60 frames per second.
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas
Authors: Amanpreet Singh, Sharan Agrawal
Date: 2018-10-05 00:00:00
Tasks: Image Generation, Sentence, Sentence Embeddings, Text to Image Generation, Text-to-Image Generation

We propose a new recurrent generative model for generating images from text
captions while attending on specific parts of text captions. Our model creates
images by incrementally adding patches on a "canvas" while attending on words
from text caption at each timestep. Finally, the canvas is passed through an
upscaling network to generate images. We also introduce a new method for
generating visual-semantic sentence embeddings based on self-attention over
text. We compare our model's generated images with those generated Reed et.
al.'s model and show that our model is a stronger baseline for text to image
generation tasks.
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
Authors: Yong Luo, Huaizheng Zhang, Yongjie Wang, Yonggang We, Xinwen Zhang
Date: 2018-10-05 00:00:00
Tasks: Management, Triplet

Recruitment of appropriate people for certain positions is critical for any
companies or organizations. Manually screening to select appropriate candidates
from large amounts of resumes can be exhausted and time-consuming. However,
there is no public tool that can be directly used for automatic resume quality
assessment (RQA). This motivates us to develop a method for automatic RQA.
Since there is also no public dataset for model training and evaluation, we
build a dataset for RQA by collecting around 10K resumes, which are provided by
a private resume management company. By investigating the dataset, we identify
some factors or features that could be useful to discriminate good resumes from
bad ones, e.g., the consistency between different parts of a resume. Then a
neural-network model is designed to predict the quality of each resume, where
some text processing techniques are incorporated. To deal with the label
deficiency issue in the dataset, we propose several variants of the model by
either utilizing the pair/triplet-based loss, or introducing some
semi-supervised learning technique to make use of the abundant unlabeled data.
Both the presented baseline model and its variants are general and easy to
implement. Various popular criteria including the receiver operating
characteristic (ROC) curve, F-measure and ranking-based average precision (AP)
are adopted for model evaluation. We compare the different variants with our
baseline model. Since there is no public algorithm for RQA, we further compare
our results with those obtained from a website that can score a resume.
Experimental results in terms of different criteria demonstrate the
effectiveness of the proposed method. We foresee that our approach would
transform the way of future human resources management.
Text Classification of the Precursory Accelerating Seismicity Corpus: Inference on some Theoretical Trends in Earthquake Predictability Research from 1988 to 2018
Authors: Arnaud Mignan
Date: 2018-10-05 00:00:00
Tasks: Articles, Binary Classification, General Classification, text-classification, Text Classification

Text analytics based on supervised machine learning classifiers has shown
great promise in a multitude of domains, but has yet to be applied to
Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors,
Support Vector Machines, and Random Forests) on a seismological corpus of 100
articles related to the topic of precursory accelerating seismicity, spanning
from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor
whether explained by critical processes (i.e., cascade triggering) or by other
processes (such as signature of main fault loading). We investigate rather the
classification process can be automatized to help analyze larger corpora in
order to better understand trends in earthquake predictability research. We
find that the Naive Bayes model performs best, in agreement with the machine
learning literature for the case of small datasets, with cross-validation
accuracies of 86% for binary classification. For a refined multiclass
classification ('non-critical process' < 'agnostic' < 'critical process
assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy.
Prediction on a dozen of articles published since 2011 shows however a weak
generalization with a F1-score of 60%, only slightly better than a random
classifier, which can be explained by a change of authorship and use of
different terminologies. Yet, the model shows F1-scores greater than 80% for
the two multiclass extremes ('non-critical process' versus 'critical process
demonstrated') while it falls to random classifier results (around 25%) for
papers labelled 'agnostic' or 'critical process assumed'. Those results are
encouraging in view of the small size of the corpus and of the high degree of
abstraction of the labelling. Domain knowledge engineering remains essential
but can be made transparent by an investigation of Naive Bayes keyword
posterior probabilities.
Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding
Authors: Xu Han, Hongsu Wang, Sanqian Zhang, Qunchao Fu, Jun S. Liu
Date: 2018-10-05 00:00:00
Tasks: Segmentation, Sentence, Sentence segmentation

In this paper, we develop a low than character feature embedding called
radical embedding, and apply it on LSTM model for sentence segmentation of pre
modern Chinese texts. The datasets includes over 150 classical Chinese books
from 3 different dynasties and contains different literary styles. LSTM CRF
model is a state of art method for the sequence labeling problem. Our new model
adds a component of radical embedding, which leads to improved performances.
Experimental results based on the aforementioned Chinese books demonstrates a
better accuracy than earlier methods on sentence segmentation, especial in Tang
Epitaph texts.
Statistical Optimality of Interpolated Nearest Neighbor Algorithms
Authors: Yue Xing, Qifan Song, Guang Cheng
Date: 2018-10-05 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

In the era of deep learning, understanding over-fitting phenomenon becomes
increasingly important. It is observed that carefully designed deep neural
networks achieve small testing error even when the training error is close to
zero. One possible explanation is that for many modern machine learning
algorithms, over-fitting can greatly reduce the estimation bias, while not
increasing the estimation variance too much. To illustrate the above idea, we
prove that the proposed interpolated nearest neighbor algorithm achieves the
minimax optimal rate in both regression and classification regimes, and observe
that they are empirically better than the traditional $k$ nearest neighbor
method in some cases.
Wide and Deep Learning for Peer-to-Peer Lending
Authors: Kaveh Bastani, Elham Asgari, Hamed Namavari
Date: 2018-10-05 00:00:00
Tasks: Deep Learning, Memorization, Prediction

This paper proposes a two-stage scoring approach to help lenders decide their
fund allocations in the peer-to-peer (P2P) lending market. The existing scoring
approaches focus on only either probability of default (PD) prediction, known
as credit scoring, or profitability prediction, known as profit scoring, to
identify the best loans for investment. Credit scoring fails to deliver the
main need of lenders on how much profit they may obtain through their
investment. On the other hand, profit scoring can satisfy that need by
predicting the investment profitability. However, profit scoring completely
ignores the class imbalance problem where most of the past loans are
non-default. Consequently, ignorance of the class imbalance problem
significantly affects the accuracy of profitability prediction. Our proposed
two-stage scoring approach is an integration of credit scoring and profit
scoring to address the above challenges. More specifically, stage 1 is designed
as credit scoring to identify non-default loans while the imbalanced nature of
loan status is considered in PD prediction. The loans identified as non-default
are then moved to stage 2 for prediction of profitability, measured by internal
rate of return. Wide and deep learning is used to build the predictive models
in both stages to achieve both memorization and generalization. Extensive
numerical studies are conducted based on real-world data to verify the
effectiveness of the proposed approach. The numerical studies indicate our
two-stage scoring approach outperforms the existing credit scoring and profit
scoring approaches.
Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning Techniques
Authors: Tiep Vu, Lam Nguyen, Vishal Monga
Date: 2018-10-04 00:00:00
Tasks: Dictionary Learning, General Classification, Sparse Representation-based Classification

Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture
radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or
mine, has been successfully demonstrated recently. Despite promising recent
progress, a significant open challenge is to distinguish obscured targets from
other (natural and manmade) clutter sources in the scene. The problem becomes
exacerbated in the presence of noisy responses from rough ground surfaces. In
this paper, we present three novel sparsity-driven techniques, which not only
exploit the subtle features of raw captured data but also take advantage of the
polarization diversity and the aspect angle dependence information from
multi-channel SAR data. First, the traditional sparse representation-based
classification (SRC) is generalized to exploit shared information of classes
and various sparsity structures of tensor coefficients for multi-channel data.
Corresponding tensor dictionary learning models are consequently proposed to
enhance classification accuracy. Lastly, a new tensor sparsity model is
proposed to model responses from multiple consecutive looks of objects, which
is a unique characteristic of the dataset we consider. Extensive experimental
results on a high-fidelity electromagnetic simulated dataset and radar data
collected from the U.S. Army Research Laboratory side-looking SAR demonstrate
the advantages of proposed tensor sparsity models.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Authors: Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira
Date: 2018-10-04 00:00:00
Tasks: Graph Embedding

We propose shifted inner-product similarity (SIPS), which is a novel yet very
simple extension of the ordinary inner-product similarity (IPS) for
neural-network based graph embedding (GE). In contrast to IPS, that is limited
to approximating positive-definite (PD) similarities, SIPS goes beyond the
limitation by introducing bias terms in IPS; we theoretically prove that SIPS
is capable of approximating not only PD but also conditionally PD (CPD)
similarities with many examples such as cosine similarity, negative Poincare
distance and negative Wasserstein distance. Since SIPS with sufficiently large
neural networks learns a variety of similarities, SIPS alleviates the need for
configuring the similarity function of GE. Approximation error rate is also
evaluated, and experiments on two real-world datasets demonstrate that graph
embedding using SIPS indeed outperforms existing methods.
Building a language evolution tree based on word vector combination model
Authors: Zhu Gao, Yanhui Jiang, Junhui Gao
Date: 2018-10-04 00:00:00
Tasks: Clustering

In this paper, we try to explore the evolution of language through case
calculations. First, we chose the novels of eleven British writers from 1400 to
2005 and found the corresponding works; Then, we use the natural language
processing tool to construct the corresponding eleven corpora, and calculate
the respective word vectors of 100 high-frequency words in eleven corpora;
Next, for each corpus, we concatenate the 100 word vectors from beginning to
end into one; Finally, we use the similarity comparison and hierarchical
clustering method to generate the relationship tree between the combined eleven
word vectors. This tree represents the relationship between eleven corpora. We
found that in the tree generated by clustering, the distance between the corpus
and the year corresponding to the corpus are basically the same. This means
that we have discovered a specific language evolution tree. To verify the
stability and versatility of this method, we add three other themes: Dickens's
eight works, the 19th century poets' works, and art criticism of recent 60
years. For these four themes, we tested different parameters such as the time
span of the corpus, the time interval between the corpora, the dimension of the
word vector, and the number of high-frequency public words. The results show
that this is fairly stable and versatile.
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
Authors: Jaejin Cho, Murali Karthick Baskar, Ruizhi Li, Matthew Wiesner, Sri Harish Mallidi, Nelson Yalta, Martin Karafiat, Shinji Watanabe, Takaaki Hori
Date: 2018-10-04 00:00:00
Tasks: Language Modeling, Language Modelling, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition, Transfer Learning

Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively
new direction in speech research. The approach benefits by performing model
training without using lexicon and alignments. However, this poses a new
problem of requiring more data compared to conventional DNN-HMM systems. In
this work, we attempt to use data from 10 BABEL languages to build a
multi-lingual seq2seq model as a prior model, and then port them towards 4
other BABEL languages using transfer learning approach. We also explore
different architectures for improving the prior multilingual seq2seq model. The
paper also discusses the effect of integrating a recurrent neural network
language model (RNNLM) with a seq2seq model during decoding. Experimental
results show that the transfer learning approach from the multilingual model
shows substantial gains over monolingual models across all 4 BABEL languages.
Incorporating an RNNLM also brings significant improvements in terms of %WER,
and achieves recognition performance comparable to the models trained with
twice more training data.
Active Learning for New Domains in Natural Language Understanding
Authors: Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha, Imre Kiss, Spyros Matsoukas
Date: 2018-10-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Active Learning, General Classification, Natural Language Understanding

We explore active learning (AL) for improving the accuracy of new domains in
a natural language understanding (NLU) system. We propose an algorithm called
Majority-CRF that uses an ensemble of classification models to guide the
selection of relevant utterances, as well as a sequence labeling model to help
prioritize informative examples. Experiments with three domains show that
Majority-CRF achieves 6.6%-9% relative error rate reduction compared to random
sampling with the same annotation budget, and statistically significant
improvements compared to other AL approaches. Additionally, case studies with
human-in-the-loop AL on six new domains show 4.6%-9% improvement on an existing
NLU system.
Exploiting Contextual Information via Dynamic Memory Network for Event Detection
Authors: Shaobo Liu, Rui Cheng, Xiaoming Yu, Xue-Qi Cheng
Date: 2018-10-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Event Detection

The task of event detection involves identifying and categorizing event
triggers. Contextual information has been shown effective on the task. However,
existing methods which utilize contextual information only process the context
once. We argue that the context can be better exploited by processing the
context multiple times, allowing the model to perform complex reasoning and to
generate better context representation, thus improving the overall performance.
Meanwhile, dynamic memory network (DMN) has demonstrated promising capability
in capturing contextual information and has been applied successfully to
various tasks. In light of the multi-hop mechanism of the DMN to model the
context, we propose the trigger detection dynamic memory network (TD-DMN) to
tackle the event detection problem. We performed a five-fold cross-validation
on the ACE-2005 dataset and experimental results show that the multi-hop
mechanism does improve the performance and the proposed model achieves best
$F_1$ score compared to the state-of-the-art methods.
CINIC-10 is not ImageNet or CIFAR-10
Authors: Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey
Date: 2018-10-02 00:00:00
Tasks: Image Classification

In this brief technical report we introduce the CINIC-10 dataset as a plug-in
extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with
images selected and downsampled from the ImageNet database. We present the
approach to compiling the dataset, illustrate the example images for different
classes, give pixel distributions for each part of the repository, and give
some standard benchmarks for well known models. Details for download, usage,
and compilation can be found in the associated github repository.
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
Authors: Bowen Cheng, Yunchao Wei, Rogerio Feris, JinJun Xiong, Wen-mei Hwu, Thomas Huang, Humphrey Shi
Date: 2018-10-05 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, object-detection, Object Detection

In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization and they have a large negative impact on the performance of object detectors. We conjecture there are three factors: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects. We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. In particular, DCR places a separate classification network in parallel with the localization network (base detector). With ROI Pooling placed on the early stage of the classification network, we enforce an adaptive receptive field in DCR. During training, DCR samples hard false positives from the base detector and trains a strong classifier to refine classification results. During testing, DCR refines all boxes from the base detector. Experiments show competitive results on PASCAL VOC and COCO without any bells and whistles. Our codes are available at: https://github.com/bowenc0221/Decoupled-Classification-Refinement.
Covariance and PCA for Categorical Variables
Authors: Hirotaka Niitsuma, Takashi Okada
Date: 2007-11-28 00:00:00
Tasks: Variable Selection

Covariances from categorical variables are defined using a regular simplex
expression for categories. The method follows the variance definition by Gini,
and it gives the covariance as a solution of simultaneous equations. The
calculated results give reasonable values for test data. A method of principal
component analysis (RS-PCA) is also proposed using regular simplex expressions,
which allows easy interpretation of the principal components. The proposed
methods apply to variable selection problem of categorical data USCensus1990
data. The proposed methods give appropriate criterion for the variable
selection problem of categorical