pwc-archive/papers-with-abstracts huggingface dataset:
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
Authors: Jin Yamanaka, Shigesumi Kuwashima, Takio Kurita
Date: 2017-07-18 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. Current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and is not suitable for network edge devices like mobile, tablet and IoT devices. Our model achieves state of the art reconstruction performance with at least 10 times lower calculation cost by Deep CNN with Residual Net, Skip Connection and Network in Network (DCSCN). A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area. Parallelized 1x1 CNNs, like the one called Network in Network, is also used for image reconstruction. That structure reduces the dimensions of the previous layer's output for faster computation with less information loss, and make it possible to process original images directly. Also we optimize the number of layers and filters of each CNN to significantly reduce the calculation cost. Thus, the proposed algorithm not only achieves the state of the art performance but also achieves faster and efficient computation. Code is available at https://github.com/jiny2001/dcscn-super-resolution
Defining a Lingua Franca to Open the Black Box of a Naïve Bayes Recommender
Authors: Kenneth L. Hess, Hugo D. Paz
Date: 2017-09-21 00:00:00

Many AI systems have a black box nature that makes it difficult to understand
how they make their recommendations. This can be unsettling, as the designer
cannot be certain how the system will respond to novelty. To penetrate our
Na\"ive Bayes recommender's black box, we first asked, what do we want to know
from our system, and how can it be obtained? The answers led us to recursively
define a common lexicon with the AI, a lingua franca, using the very items that
the system ranks to create meta-symbols recognized by the system, and enabling
us to understand the system's knowledge in plain terms and at different levels
of abstraction. As one bonus, using its existing knowledge, the lingua franca
can enable the system to extend recommendations to related, but entirely new
areas, ameliorating the cold start problem. We also supplement the lingua
franca with techniques for visualizing the system's knowledge state, develop
metrics for evaluating the meaningfulness of terms in the lingua franca, and
generalize the requirements for developing a similar lingua franca in other
applications.
Robust Optimization of Unconstrained Binary Quadratic Problems
Authors: Mark Lewis, Gary Kochenberger, John Metcalfe
Date: 2017-09-21 00:00:00
Tasks: Decision Making, Experimental Design

In this paper we focus on the unconstrained binary quadratic optimization
model, maximize x^t Qx, x binary, and consider the problem of identifying
optimal solutions that are robust with respect to perturbations in the Q
matrix.. We are motivated to find robust, or stable, solutions because of the
uncertainty inherent in the big data origins of Q and limitations in computer
numerical precision, particularly in a new class of quantum annealing
computers. Experimental design techniques are used to generate a diverse subset
of possible scenarios, from which robust solutions are identified. An
illustrative example with practical application to business decision making is
examined. The approach presented also generates a surface response equation
which is used to estimate upper bounds in constant time for Q instantiations
within the scenario extremes. In addition, a theoretical framework for the
robustness of individual x_i variables is considered by examining the range of
Q values over which the x_i are predetermined.
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition
Authors: Ahmed Ali, Preslav Nakov, Peter Bell, Steve Renals
Date: 2017-09-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

We study the problem of evaluating automatic speech recognition (ASR) systems
that target dialectal speech input. A major challenge in this case is that the
orthography of dialects is typically not standardized. From an ASR evaluation
perspective, this means that there is no clear gold standard for the expected
output, and several possible outputs could be considered correct according to
different human annotators, which makes standard word error rate (WER)
inadequate as an evaluation metric. Such a situation is typical for machine
translation (MT), and thus we borrow ideas from an MT evaluation metric, namely
TERp, an extension of translation error rate which is closely-related to WER.
In particular, in the process of comparing a hypothesis to a reference, we make
use of spelling variants for words and phrases, which we mine from Twitter in
an unsupervised fashion. Our experiments with evaluating ASR output for
Egyptian Arabic, and further manual analysis, show that the resulting WERd
(i.e., WER for dialects) metric, a variant of TERp, is more adequate than WER
for evaluating dialectal ASR.
Complexity of Scheduling Charging in the Smart Grid
Authors: Mathijs de Weerdt, Michael Albert, Vincent Conitzer
Date: 2017-09-21 00:00:00
Tasks: Scheduling

In the smart grid, the intent is to use flexibility in demand, both to
balance demand and supply as well as to resolve potential congestion. A first
prominent example of such flexible demand is the charging of electric vehicles,
which do not necessarily need to be charged as soon as they are plugged in. The
problem of optimally scheduling the charging demand of electric vehicles within
the constraints of the electricity infrastructure is called the charge
scheduling problem. The models of the charging speed, horizon, and charging
demand determine the computational complexity of the charge scheduling problem.
For about 20 variants, we show, using a dynamic programming approach, that the
problem is either in P or weakly NP-hard. We also show that about 10 variants
of the problem are strongly NP-hard, presenting a potentially significant
obstacle to their use in practical situations of scale.
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts
Authors: Arpita Roy, Youngja Park, SHimei Pan
Date: 2017-09-21 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentiment Analysis, Word Embeddings

Word embedding is a Natural Language Processing (NLP) technique that
automatically maps words from a vocabulary to vectors of real numbers in an
embedding space. It has been widely used in recent years to boost the
performance of a vari-ety of NLP tasks such as Named Entity Recognition,
Syntac-tic Parsing and Sentiment Analysis. Classic word embedding methods such
as Word2Vec and GloVe work well when they are given a large text corpus. When
the input texts are sparse as in many specialized domains (e.g.,
cybersecurity), these methods often fail to produce high-quality vectors. In
this pa-per, we describe a novel method to train domain-specificword embeddings
from sparse texts. In addition to domain texts, our method also leverages
diverse types of domain knowledge such as domain vocabulary and semantic
relations. Specifi-cally, we first propose a general framework to encode
diverse types of domain knowledge as text annotations. Then we de-velop a novel
Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text
annotations in word em-bedding. We have evaluated our method on two
cybersecurity text corpora: a malware description corpus and a Common
Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have
demonstrated the effectiveness of our method in learning domain-specific word
embeddings.
Analyzing users' sentiment towards popular consumer industries and brands on Twitter
Authors: Guoning Hu, Preeti Bhargava, Saul Fuhrmann, Sarah Ellinger, Nemanja Spasojevic
Date: 2017-09-21 00:00:00
Tasks: Sentiment Analysis

Social media serves as a unified platform for users to express their thoughts
on subjects ranging from their daily lives to their opinion on consumer brands
and products. These users wield an enormous influence in shaping the opinions
of other consumers and influence brand perception, brand loyalty and brand
advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62
popular industries, encompassing 12,898 enterprise and consumer brands, as well
as associated subject matter topics, via sentiment analysis of 330M tweets over
a period spanning a month. We find that users tend to be most positive towards
manufacturing and most negative towards service industries. In addition, they
tend to be more positive or negative when interacting with brands than
generally on Twitter. We also find that sentiment towards brands within an
industry varies greatly and we demonstrate this using two industries as use
cases. In addition, we discover that there is no strong correlation between
topic sentiments of different industries, demonstrating that topic sentiments
are highly dependent on the context of the industry that they are mentioned in.
We demonstrate the value of such an analysis in order to assess the impact of
brands on social media. We hope that this initial study will prove valuable for
both researchers and companies in understanding users' perception of
industries, brands and associated topics and encourage more research in this
field.
Consensus measure of rankings
Authors: Zhiwei Lin, Yi Li, Xiaolian Guo
Date: 2017-04-27 00:00:00

A ranking is an ordered sequence of items, in which an item with higher
ranking score is more preferred than the items with lower ranking scores. In
many information systems, rankings are widely used to represent the preferences
over a set of items or candidates. The consensus measure of rankings is the
problem of how to evaluate the degree to which the rankings agree. The
consensus measure can be used to evaluate rankings in many information systems,
as quite often there is not ground truth available for evaluation.
  This paper introduces a novel approach for consensus measure of rankings by
using graph representation, in which the vertices or nodes are the items and
the edges are the relationship of items in the rankings. Such representation
leads to various algorithms for consensus measure in terms of different aspects
of rankings, including the number of common patterns, the number of common
patterns with fixed length and the length of the longest common patterns. The
proposed measure can be adopted for various types of rankings, such as full
rankings, partial rankings and rankings with ties. This paper demonstrates how
the proposed approaches can be used to evaluate the quality of rank aggregation
and the quality of top-$k$ rankings from Google and Bing search engines.
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation
Authors: Toan Q. Nguyen, David Chiang
Date: 2017-08-31 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Transfer Learning, Translation, Word Embeddings

We present a simple method to improve neural translation of a low-resource
language pair using parallel data from a related, also low-resource, language
pair. The method is based on the transfer method of Zoph et al., but whereas
their method ignores any source vocabulary overlap, ours exploits it. First, we
split words using Byte Pair Encoding (BPE) to increase vocabulary overlap.
Then, we train a model on the first language pair and transfer its parameters,
including its source word embeddings, to another model and continue training on
the second language pair. Our experiments show that transfer learning helps
word-based translation only slightly, but when used on top of a much stronger
BPE baseline, it yields larger improvements of up to 4.3 BLEU.
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
Authors: Jawad Tayyub, Majd Hawasly, David C. Hogg, Anthony G. Cohn
Date: 2017-09-11 00:00:00
Tasks: Activity Recognition, object-detection, Object Detection

This paper introduces a novel activity dataset which exhibits real-life and
diverse scenarios of complex, temporally-extended human activities and actions.
The dataset presents a set of videos of actors performing everyday activities
in a natural and unscripted manner. The dataset was recorded using a static
Kinect 2 sensor which is commonly used on many robotic platforms. The dataset
comprises of RGB-D images, point cloud data, automatically generated skeleton
tracks in addition to crowdsourced annotations. Furthermore, we also describe
the methodology used to acquire annotations through crowdsourcing. Finally some
activity recognition benchmarks are presented using current state-of-the-art
techniques. We believe that this dataset is particularly suitable as a testbed
for activity recognition research but it can also be applicable for other
common tasks in robotics/computer vision research such as object detection and
human skeleton tracking.
Quantum autoencoders via quantum adders with genetic algorithms
Authors: L. Lamata, U. Alvarez-Rodriguez, J. D. Martín-Guerrero, M. Sanz, E. Solano
Date: 2017-09-21 00:00:00
Tasks: Quantum Machine Learning

The quantum autoencoder is a recent paradigm in the field of quantum machine
learning, which may enable an enhanced use of resources in quantum
technologies. To this end, quantum neural networks with less nodes in the inner
than in the outer layers were considered. Here, we propose a useful connection
between approximate quantum adders and quantum autoencoders. Specifically, this
link allows us to employ optimized approximate quantum adders, obtained with
genetic algorithms, for the implementation of quantum autoencoders for a
variety of initial states. Furthermore, we can also directly optimize the
quantum autoencoders via genetic algorithms. Our approach opens a different
path for the design of quantum autoencoders in controllable quantum platforms.
Influence of Personal Preferences on Link Dynamics in Social Networks
Authors: Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh Chawla, Omar Lizardo, Kevin Chan
Date: 2017-09-21 00:00:00
Tasks: Attribute

We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.
Symbolic LTLf Synthesis
Authors: Shufang Zhu, Lucas M. Tabajara, Jianwen Li, Geguang Pu, Moshe Y. Vardi
Date: 2017-05-23 00:00:00

LTLf synthesis is the process of finding a strategy that satisfies a linear
temporal specification over finite traces. An existing solution to this problem
relies on a reduction to a DFA game. In this paper, we propose a symbolic
framework for LTLf synthesis based on this technique, by performing the
computation over a representation of the DFA as a boolean formula rather than
as an explicit graph. This approach enables strategy generation by utilizing
the mechanism of boolean synthesis. We implement this symbolic synthesis method
in a tool called Syft, and demonstrate by experiments on scalable benchmarks
that the symbolic approach scales better than the explicit one.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Authors: Hongteng Xu, Hongyuan Zha
Date: 2017-01-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Bayesian Inference, Clustering, Point Processes

We propose an effective method to solve the event sequence clustering
problems based on a novel Dirichlet mixture model of a special but significant
type of point processes --- Hawkes process. In this model, each event sequence
belonging to a cluster is generated via the same Hawkes process with specific
parameters, and different clusters correspond to different Hawkes processes.
The prior distribution of the Hawkes processes is controlled via a Dirichlet
distribution. We learn the model via a maximum likelihood estimator (MLE) and
propose an effective variational Bayesian inference algorithm. We specifically
analyze the resulting EM-type algorithm in the context of inner-outer
iterations and discuss several inner iteration allocation strategies. The
identifiability of our model, the convergence of our learning method, and its
sample complexity are analyzed in both theoretical and empirical ways, which
demonstrate the superiority of our method to other competitors. The proposed
method learns the number of clusters automatically and is robust to model
misspecification. Experiments on both synthetic and real-world data show that
our method can learn diverse triggering patterns hidden in asynchronous event
sequences and achieve encouraging performance on clustering purity and
consistency.
Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE
Authors: Georgios Douzas, Fernando Bacao
Date: 2017-09-21 00:00:00

Classification of imbalanced datasets is a challenging task for standard
algorithms. Although many methods exist to address this problem in different
ways, generating artificial data for the minority class is a more general
approach compared to algorithmic modifications. SMOTE algorithm and its
variations generate synthetic samples along a line segment that joins minority
class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
generalization of the SMOTE data generation mechanism. G-SMOTE generates
synthetic samples in a geometric region of the input space, around each
selected minority instance. While in the basic configuration this region is a
hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
a line segment, emulating, in the last case, the SMOTE mechanism. The
performance of G-SMOTE is compared against multiple standard oversampling
algorithms. We present empirical results that show a significant improvement in
the quality of the generated data when G-SMOTE is used as an oversampling
algorithm.
Frankenstein: Learning Deep Face Representations using Small Data
Authors: Guosheng Hu, Xiaojiang Peng, Yongxin Yang, Timothy Hospedales, Jakob Verbeek
Date: 2016-03-21 00:00:00
Tasks: Face Recognition, Heterogeneous Face Recognition, Small Data Image Classification

Deep convolutional neural networks have recently proven extremely effective
for difficult face recognition problems in uncontrolled settings. To train such
networks, very large training sets are needed with millions of labeled images.
For some applications, such as near-infrared (NIR) face recognition, such large
training datasets are not publicly available and difficult to collect. In this
work, we propose a method to generate very large training datasets of synthetic
images by compositing real face images in a given dataset. We show that this
method enables to learn models from as few as 10,000 training images, which
perform on par with models trained from 500,000 images. Using our approach we
also obtain state-of-the-art results on the CASIA NIR-VIS2.0 heterogeneous face
recognition dataset.
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs
Authors: Chandan Singh, Beilun Wang, Yanjun Qi
Date: 2017-09-13 00:00:00
Proceeding: arXiv 2017 9
Tasks: Connectivity Estimation, Functional Connectivity

Determining functional brain connectivity is crucial to understanding the
brain and neural differences underlying disorders such as autism. Recent
studies have used Gaussian graphical models to learn brain connectivity via
statistical dependencies across brain regions from neuroimaging. However,
previous studies often fail to properly incorporate priors tailored to
neuroscience, such as preferring shorter connections. To remedy this problem,
the paper here introduces a novel, weighted-$\ell_1$, multi-task graphical
model (W-SIMULE). This model elegantly incorporates a flexible prior, along
with a parallelizable formulation. Additionally, W-SIMULE extends the
often-used Gaussian assumption, leading to considerable performance increases.
Here, applications to fMRI data show that W-SIMULE succeeds in determining
functional connectivity in terms of (1) log-likelihood, (2) finding edges that
differentiate groups, and (3) classifying different groups based on their
connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having
established W-SIMULE's effectiveness, it links four key areas to autism, all of
which are consistent with the literature. Due to its elegant domain adaptivity,
W-SIMULE can be readily applied to various data types to effectively estimate
connectivity.
Non-Depth-First Search against Independent Distributions on an AND-OR Tree
Authors: Toshio Suzuki
Date: 2017-09-21 00:00:00

Suzuki and Niida (Ann. Pure. Appl. Logic, 2015) showed the following results
on independent distributions (IDs) on an AND-OR tree, where they took only
depth-first algorithms into consideration. (1) Among IDs such that probability
of the root having value 0 is fixed as a given r such that 0 < r < 1, if d is a
maximizer of cost of the best algorithm then d is an independent and identical
distribution (IID). (2) Among all IDs, if d is a maximizer of cost of the best
algorithm then d is an IID. In the case where non-depth-first algorithms are
taken into consideration, the counter parts of (1) and (2) are left open in the
above work. Peng et al. (Inform. Process. Lett., 2017) extended (1) and (2) to
multi-branching trees, where in (2) they put an additional hypothesis on IDs
that probability of the root having value 0 is neither 0 nor 1. We give
positive answers for the two questions of Suzuki-Niida. A key to the proof is
that if ID d achieves the equilibrium among IDs then we can chose an algorithm
of the best cost against d from depth-first algorithms. In addition, we extend
the result of Peng et al. to the case where non-depth-first algorithms are
taken into consideration.