pwc-archive/papers-with-abstracts huggingface dataset:
K-sets+: a Linear-time Clustering Algorithm for Data Points with a Sparse Similarity Measure
Authors: Cheng-Shang Chang, Chia-Tai Chang, Duan-Shin Lee, Li-Heng Liou
Date: 2017-05-11 00:00:00
Tasks: Clustering, Stochastic Block Model

In this paper, we first propose a new iterative algorithm, called the K-sets+
algorithm for clustering data points in a semi-metric space, where the distance
measure does not necessarily satisfy the triangular inequality. We show that
the K-sets+ algorithm converges in a finite number of iterations and it retains
the same performance guarantee as the K-sets algorithm for clustering data
points in a metric space. We then extend the applicability of the K-sets+
algorithm from data points in a semi-metric space to data points that only have
a symmetric similarity measure. Such an extension leads to great reduction of
computational complexity. In particular, for an n * n similarity matrix with m
nonzero elements in the matrix, the computational complexity of the K-sets+
algorithm is O((Kn + m)I), where I is the number of iterations. The memory
complexity to achieve that computational complexity is O(Kn + m). As such, both
the computational complexity and the memory complexity are linear in n when the
n * n similarity matrix is sparse, i.e., m = O(n). We also conduct various
experiments to show the effectiveness of the K-sets+ algorithm by using a
synthetic dataset from the stochastic block model and a real network from the
WonderNetwork website.
A landmark-based algorithm for automatic pattern recognition and abnormality detection
Authors: S. Huzurbazar, Long Lee, Dongyang Kuang
Date: 2016-02-17 00:00:00
Tasks: Anomaly Detection, Template Matching

We study a class of mathematical and statistical algorithms with the aim of
establishing a computer-based framework for fast and reliable automatic
abnormality detection on landmark represented image templates. Under this
framework, we apply a landmark-based algorithm for finding a group average as
an estimator that is said to best represent the common features of the group in
study. This algorithm extracts information of momentum at each landmark through
the process of template matching. If ever converges, the proposed algorithm
produces a local coordinate system for each member of the observing group, in
terms of the residual momentum. We use a Bayesian approach on the collected
residual momentum representations for making inference. For illustration, we
apply this framework to a small database of brain images for detecting
structure abnormality. The brain structure changes identified by our framework
are highly consistent with studies in the literature.
On the role of words in the network structure of texts: application to authorship attribution
Authors: Camilo Akimushkin, Diego R. Amancio, Osvaldo N. Oliveira Jr
Date: 2017-05-11 00:00:00
Tasks: Authorship Attribution

Well-established automatic analyses of texts mainly consider frequencies of
linguistic units, e.g. letters, words and bigrams, while methods based on
co-occurrence networks consider the structure of texts regardless of the nodes
label (i.e. the words semantics). In this paper, we reconcile these distinct
viewpoints by introducing a generalized similarity measure to compare texts
which accounts for both the network structure of texts and the role of
individual words in the networks. We use the similarity measure for authorship
attribution of three collections of books, each composed of 8 authors and 10
books per author. High accuracy rates were obtained with typical values from
90% to 98.75%, much higher than with the traditional the TF-IDF approach for
the same collections. These accuracies are also higher than taking only the
topology of networks into account. We conclude that the different properties of
specific words on the macroscopic scale structure of a whole text are as
relevant as their frequency of appearance; conversely, considering the identity
of nodes brings further knowledge about a piece of text represented as a
network.
Dynamic Compositional Neural Networks over Tree Structure
Authors: Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Date: 2017-05-11 00:00:00
Tasks: Learning Semantic Representations

Tree-structured neural networks have proven to be effective in learning
semantic representations by exploiting syntactic information. In spite of their
success, most existing models suffer from the underfitting problem: they
recursively use the same shared compositional function throughout the whole
compositional process and lack expressive power due to inability to capture the
richness of compositionality. In this paper, we address this issue by
introducing the dynamic compositional neural networks over tree structure
(DC-TreeNN), in which the compositional function is dynamically generated by a
meta network. The role of meta-network is to capture the metaknowledge across
the different compositional rules and formulate them. Experimental results on
two typical tasks show the effectiveness of the proposed models.
Graph-based Predictable Feature Analysis
Authors: Björn Weghenkel, Asja Fischer, Laurenz Wiskott
Date: 2016-02-01 00:00:00
Tasks: Graph Embedding, Time Series, Time Series Analysis

We propose graph-based predictable feature analysis (GPFA), a new method for
unsupervised learning of predictable features from high-dimensional time
series, where high predictability is understood very generically as low
variance in the distribution of the next data point given the previous ones. We
show how this measure of predictability can be understood in terms of graph
embedding as well as how it relates to the information-theoretic measure of
predictive information in special cases. We confirm the effectiveness of GPFA
on different datasets, comparing it to three existing algorithms with similar
objectives---namely slow feature analysis, forecastable component analysis, and
predictable feature analysis---to which GPFA shows very competitive results.
An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background
Authors: Kumar S. Ray, Soma Chakraborty
Date: 2017-05-11 00:00:00
Tasks: Object, object-detection, Object Detection

This paper proposes a novel approach to create an automated visual
surveillance system which is very efficient in detecting and tracking moving
objects in a video captured by moving camera without any apriori information
about the captured scene. Separating foreground from the background is
challenging job in videos captured by moving camera as both foreground and
background information change in every consecutive frames of the image
sequence; thus a pseudo-motion is perceptive in background. In the proposed
algorithm, the pseudo-motion in background is estimated and compensated using
phase correlation of consecutive frames based on the principle of Fourier shift
theorem. Then a method is proposed to model an acting background from recent
history of commonality of the current frame and the foreground is detected by
the differences between the background model and the current frame. Further
exploiting the recent history of dissimilarities of the current frame, actual
moving objects are detected in the foreground. Next, a two-stepped
morphological operation is proposed to refine the object region for an optimum
object size. Each object is attributed by its centroid, dimension and three
highest peaks of its gray value histogram. Finally, each object is tracked
using Kalman filter based on its attributes. The major advantage of this
algorithm over most of the existing object detection and tracking algorithms is
that, it does not require initialization of object position in the first frame
or training on sample data to perform. Performance of the algorithm is tested
on benchmark videos containing variable background and very satisfiable results
is achieved. The performance of the algorithm is also comparable with some of
the state-of-the-art algorithms for object detection and tracking.
Learning activation functions from data using cubic spline interpolation
Authors: Simone Scardapane, Michele Scarpiniti, Danilo Comminiello, Aurelio Uncini
Date: 2016-05-18 00:00:00

Neural networks require a careful design in order to perform properly on a
given task. In particular, selecting a good activation function (possibly in a
data-dependent fashion) is a crucial step, which remains an open problem in the
research community. Despite a large amount of investigations, most current
implementations simply select one fixed function from a small set of
candidates, which is not adapted during training, and is shared among all
neurons throughout the different layers. However, neither two of these
assumptions can be supposed optimal in practice. In this paper, we present a
principled way to have data-dependent adaptation of the activation functions,
which is performed independently for each neuron. This is achieved by
leveraging over past and present advances on cubic spline interpolation,
allowing for local adaptation of the functions around their regions of use. The
resulting algorithm is relatively cheap to implement, and overfitting is
counterbalanced by the inclusion of a novel damping criterion, which penalizes
unwanted oscillations from a predefined shape. Experimental results validate
the proposal over two well-known benchmarks.
Building a Semantic Role Labelling System for Vietnamese
Authors: Thai-Hoang Pham, Xuan-Khoai Pham, Phuong Le-Hong
Date: 2017-05-11 00:00:00
Tasks: Sentence

Semantic role labelling (SRL) is a task in natural language processing which
detects and classifies the semantic arguments associated with the predicates of
a sentence. It is an important step towards understanding the meaning of a
natural language. There exists SRL systems for well-studied languages like
English, Chinese or Japanese but there is not any such system for the
Vietnamese language. In this paper, we present the first SRL system for
Vietnamese with encouraging accuracy. We first demonstrate that a simple
application of SRL techniques developed for English could not give a good
accuracy for Vietnamese. We then introduce a new algorithm for extracting
candidate syntactic constituents, which is much more accurate than the common
node-mapping algorithm usually used in the identification step. Finally, in the
classification step, in addition to the common linguistic features, we propose
novel and useful features for use in SRL. Our SRL system achieves an $F_1$
score of 73.53\% on the Vietnamese PropBank corpus. This system, including
software and corpus, is available as an open source project and we believe that
it is a good baseline for the development of future Vietnamese SRL systems.
Survey of Visual Question Answering: Datasets and Techniques
Authors: Akshay Kumar Gupta
Date: 2017-05-10 00:00:00
Tasks: Deep Learning, Question Answering, Survey, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (or VQA) is a new and exciting problem that
combines natural language processing and computer vision techniques. We present
a survey of the various datasets and models that have been used to tackle this
task. The first part of the survey details the various datasets for VQA and
compares them along some common factors. The second part of this survey details
the different approaches for VQA, classified into four types: non-deep learning
models, deep learning models without attention, deep learning models with
attention, and other models which do not fit into the first three. Finally, we
compare the performances of these approaches and provide some directions for
future work.
Cryptocurrency Portfolio Management with Deep Reinforcement Learning
Authors: Zhengyao Jiang, Jinjun Liang
Date: 2016-12-05 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Portfolio management is the decision-making process of allocating an amount
of fund into different financial investment products. Cryptocurrencies are
electronic and decentralized alternatives to government-issued money, with
Bitcoin as the best-known example of a cryptocurrency. This paper presents a
model-less convolutional neural network with historic prices of a set of
financial assets as its input, outputting portfolio weights of the set. The
network is trained with 0.7 years' price data from a cryptocurrency exchange.
The training is done in a reinforcement manner, maximizing the accumulative
return, which is regarded as the reward function of the network. Backtest
trading experiments with trading period of 30 minutes is conducted in the same
market, achieving 10-fold returns in 1.8 months' periods. Some recently
published portfolio selection strategies are also used to perform the same
back-tests, whose results are compared with the neural network. The network is
not limited to cryptocurrency, but can be applied to any other financial
markets.
Content-based Approach for Vietnamese Spam SMS Filtering
Authors: Thai-Hoang Pham, Phuong Le-Hong
Date: 2017-05-11 00:00:00

Short Message Service (SMS) spam is a serious problem in Vietnam because of
the availability of very cheap pre-paid SMS packages. There are some systems to
detect and filter spam messages for English, most of which use machine learning
techniques to analyze the content of messages and classify them. For
Vietnamese, there is some research on spam email filtering but none focused on
SMS. In this work, we propose the first system for filtering Vietnamese spam
SMS. We first propose an appropriate preprocessing method since existing tools
for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
experiment with vector representations and classifiers to find the best model
for this problem. Our system achieves an accuracy of 94% when labelling spam
messages while the misclassification rate of legitimate messages is relatively
small, about only 0.4%. This is an encouraging result compared to that of
English and can be served as a strong baseline for future development of
Vietnamese SMS spam prevention systems.
Mining Functional Modules by Multiview-NMF of Phenome-Genome Association
Authors: YaoGong Zhang, YingJie Xu, Xin Fan, YuXiang Hong, Jiahui Liu, ZhiCheng He, YaLou Huang, MaoQiang Xie
Date: 2017-05-11 00:00:00
Tasks: Clustering

Background: Mining gene modules from genomic data is an important step to
detect gene members of pathways or other relations such as protein-protein
interactions. In this work, we explore the plausibility of detecting gene
modules by factorizing gene-phenotype associations from a phenotype ontology
rather than the conventionally used gene expression data. In particular, the
hierarchical structure of ontology has not been sufficiently utilized in
clustering genes while functionally related genes are consistently associated
with phenotypes on the same path in the phenotype ontology. Results: We propose
a hierarchal Nonnegative Matrix Factorization (NMF)-based method, called
Consistent Multiple Nonnegative Matrix Factorization (CMNMF), to factorize
genome-phenome association matrix at two levels of the hierarchical structure
in phenotype ontology for mining gene functional modules. CMNMF constrains the
gene clusters from the association matrices at two consecutive levels to be
consistent since the genes are annotated with both the child phenotype and the
parent phenotype in the consecutive levels. CMNMF also restricts the identified
phenotype clusters to be densely connected in the phenotype ontology hierarchy.
In the experiments on mining functionally related genes from mouse phenotype
ontology and human phenotype ontology, CMNMF effectively improved clustering
performance over the baseline methods. Gene ontology enrichment analysis was
also conducted to reveal interesting gene modules. Conclusions: Utilizing the
information in the hierarchical structure of phenotype ontology, CMNMF can
identify functional gene modules with more biological significance than the
conventional methods. CMNMF could also be a better tool for predicting members
of gene pathways and protein-protein interactions. Availability:
https://github.com/nkiip/CMNMF
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
Authors: Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao
Date: 2017-05-11 00:00:00
Proceeding: ACL 2017 7
Tasks: General Classification, Relation, Relation Extraction

Distant supervision significantly reduces human efforts in building training
data for many classification tasks. While promising, this technique often
introduces noise to the generated training data, which can severely affect the
model performance. In this paper, we take a deep look at the application of
distant supervision in relation extraction. We show that the dynamic transition
matrix can effectively characterize the noise in the training data built by
distant supervision. The transition matrix can be effectively trained using a
novel curriculum learning based method without any direct supervision about the
noise. We thoroughly evaluate our approach under a wide range of extraction
scenarios. Experimental results show that our approach consistently improves
the extraction results and outperforms the state-of-the-art in various
evaluation scenarios.
Distribution of degrees of freedom over structure and motion of rigid bodies
Authors: Mieczysław A. Kłopotek
Date: 2017-05-11 00:00:00

This paper is concerned with recovery of motion and structure parameters from
multiframes under orthogonal projection when only points are traced. The main
question is how many points and/or how many frames are necessary for the task.
It is demonstrated that 3 frames and 3 points are the absolute minimum.
Closed-form solution is presented. Furthermore, it is shown that the task may
be linearized if either four points or four frames are available. It is
demonstrated that no increase in the number of points may lead to recovery of
structure and motion parameters from two frames only. It is shown that instead
the increase in the number of points may support the task of tracing the points
from frame to frame.
Predicting First Impressions with Deep Learning
Authors: Mel McCurrie, Fernando Beletti, Lucas Parzianello, Allen Westendorp, Samuel Anthony, Walter Scheirer
Date: 2016-10-25 00:00:00
Tasks: Attribute, Deep Learning

Describable visual facial attributes are now commonplace in human biometrics
and affective computing, with existing algorithms even reaching a sufficient
point of maturity for placement into commercial products. These algorithms
model objective facets of facial appearance, such as hair and eye color,
expression, and aspects of the geometry of the face. A natural extension, which
has not been studied to any great extent thus far, is the ability to model
subjective attributes that are assigned to a face based purely on visual
judgements. For instance, with just a glance, our first impression of a face
may lead us to believe that a person is smart, worthy of our trust, and perhaps
even our admiration - regardless of the underlying truth behind such
attributes. Psychologists believe that these judgements are based on a variety
of factors such as emotional states, personality traits, and other physiognomic
cues. But work in this direction leads to an interesting question: how do we
create models for problems where there is no ground truth, only measurable
behavior? In this paper, we introduce a new convolutional neural network-based
regression framework that allows us to train predictive models of crowd
behavior for social attribute assignment. Over images from the AFLW face
database, these models demonstrate strong correlations with human crowd
ratings.
GQ($λ$) Quick Reference and Implementation Guide
Authors: Adam White, Richard S. Sutton
Date: 2017-05-10 00:00:00

This document should serve as a quick reference for and guide to the
implementation of linear GQ($\lambda$), a gradient-based off-policy
temporal-difference learning algorithm. Explanation of the intuition and theory
behind the algorithm are provided elsewhere (e.g., Maei & Sutton 2010, Maei
2011). If you questions or concerns about the content in this document or the
attached java code please email Adam White (adam.white@ualberta.ca).
  The code is provided as part of the source files in the arXiv submission.
Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
Authors: Naiyun Zhou, Andrey Fedorov, Fiona Fennessy, Ron Kikinis, Yi Gao
Date: 2017-05-07 00:00:00
Tasks: Marketing, Prognosis, whole slide images

Histopathological assessments, including surgical resection and core needle
biopsy, are the standard procedures in the diagnosis of the prostate cancer.
Current interpretation of the histopathology images includes the determination
of the tumor area, Gleason grading, and identification of certain
prognosis-critical features. Such a process is not only tedious, but also prune
to intra/inter-observe variabilities. Recently, FDA cleared the marketing of
the first whole slide imaging system for digital pathology. This opens a new
era for the computer aided prostate image analysis and feature extraction based
on the digital histopathology images. In this work, we present an analysis
pipeline that includes localization of the cancer region, grading, area ratio
of different Gleason grades, and cytological/architectural feature extraction.
The proposed algorithm combines the human engineered feature extraction as well
as those learned by the deep neural network. Moreover, the entire pipeline is
implemented to directly operate on the whole slide images produced by the
digital scanners and is therefore potentially easy to translate into clinical
practices. The algorithm is tested on 368 whole slide images from the TCGA data
set and achieves an overall accuracy of 75% in differentiating Gleason 3+4 with
4+3 slides.
Multi-armed Bandit Problem with Known Trend
Authors: Djallel Bouneffouf, Raphaël Feraud
Date: 2015-08-28 00:00:00
Tasks: Active Learning

We consider a variant of the multi-armed bandit model, which we call
multi-armed bandit problem with known trend, where the gambler knows the shape
of the reward function of each arm but not its distribution. This new problem
is motivated by different online problems like active learning, music and
interface recommendation applications, where when an arm is sampled by the
model the received reward change according to a known trend. By adapting the
standard multi-armed bandit algorithm UCB1 to take advantage of this setting,
we propose the new algorithm named A-UCB that assumes a stochastic model. We
provide upper bounds of the regret which compare favourably with the ones of
UCB1. We also confirm that experimentally with different simulations
Why & When Deep Learning Works: Looking Inside Deep Learnings
Authors: Ronny Ronen
Date: 2017-05-10 00:00:00
Tasks: Deep Learning

The Intel Collaborative Research Institute for Computational Intelligence
(ICRI-CI) has been heavily supporting Machine Learning and Deep Learning
research from its foundation in 2012. We have asked six leading ICRI-CI Deep
Learning researchers to address the challenge of "Why & When Deep Learning
works", with the goal of looking inside Deep Learning, providing insights on
how deep networks function, and uncovering key observations on their
expressiveness, limitations, and potential. The output of this challenge
resulted in five papers that address different facets of deep learning. These
different facets include a high-level understating of why and when deep
networks work (and do not work), the impact of geometry on the expressiveness
of deep networks, and making deep networks interpretable.