pwc-archive/papers-with-abstracts huggingface dataset:
Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study
Authors: Mason Wright
Date: 2016-04-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Empirical game-theoretic analysis (EGTA) has recently been applied
successfully to analyze the behavior of large numbers of competing traders in a
continuous double auction market. Multiagent simulation methods like EGTA are
useful for studying complex strategic environments like a stock market, where
it is not feasible to solve analytically for the rational behavior of each
agent. A weakness of simulation-based methods in strategic settings, however,
is that it is typically impossible to prove that the strategy profile assigned
to the simulated agents is stable, as in a Nash equilibrium. I propose using
reinforcement learning to analyze the regret of supposed Nash-equilibrium
strategy profiles found by EGTA. I have developed a new library of
reinforcement learning tools, which I have integrated into an extended version
of the market simulator from our prior work. I provide evidence for the
effectiveness of our library methods, both on a suite of benchmark problems
from the literature, and on non-equilibrium strategy profiles in our market
environment. Finally, I use our new reinforcement learning tools to provide
evidence that the equilibria found by EGTA in our recent continuous double
auction study are likely to have only negligible regret, even with respect to
an extended strategy space.
Detecting state of aggression in sentences using CNN
Authors: Rodmonga Potapova, Denis Gordeev
Date: 2016-04-22 00:00:00
Tasks: BIG-bench Machine Learning, Sentence

In this article we study verbal expression of aggression and its detection
using machine learning and neural networks methods. We test our results using
our corpora of messages from anonymous imageboards. We also compare Random
forest classifier with convolutional neural network for "Movie reviews with one
sentence per review" corpus.
Automatic verbal aggression detection for Russian and American imageboards
Authors: Denis Gordeev
Date: 2016-04-22 00:00:00
Tasks: BIG-bench Machine Learning

The problem of aggression for Internet communities is rampant. Anonymous
forums usually called imageboards are notorious for their aggressive and
deviant behaviour even in comparison with other Internet communities. This
study is aimed at studying ways of automatic detection of verbal expression of
aggression for the most popular American (4chan.org) and Russian (2ch.hk)
imageboards. A set of 1,802,789 messages was used for this study. The machine
learning algorithm word2vec was applied to detect the state of aggression. A
decent result is obtained for English (88%), the results for Russian are yet to
be improved.
Synthetic Data for Text Localisation in Natural Images
Authors: Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
Date: 2016-04-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU, object-detection, Object Detection, regression, Text Detection

In this paper we introduce a new method for text detection in natural images.
The method comprises two contributions: First, a fast and scalable engine to
generate synthetic images of text in clutter. This engine overlays synthetic
text to existing background images in a natural way, accounting for the local
3D scene geometry. Second, we use the synthetic images to train a
Fully-Convolutional Regression Network (FCRN) which efficiently performs text
detection and bounding-box regression at all locations and multiple scales in
an image. We discuss the relation of FCRN to the recently-introduced YOLO
detector, as well as other end-to-end object detection systems based on deep
learning. The resulting detection network significantly out performs current
methods for text detection in natural images, achieving an F-measure of 84.2%
on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per
second on a GPU.
Compact-Table: Efficiently Filtering Table Constraints with Reversible Sparse Bit-Sets
Authors: Jordan Demeulenaere, Renaud Hartert, Christophe Lecoutre, Guillaume Perez, Laurent Perron, Jean-Charles Régin, Pierre Schaus
Date: 2016-04-22 00:00:00
Tasks: ARC

In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce
Generalized Arc Consistency (GAC) on table con- straints. Although this
algorithm is the default propagator for table constraints in or-tools and
OscaR, two publicly available CP solvers, it has never been described so far.
Importantly, CT has been recently improved further with the introduction of
residues, resetting operations and a data-structure called reversible sparse
bit-set, used to maintain tables of supports (following the idea of tabular
reduction): tuples are invalidated incrementally on value removals by means of
bit-set operations. The experimentation that we have conducted with OscaR shows
that CT outperforms state-of-the-art algorithms STR2, STR3, GAC4R, MDD4R and
AC5-TC on standard benchmarks.
Bridging LSTM Architecture and the Neural Dynamics during Reading
Authors: Peng Qian, Xipeng Qiu, Xuanjing Huang
Date: 2016-04-22 00:00:00

Recently, the long short-term memory neural network (LSTM) has attracted wide
interest due to its success in many tasks. LSTM architecture consists of a
memory cell and three gates, which looks similar to the neuronal networks in
the brain. However, there still lacks the evidence of the cognitive
plausibility of LSTM architecture as well as its working mechanism. In this
paper, we study the cognitive plausibility of LSTM by aligning its internal
architecture with the brain activity observed via fMRI when the subjects read a
story. Experiment results show that the artificial memory vector in LSTM can
accurately predict the observed sequential brain activities, indicating the
correlation between LSTM architecture and the cognitive process of story
reading.
Agenda Separability in Judgment Aggregation
Authors: Jérôme Lang, Marija Slavkovik, Srdjan Vesic
Date: 2016-04-22 00:00:00

One of the better studied properties for operators in judgment aggregation is
independence, which essentially dictates that the collective judgment on one
issue should not depend on the individual judgments given on some other
issue(s) in the same agenda. Independence, although considered a desirable
property, is too strong, because together with mild additional conditions it
implies dictatorship. We propose here a weakening of independence, named agenda
separability: a judgment aggregation rule satisfies it if, whenever the agenda
is composed of several independent sub-agendas, the resulting collective
judgment sets can be computed separately for each sub-agenda and then put
together. We show that this property is discriminant, in the sense that among
judgment aggregation rules so far studied in the literature, some satisfy it
and some do not. We briefly discuss the implications of agenda separability on
the computation of judgment aggregation rules.
Recurrent Memory Networks for Language Modeling
Authors: Ke Tran, Arianna Bisazza, Christof Monz
Date: 2016-01-06 00:00:00
Proceeding: NAACL 2016 6
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Completion

Recurrent Neural Networks (RNN) have obtained excellent result in many
natural language processing (NLP) tasks. However, understanding and
interpreting the source of this success remains a challenge. In this paper, we
propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only
amplifies the power of RNN but also facilitates our understanding of its
internal functioning and allows us to discover underlying patterns in data. We
demonstrate the power of RMN on language modeling and sentence completion
tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM)
network on three large German, Italian, and English dataset. Additionally we
perform in-depth analysis of various linguistic dimensions that RMN captures.
On Sentence Completion Challenge, for which it is essential to capture sentence
coherence, our RMN obtains 69.2% accuracy, surpassing the previous
state-of-the-art by a large margin.
K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms
Authors: Aram Ter-Sarkisov, Stephen Marsland
Date: 2016-04-22 00:00:00
Tasks: Clustering, Evolutionary Algorithms

There has been a variety of crossover operators proposed for Real-Coded
Genetic Algorithms (RCGAs), which recombine values from the same location in
pairs of strings. In this article we present a recombination operator for RC-
GAs that selects the locations randomly in both parents, and compare it to
mainstream crossover operators in a set of experiments on a range of standard
multidimensional optimization problems and a clustering problem. We present two
variants of the operator, either selecting both bits uniformly at random in the
strings, or sampling the second bit from a normal distribution centered at the
selected location in the first string. While the operator is biased towards
exploitation of fitness space, the random selection of the second bit for swap-
ping makes it slightly less exploitation-biased. Extensive statistical analysis
using a non-parametric test shows the advantage of the new recombination
operators on a range of test functions.
Sentiment Analysis of Twitter Data: A Survey of Techniques
Authors: Vishal. A. Kharde, Prof. Sheetal. Sonawane
Date: 2016-01-26 00:00:00
Tasks: BIG-bench Machine Learning, Opinion Mining, Sentiment Analysis, Survey

With the advancement of web technology and its growth, there is a huge volume
of data present in the web for internet users and a lot of data is generated
too. Internet has become a platform for online learning, exchanging ideas and
sharing opinions. Social networking sites like Twitter, Facebook, Google+ are
rapidly gaining popularity as they allow people to share and express their
views about topics,have discussion with different communities, or post messages
across the world. There has been lot of work in the field of sentiment analysis
of twitter data. This survey focuses mainly on sentiment analysis of twitter
data which is helpful to analyze the information in the tweets where opinions
are highly unstructured, heterogeneous and are either positive or negative, or
neutral in some cases. In this paper, we provide a survey and a comparative
analyses of existing techniques for opinion mining like machine learning and
lexicon-based approaches, together with evaluation metrics. Using various
machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector
Machine, we provide a research on twitter data streams.General challenges and
applications of Sentiment Analysis on Twitter are also discussed in this paper.
SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
Authors: Elena Volodina, Ildikó Pilán, Ingegerd Enström, Lorena Llozhi, Peter Lundkvist, Gunlög Sundberg, Monica Sandell
Date: 2016-04-22 00:00:00
Proceeding: LREC 2016 5

We present a new resource for Swedish, SweLL, a corpus of Swedish Learner
essays linked to learners' performance according to the Common European
Framework of Reference (CEFR). SweLL consists of three subcorpora - SpIn,
SW1203 and Tisus, collected from three different educational establishments.
The common metadata for all subcorpora includes age, gender, native languages,
time of residence in Sweden, type of written task. Depending on the subcorpus,
learner texts may contain additional information, such as text genres, topics,
grades. Five of the six CEFR levels are represented in the corpus: A1, A2, B1,
B2 and C1 comprising in total 339 essays. C2 level is not included since
courses at C2 level are not offered. The work flow consists of collection of
essays and permits, essay digitization and registration, meta-data annotation,
automatic linguistic annotation. Inter-rater agreement is presented on the
basis of SW1203 subcorpus. The work on SweLL is still ongoing with more than
100 essays waiting in the pipeline. This article both describes the resource
and the "how-to" behind the compilation of SweLL.
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Authors: Fereshteh Asgari, Alexis Sultan, Haoyi Xiong, Vincent Gauthier, Mounim El-Yacoubi
Date: 2016-04-22 00:00:00

Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.
A Classifier-guided Approach for Top-down Salient Object Detection
Authors: Hisham Cholakkal, Jubin Johnson, Deepu Rajan
Date: 2016-04-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Segmentation, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation

We propose a framework for top-down salient object detection that
incorporates a tightly coupled image classification module. The classifier is
trained on novel category-aware sparse codes computed on object dictionaries
used for saliency modeling. A misclassification indicates that the
corresponding saliency model is inaccurate. Hence, the classifier selects
images for which the saliency models need to be updated. The category-aware
sparse coding produces better image classification accuracy as compared to
conventional sparse coding with a reduced computational complexity. A
saliency-weighted max-pooling is proposed to improve image classification,
which is further used to refine the saliency maps. Experimental results on
Graz-02 and PASCAL VOC-07 datasets demonstrate the effectiveness of salient
object detection. Although the role of the classifier is to support salient
object detection, we evaluate its performance in image classification and also
illustrate the utility of thresholded saliency maps for image segmentation.
Car Type Recognition with Deep Neural Networks
Authors: Heikki Huttunen, Fatemeh Shokrollahi Yancheshmeh, Ke Chen
Date: 2016-02-23 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper we study automatic recognition of cars of four types: Bus,
Truck, Van and Small car. For this problem we consider two data driven
frameworks: a deep neural network and a support vector machine using SIFT
features. The accuracy of the methods is validated with a database of over 6500
images, and the resulting prediction accuracy is over 97 %. This clearly
exceeds the accuracies of earlier studies that use manually engineered feature
extraction pipelines.
Neural Generative Question Answering
Authors: Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li
Date: 2015-12-04 00:00:00
Proceeding: WS 2016 6
Tasks: Decoder, Generative Question Answering, Question Answering, Text Generation

This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation
Authors: Bing Wang, Gang Wang, Kap Luk Chan, Li Wang
Date: 2015-11-20 00:00:00
Tasks: Metric Learning, Object

In this paper, we present a novel method based on online target-specific
metric learning and coherent dynamics estimation for tracklet (track fragment)
association by network flow optimization in long-term multi-person tracking.
Our proposed framework aims to exploit appearance and motion cues to prevent
identity switches during tracking and to recover missed detections.
Furthermore, target-specific metrics (appearance cue) and motion dynamics
(motion cue) are proposed to be learned and estimated online, i.e. during the
tracking process. Our approach is effective even when such cues fail to
identify or follow the target due to occlusions or object-to-object
interactions. We also propose to learn the weights of these two tracking cues
to handle the difficult situations, such as severe occlusions and
object-to-object interactions effectively. Our method has been validated on
several public datasets and the experimental results show that it outperforms
several state-of-the-art tracking methods.
Creation of a Deep Convolutional Auto-Encoder in Caffe
Authors: Volodymyr Turchenko, Artur Luczak
Date: 2015-12-04 00:00:00
Tasks: Dimensionality Reduction

The development of a deep (stacked) convolutional auto-encoder in the Caffe
deep learning framework is presented in this paper. We describe simple
principles which we used to create this model in Caffe. The proposed model of
convolutional auto-encoder does not have pooling/unpooling layers yet. The
results of our experimental research show comparable accuracy of dimensionality
reduction in comparison with a classic auto-encoder on the example of MNIST
dataset.
Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data
Authors: Fan Zhang, Patrick Flaherty
Date: 2016-04-14 00:00:00
Tasks: Specificity, Time Series, Time Series Analysis, Variational Inference

The detection of rare variants is important for understanding the genetic
heterogeneity in mixed samples. Recently, next-generation sequencing (NGS)
technologies have enabled the identification of single nucleotide variants
(SNVs) in mixed samples with high resolution. Yet, the noise inherent in the
biological processes involved in next-generation sequencing necessitates the
use of statistical methods to identify true rare variants. We propose a novel
Bayesian statistical model and a variational expectation-maximization (EM)
algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs
in heterogeneous cell populations. We demonstrate that our variational EM
algorithm has comparable sensitivity and specificity compared with a Markov
Chain Monte Carlo (MCMC) sampling inference algorithm, and is more
computationally efficient on tests of low coverage ($27\times$ and $298\times$)
data. Furthermore, we show that our model with a variational EM inference
algorithm has higher specificity than many state-of-the-art algorithms. In an
analysis of a directed evolution longitudinal yeast data set, we are able to
identify a time-series trend in non-reference allele frequency and detect novel
variants that have not yet been reported. Our model also detects the emergence
of a beneficial variant earlier than was previously shown, and a pair of
concomitant variants.
Improving Human Action Recognition by Non-action Classification
Authors: Yang Wang, Minh Hoai
Date: 2016-04-21 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Classification, Action Recognition, Classification, General Classification, Temporal Action Localization

In this paper we consider the task of recognizing human actions in realistic
video where human actions are dominated by irrelevant factors. We first study
the benefits of removing non-action video segments, which are the ones that do
not portray any human action. We then learn a non-action classifier and use it
to down-weight irrelevant video segments. The non-action classifier is trained
using ActionThread, a dataset with shot-level annotation for the occurrence or
absence of a human action. The non-action classifier can be used to identify
non-action shots with high precision and subsequently used to improve the
performance of action recognition systems.
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
Authors: Yong Cheng, Shiqi Shen, Zhongjun He, wei he, Hua Wu, Maosong Sun, Yang Liu
Date: 2015-12-15 00:00:00
Tasks: Machine Translation, Translation, Word Alignment

The attentional mechanism has proven to be effective in improving end-to-end
neural machine translation. However, due to the intricate structural divergence
between natural languages, unidirectional attention-based models might only
capture partial aspects of attentional regularities. We propose agreement-based
joint training for bidirectional attention-based end-to-end neural machine
translation. Instead of training source-to-target and target-to-source
translation models independently,our approach encourages the two complementary
models to agree on word alignment matrices on the same training data.
Experiments on Chinese-English and English-French translation tasks show that
agreement-based joint training significantly improves both alignment and
translation quality over independent training.
The DARPA Twitter Bot Challenge
Authors: V. S. Subrahmanian, Amos Azaria, Skylar Durst, Vadim Kagan, Aram Galstyan, Kristina Lerman, Linhong Zhu, Emilio Ferrara, Alessandro Flammini, Filippo Menczer, Andrew Stevens, Alexander Dekhtyar, Shuyang Gao, Tad Hogg, Farshad Kooti, Yan Liu, Onur Varol, Prashant Shiralkar, Vinod Vydiswaran, Qiaozhu Mei, Tim Hwang
Date: 2016-01-20 00:00:00

A number of organizations ranging from terrorist groups such as ISIS to
politicians and nation states reportedly conduct explicit campaigns to
influence opinion on social media, posing a risk to democratic processes. There
is thus a growing need to identify and eliminate "influence bots" - realistic,
automated identities that illicitly shape discussion on sites like Twitter and
Facebook - before they get too influential. Spurred by such events, DARPA held
a 4-week competition in February/March 2015 in which multiple teams supported
by the DARPA Social Media in Strategic Communications program competed to
identify a set of previously identified "influence bots" serving as ground
truth on a specific topic within Twitter. Past work regarding influence bots
often has difficulty supporting claims about accuracy, since there is limited
ground truth (though some exceptions do exist [3,7]). However, with the
exception of [3], no past work has looked specifically at identifying influence
bots on a specific topic. This paper describes the DARPA Challenge and
describes the methods used by the three top-ranked teams.