pwc-archive/papers-with-abstracts huggingface dataset:
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Authors: Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, Kai-Wei Chang
Date: 2017-07-29 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Semantic Role Labeling, Structured Prediction

Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found in web corpora. In this work, we study data and models
associated with multilabel object classification and visual semantic role
labeling. We find that (a) datasets for these tasks contain significant gender
bias and (b) models trained on these datasets further amplify existing bias.
For example, the activity cooking is over 33% more likely to involve females
than males in a training set, and a trained model further amplifies the
disparity to 68% at test time. We propose to inject corpus-level constraints
for calibrating existing structured prediction models and design an algorithm
based on Lagrangian relaxation for collective inference. Our method results in
almost no performance loss for the underlying recognition task but decreases
the magnitude of bias amplification by 47.5% and 40.5% for multilabel
classification and visual semantic role labeling, respectively.
Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
Authors: Vineet John, Olga Vechtomova
Date: 2017-07-29 00:00:00
Tasks: Data Augmentation, regression, Sentiment Analysis

This paper discusses the approach taken by the UWaterloo team to arrive at a
solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of
SemEval 2017. The paper describes the document vectorization and sentiment
score prediction techniques used, as well as the design and implementation
decisions taken while building the system for this task. The system uses text
vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled
with regression model variants to predict the sentiment scores. Amongst the
methods examined, unigrams and bigrams coupled with simple linear regression
obtained the best baseline accuracy. The paper also explores data augmentation
methods to supplement the training dataset. This system was designed for
Subtask 2 (News Statements and Headlines).
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
Authors: Hamid Dadkhahi, Marco F. Duarte, Benjamin Marlin
Date: 2016-06-27 00:00:00
Tasks: Dimensionality Reduction, Gaze Estimation, Time Series, Time Series Analysis

This paper proposes an out-of-sample extension framework for a global
manifold learning algorithm (Isomap) that uses temporal information in
out-of-sample points in order to make the embedding more robust to noise and
artifacts. Given a set of noise-free training data and its embedding, the
proposed framework extends the embedding for a noisy time series. This is
achieved by adding a spatio-temporal compactness term to the optimization
objective of the embedding. To the best of our knowledge, this is the first
method for out-of-sample extension of manifold embeddings that leverages timing
information available for the extension set. Experimental results demonstrate
that our out-of-sample extension algorithm renders a more robust and accurate
embedding of sequentially ordered image data in the presence of various noise
and artifacts when compared to other timing-aware embeddings. Additionally, we
show that an out-of-sample extension framework based on the proposed algorithm
outperforms the state of the art in eye-gaze estimation.
Author Name Disambiguation by Using Deep Neural Network
Authors: Hung Nghiep Tran, Tin Huynh, Tien Do
Date: 2015-02-27 00:00:00

Author name ambiguity decreases the quality and reliability of information
retrieved from digital libraries. Existing methods have tried to solve this
problem by predefining a feature set based on expert's knowledge for a specific
dataset. In this paper, we propose a new approach which uses deep neural
network to learn features automatically from data. Additionally, we propose the
general system architecture for author name disambiguation on any dataset. In
this research, we evaluate the proposed method on a dataset containing
Vietnamese author names. The results show that this method significantly
outperforms other methods that use predefined feature set. The proposed method
achieves 99.31% in terms of accuracy. Prediction error rate decreases from
1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with
other methods that use predefined feature set (Table 3).
Bilingual Document Alignment with Latent Semantic Indexing
Authors: Ulrich Germann
Date: 2017-07-29 00:00:00
Proceeding: WS 2016 8

We apply cross-lingual Latent Semantic Indexing to the Bilingual Document
Alignment Task at WMT16. Reduced-rank singular value decomposition of a
bilingual term-document matrix derived from known English/French page pairs in
the training data allows us to map monolingual documents into a joint semantic
space. Two variants of cosine similarity between the vectors that place each
document into the joint semantic space are combined with a measure of string
similarity between corresponding URLs to produce 1:1 alignments of
English/French web pages in a variety of domains. The system achieves a recall
of ca. 88% if no in-domain data is used for building the latent semantic model,
and 93% if such data is included.
  Analysing the system's errors on the training data, we argue that evaluating
aligner performance based on exact URL matches under-estimates their true
performance and propose an alternative that is able to account for duplicates
and near-duplicates in the underlying data.
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
Authors: Brett W. Israelsen, Nisar Ahmed, Kenneth Center, Roderick Green, Winston Bennett Jr
Date: 2017-03-27 00:00:00
Tasks: Bayesian Optimization, Decision Making

This work studies how an AI-controlled dog-fighting agent with tunable
decision-making parameters can learn to optimize performance against an
intelligent adversary, as measured by a stochastic objective function evaluated
on simulated combat engagements. Gaussian process Bayesian optimization (GPBO)
techniques are developed to automatically learn global Gaussian Process (GP)
surrogate models, which provide statistical performance predictions in both
explored and unexplored areas of the parameter space. This allows a learning
engine to sample full-combat simulations at parameter values that are most
likely to optimize performance and also provide highly informative data points
for improving future predictions. However, standard GPBO methods do not provide
a reliable surrogate model for the highly volatile objective functions found in
aerial combat, and thus do not reliably identify global maxima. These issues
are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point
Sampling (HRMS) techniques. Simulation studies show that HRMS improves the
accuracy of GP surrogate models, allowing AI decision-makers to more accurately
predict performance and efficiently tune parameters.
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Authors: Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey
Date: 2016-08-04 00:00:00

Phenomenally successful in practical inference problems, convolutional neural
networks (CNN) are widely deployed in mobile devices, data centers, and even
supercomputers. The number of parameters needed in CNNs, however, are often
large and undesirable. Consequently, various methods have been developed to
prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited
benefits. While pruning the fully connected layers reduces a CNN's size
considerably, it does not improve inference speed noticeably as the compute
heavy parts lie in convolutions. Pruning CNNs in a way that increase inference
speed often imposes specific sparsity structures, thus limiting the achievable
sparsity levels.
  We present a method to realize simultaneously size economy and speed
improvement while pruning CNNs. Paramount to our success is an efficient
general sparse-with-dense matrix multiplication implementation that is
applicable to convolution of feature maps with kernels of arbitrary sparsity
patterns. Complementing this, we developed a performance model that predicts
sweet spots of sparsity levels for different layers and on different computer
architectures. Together, these two allow us to demonstrate 3.1--7.3$\times$
convolution speedups over dense convolution in AlexNet, on Intel Atom, Xeon,
and Xeon Phi processors, spanning the spectrum from mobile devices to
supercomputers. We also open source our project at
https://github.com/IntelLabs/SkimCaffe.
Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms
Authors: Christian A. Hammerschmidt, Radu State, Sicco Verwer
Date: 2017-07-28 00:00:00

We present an interactive version of an evidence-driven state-merging (EDSM)
algorithm for learning variants of finite state automata. Learning these
automata often amounts to recovering or reverse engineering the model
generating the data despite noisy, incomplete, or imperfectly sampled data
sources rather than optimizing a purely numeric target function. Domain
expertise and human knowledge about the target domain can guide this process,
and typically is captured in parameter settings. Often, domain expertise is
subconscious and not expressed explicitly. Directly interacting with the
learning algorithm makes it easier to utilize this knowledge effectively.
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
Authors: Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
Date: 2017-07-28 00:00:00
Tasks: Drift Detection

With the wide application of machine learning algorithms to the real world,
class imbalance and concept drift have become crucial learning issues. Class
imbalance happens when the data categories are not equally represented, i.e.,
at least one category is minority compared to other categories. It can cause
learning bias towards the majority class and poor generalization. Concept drift
is a change in the underlying distribution of the problem, and is a significant
issue specially when learning from data streams. It requires learners to be
adaptive to dynamic changes.
  Class imbalance and concept drift can significantly hinder predictive
performance, and the problem becomes particularly challenging when they occur
simultaneously. This challenge arises from the fact that one problem can affect
the treatment of the other. For example, drift detection algorithms based on
the traditional classification error may be sensitive to the imbalanced degree
and become less effective; and class imbalance techniques need to be adaptive
to changing imbalance rates, otherwise the class receiving the preferential
treatment may not be the correct minority class at the current moment.
Therefore, the mutual effect of class imbalance and concept drift should be
considered during algorithm design.
  The aim of this workshop is to bring together researchers from the areas of
class imbalance learning and concept drift in order to encourage discussions
and new collaborations on solving the combined issue of class imbalance and
concept drift. It provides a forum for international researchers and
practitioners to share and discuss their original work on addressing new
challenges and research issues in class imbalance learning, concept drift, and
the combined issues of class imbalance and concept drift. The proceedings
include 8 papers on these topics.
Theoretical Analysis of Domain Adaptation with Optimal Transport
Authors: Ievgen Redko, Amaury Habrard, Marc Sebban
Date: 2016-10-14 00:00:00
Tasks: Domain Adaptation

Domain adaptation (DA) is an important and emerging field of machine learning
that tackles the problem occurring when the distributions of training (source
domain) and test (target domain) data are similar but different. Current
theoretical results show that the efficiency of DA algorithms depends on their
capacity of minimizing the divergence between source and target probability
distributions. In this paper, we provide a theoretical study on the advantages
that concepts borrowed from optimal transportation theory can bring to DA. In
particular, we show that the Wasserstein metric can be used as a divergence
measure between distributions to obtain generalization guarantees for three
different learning settings: (i) classic DA with unsupervised target data (ii)
DA combining source and target labeled data, (iii) multiple source DA. Based on
the obtained results, we provide some insights showing when this analysis can
be tighter than other existing frameworks.
Hyperprofile-based Computation Offloading for Mobile Edge Networks
Authors: Andrew Crutcher, Caleb Koch, Kyle Coleman, Jon Patman, Flavio Esposito, Prasad Calyam
Date: 2017-07-28 00:00:00

In recent studies, researchers have developed various computation offloading
frameworks for bringing cloud services closer to the user via edge networks.
Specifically, an edge device needs to offload computationally intensive tasks
because of energy and processing constraints. These constraints present the
challenge of identifying which edge nodes should receive tasks to reduce
overall resource consumption. We propose a unique solution to this problem
which incorporates elements from Knowledge-Defined Networking (KDN) to make
intelligent predictions about offloading costs based on historical data. Each
server instance can be represented in a multidimensional feature space where
each dimension corresponds to a predicted metric. We compute features for a
"hyperprofile" and position nodes based on the predicted costs of offloading a
particular task. We then perform a k-Nearest Neighbor (kNN) query within the
hyperprofile to select nodes for offloading computation. This paper formalizes
our hyperprofile-based solution and explores the viability of using machine
learning (ML) techniques to predict metrics useful for computation offloading.
We also investigate the effects of using different distance metrics for the
queries. Our results show various network metrics can be modeled accurately
with regression, and there are circumstances where kNN queries using Euclidean
distance as opposed to rectilinear distance is more favorable.
FontCode: Embedding Information in Text Documents using Glyph Perturbation
Authors: Chang Xiao, Cheng Zhang, Changxi Zheng
Date: 2017-07-28 00:00:00

We introduce FontCode, an information embedding technique for text documents.
Provided a text document with specific fonts, our method embeds user-specified
information in the text by perturbing the glyphs of text characters while
preserving the text content. We devise an algorithm to chooses unobtrusive yet
machine-recognizable glyph perturbations, leveraging a recently developed
generative model that alters the glyphs of each character continuously on a
font manifold. We then introduce an algorithm that embeds a user-provided
message in the text document and produces an encoded document whose appearance
is minimally perturbed from the original document. We also present a glyph
recognition method that recovers the embedded information from an encoded
document stored as a vector graphic or pixel image, or even on a printed paper.
In addition, we introduce a new error-correction coding scheme that rectifies a
certain number of recognition errors. Lastly, we demonstrate that our technique
enables a wide array of applications, using it as a text document metadata
holder, an unobtrusive optical barcode, a cryptographic message embedding
scheme, and a text document signature.
A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Authors: Wenlin Yao, Saipravallika Nettyam, Ruihong Huang
Date: 2017-07-28 00:00:00
Proceeding: RANLP 2017 9
Tasks: Relation, Weakly-supervised Learning

Capabilities of detecting temporal relations between two events can benefit
many applications. Most of existing temporal relation classifiers were trained
in a supervised manner. Instead, we explore the observation that regular event
pairs show a consistent temporal relation despite of their various contexts,
and these rich contexts can be used to train a contextual temporal relation
classifier, which can further recognize new temporal relation contexts and
identify new regular event pairs. We focus on detecting after and before
temporal relations and design a weakly supervised learning approach that
extracts thousands of regular event pairs and learns a contextual temporal
relation classifier simultaneously. Evaluation shows that the acquired regular
event pairs are of high quality and contain rich commonsense knowledge and
domain specific knowledge. In addition, the weakly supervised trained temporal
relation classifier achieves comparable performance with the state-of-the-art
supervised systems.
Online Deception Detection Refueled by Real World Data Collection
Authors: Wenlin Yao, Zeyu Dai, Ruihong Huang, James Caverlee
Date: 2017-07-28 00:00:00
Proceeding: RANLP 2017 9
Tasks: Deception Detection

The lack of large realistic datasets presents a bottleneck in online
deception detection studies. In this paper, we apply a data collection method
based on social network analysis to quickly identify high-quality deceptive and
truthful online reviews from Amazon. The dataset contains more than 10,000
deceptive reviews and is diverse in product domains and reviewers. Using this
dataset, we explore effective general features for online deception detection
that perform well across domains. We demonstrate that with generalized features
- advertising speak and writing complexity scores - deception detection
performance can be further improved by adding additional deceptive reviews from
assorted domains in training. Finally, reviewer level evaluation gives an
interesting insight into different deceptive reviewers' writing styles.
Photographic Image Synthesis with Cascaded Refinement Networks
Authors: Qifeng Chen, Vladlen Koltun
Date: 2017-07-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Generation, Image-to-Image Translation

We present an approach to synthesizing photographic images conditioned on
semantic layouts. Given a semantic label map, our approach produces an image
with photographic appearance that conforms to the input layout. The approach
thus functions as a rendering engine that takes a two-dimensional semantic
specification of the scene and produces a corresponding photographic image.
Unlike recent and contemporaneous work, our approach does not rely on
adversarial training. We show that photographic images can be synthesized from
semantic layouts by a single feedforward network with appropriate structure,
trained end-to-end with a direct regression objective. The presented approach
scales seamlessly to high resolutions; we demonstrate this by synthesizing
photographic images at 2-megapixel resolution, the full resolution of our
training data. Extensive perceptual experiments on datasets of outdoor and
indoor scenes demonstrate that images synthesized by the presented approach are
considerably more realistic than alternative approaches. The results are shown
in the supplementary video at https://youtu.be/0fhUJT21-bs
Eyemotion: Classifying facial expressions in VR using eye-tracking cameras
Authors: Steven Hickson, Nick Dufour, Avneesh Sud, Vivek Kwatra, Irfan Essa
Date: 2017-07-22 00:00:00
Tasks: Blocking

One of the main challenges of social interaction in virtual reality settings
is that head-mounted displays occlude a large portion of the face, blocking
facial expressions and thereby restricting social engagement cues among users.
Hence, auxiliary means of sensing and conveying these expressions are needed.
We present an algorithm to automatically infer expressions by analyzing only a
partially occluded face while the user is engaged in a virtual reality
experience. Specifically, we show that images of the user's eyes captured from
an IR gaze-tracking camera within a VR headset are sufficient to infer a select
subset of facial expressions without the use of any fixed external camera.
Using these inferences, we can generate dynamic avatars in real-time which
function as an expressive surrogate for the user. We propose a novel data
collection pipeline as well as a novel approach for increasing CNN accuracy via
personalization. Our results show a mean accuracy of 74% ($F1$ of 0.73) among 5
`emotive' expressions and a mean accuracy of 70% ($F1$ of 0.68) among 10
distinct facial action units, outperforming human raters.
Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones
Authors: Mohamed Abudulaziz Ali Haseeb, Ramviyas Parasuraman
Date: 2017-07-26 00:00:00
Tasks: BIG-bench Machine Learning, Gesture Recognition

This paper introduces Wisture, a new online machine learning solution for
recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on
the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term
Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic
induction. Unlike other Wi-Fi based gesture recognition methods, the proposed
method does not require a modification of the smartphone hardware or the
operating system, and performs the gesture recognition without interfering with
the normal operation of other smartphone applications.
  We discuss the characteristics of Wisture, and conduct extensive experiments
to compare its performance against state-of-the-art machine learning solutions
in terms of both accuracy and time efficiency. The experiments include a set of
different scenarios in terms of both spatial setup and traffic between the
smartphone and Wi-Fi access points (AP). The results show that Wisture achieves
an online recognition accuracy of up to 94% (average 78%) in detecting and
classifying three hand gestures.
Face Deidentification with Generative Deep Neural Networks
Authors: Blaž Meden, Refik Can Malli, Sebastjan Fabijan, Hazim Kemal Ekenel, Vitomir Štruc, Peter Peer
Date: 2017-07-28 00:00:00

Face deidentification is an active topic amongst privacy and security
researchers. Early deidentification methods relying on image blurring or
pixelization were replaced in recent years with techniques based on formal
anonymity models that provide privacy guaranties and at the same time aim at
retaining certain characteristics of the data even after deidentification. The
latter aspect is particularly important, as it allows to exploit the
deidentified data in applications for which identity information is irrelevant.
In this work we present a novel face deidentification pipeline, which ensures
anonymity by synthesizing artificial surrogate faces using generative neural
networks (GNNs). The generated faces are used to deidentify subjects in images
or video, while preserving non-identity-related aspects of the data and
consequently enabling data utilization. Since generative networks are very
adaptive and can utilize a diverse set of parameters (pertaining to the
appearance of the generated output in terms of facial expressions, gender,
race, etc.), they represent a natural choice for the problem of face
deidentification. To demonstrate the feasibility of our approach, we perform
experiments using automated recognition tools and human annotators. Our results
show that the recognition performance on deidentified images is close to
chance, suggesting that the deidentification process based on GNNs is highly
effective.