pwc-archive/papers-with-abstracts huggingface dataset:
Active exploration in parameterized reinforcement learning
Authors: Mehdi Khamassi, Costas Tzafestas
Date: 2016-10-06 00:00:00
Tasks: Meta-Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online model-free reinforcement learning (RL) methods with continuous actions
are playing a prominent role when dealing with real-world applications such as
Robotics. However, when confronted to non-stationary environments, these
methods crucially rely on an exploration-exploitation trade-off which is rarely
dynamically and automatically adjusted to changes in the environment. Here we
propose an active exploration algorithm for RL in structured (parameterized)
continuous action space. This framework deals with a set of discrete actions,
each of which is parameterized with continuous variables. Discrete exploration
is controlled through a Boltzmann softmax function with an inverse temperature
$\beta$ parameter. In parallel, a Gaussian exploration is applied to the
continuous action parameters. We apply a meta-learning algorithm based on the
comparison between variations of short-term and long-term reward running
averages to simultaneously tune $\beta$ and the width of the Gaussian
distribution from which continuous action parameters are drawn. When applied to
a simple virtual human-robot interaction task, we show that this algorithm
outperforms continuous parameterized RL both without active exploration and
with active exploration based on uncertainty variations measured by a
Kalman-Q-learning algorithm.
Polynomial-time Tensor Decompositions with Sum-of-Squares
Authors: Tengyu Ma, Jonathan Shi, David Steurer
Date: 2016-10-06 00:00:00
Tasks: Tensor Decomposition

We give new algorithms based on the sum-of-squares method for tensor
decomposition. Our results improve the best known running times from
quasi-polynomial to polynomial for several problems, including decomposing
random overcomplete 3-tensors and learning overcomplete dictionaries with
constant relative sparsity. We also give the first robust analysis for
decomposing overcomplete 4-tensors in the smoothed analysis model. A key
ingredient of our analysis is to establish small spectral gaps in moment
matrices derived from solutions to sum-of-squares relaxations. To enable this
analysis we augment sum-of-squares relaxations with spectral analogs of maximum
entropy constraints.
DeepDGA: Adversarially-Tuned Domain Generation and Detection
Authors: Hyrum S. Anderson, Jonathan Woodbridge, Bobby Filar
Date: 2016-10-06 00:00:00
Tasks: BIG-bench Machine Learning, Decoder, Deep Learning, Generative Adversarial Network

Many malware families utilize domain generation algorithms (DGAs) to
establish command and control (C&C) connections. While there are many methods
to pseudorandomly generate domains, we focus in this paper on detecting (and
generating) domains on a per-domain basis which provides a simple and flexible
means to detect known DGA families. Recent machine learning approaches to DGA
detection have been successful on fairly simplistic DGAs, many of which produce
names of fixed length. However, models trained on limited datasets are somewhat
blind to new DGA variants.
  In this paper, we leverage the concept of generative adversarial networks to
construct a deep learning based DGA that is designed to intentionally bypass a
deep learning based detector. In a series of adversarial rounds, the generator
learns to generate domain names that are increasingly more difficult to detect.
In turn, a detector model updates its parameters to compensate for the
adversarially generated domains. We test the hypothesis of whether
adversarially generated domains may be used to augment training sets in order
to harden other machine learning models against yet-to-be-observed DGAs. We
detail solutions to several challenges in training this character-based
generative adversarial network (GAN). In particular, our deep learning
architecture begins as a domain name auto-encoder (encoder + decoder) trained
on domains in the Alexa one million. Then the encoder and decoder are
reassembled competitively in a generative adversarial network (detector +
generator), with novel neural architectures and training strategies to improve
convergence.
Efficient L1-Norm Principal-Component Analysis via Bit Flipping
Authors: Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Dimitris A. Pados
Date: 2016-10-06 00:00:00
Tasks: Dimensionality Reduction

It was shown recently that the $K$ L1-norm principal components (L1-PCs) of a
real-valued data matrix $\mathbf X \in \mathbb R^{D \times N}$ ($N$ data
samples of $D$ dimensions) can be exactly calculated with cost
$\mathcal{O}(2^{NK})$ or, when advantageous, $\mathcal{O}(N^{dK - K + 1})$
where $d=\mathrm{rank}(\mathbf X)$, $K<d$ [1],[2]. In applications where
$\mathbf X$ is large (e.g., "big" data of large $N$ and/or "heavy" data of
large $d$), these costs are prohibitive. In this work, we present a novel
suboptimal algorithm for the calculation of the $K < d$ L1-PCs of $\mathbf X$
of cost $\mathcal O(ND \mathrm{min} \{ N,D\} + N^2(K^4 + dK^2) + dNK^3)$, which
is comparable to that of standard (L2-norm) PC analysis. Our theoretical and
experimental studies show that the proposed algorithm calculates the exact
optimal L1-PCs with high frequency and achieves higher value in the L1-PC
optimization metric than any known alternative algorithm of comparable
computational cost. The superiority of the calculated L1-PCs over standard
L2-PCs (singular vectors) in characterizing potentially faulty
data/measurements is demonstrated with experiments on data dimensionality
reduction and disease diagnosis from genomic data.
Sequence-based Sleep Stage Classification using Conditional Neural Fields
Authors: Intan Nurma Yulita, Mohamad Ivan Fanany, Aniati Murni Arymurthy
Date: 2016-10-06 00:00:00
Tasks: Classification, Clustering, General Classification

Sleep signals from a polysomnographic database are sequences in nature.
Commonly employed analysis and classification methods, however, ignored this
fact and treated the sleep signals as non-sequence data. Treating the sleep
signals as sequences, this paper compared two powerful unsupervised feature
extractors and three sequence-based classifiers regarding accuracy and
computational (training and testing) time after 10-folds cross-validation. The
compared feature extractors are Deep Belief Networks (DBN) and Fuzzy C-Means
(FCM) clustering. Whereas the compared sequence-based classifiers are Hidden
Markov Models (HMM), Conditional Random Fields (CRF) and its variants, i.e.,
Hidden-state CRF (HCRF) and Latent-Dynamic CRF (LDCRF); and Conditional Neural
Fields (CNF) and its variant (LDCNF). In this study, we use two datasets. The
first dataset is an open (public) polysomnographic dataset downloadable from
the Internet, while the second dataset is our polysomnographic dataset (also
available for download). For the first dataset, the combination of FCM and CNF
gives the highest accuracy (96.75\%) with relatively short training time (0.33
hours). For the second dataset, the combination of DBN and CRF gives the
accuracy of 99.96\% but with 1.02 hours training time, whereas the combination
of DBN and CNF gives slightly less accuracy (99.69\%) but also less computation
time (0.89 hours).
Metaheuristic Algorithms for Convolution Neural Network
Authors: L. M. Rasdi Rere, Mohamad Ivan Fanany, Aniati Murni Arymurthy
Date: 2016-10-06 00:00:00

A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN), a famous deep learning method, is still
rarely investigated. Deep learning relates to a type of machine learning
technique, where its aim is to move closer to the goal of artificial
intelligence of creating a machine that could successfully perform any
intellectual tasks that can be carried out by a human. In this paper, we
propose the implementation strategy of three popular metaheuristic approaches,
that is, simulated annealing, differential evolution, and harmony search, to
optimize CNN. The performances of these metaheuristic methods in optimizing CNN
on classifying MNIST and CIFAR dataset were evaluated and compared.
Furthermore, the proposed methods are also compared with the original CNN.
Although the proposed methods show an increase in the computation time, their
accuracy has also been improved (up to 7.14 percent).
Adaptive Online Sequential ELM for Concept Drift Tackling
Authors: Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin
Date: 2016-10-06 00:00:00
Tasks: General Classification, regression

A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.
Toward Automatic Understanding of the Function of Affective Language in Support Groups
Authors: Amit Navindgi, Caroline Brun, Cécile Boulard Masson, Scott Nowson
Date: 2016-10-06 00:00:00
Tasks: Position

Understanding expressions of emotions in support forums has considerable
value and NLP methods are key to automating this. Many approaches
understandably use subjective categories which are more fine-grained than a
straightforward polarity-based spectrum. However, the definition of such
categories is non-trivial and, in fact, we argue for a need to incorporate
communicative elements even beyond subjectivity. To support our position, we
report experiments on a sentiment-labelled corpus of posts taken from a medical
support forum. We argue that not only is a more fine-grained approach to text
analysis important, but simultaneously recognising the social function behind
affective expressions enable a more accurate and valuable level of
understanding.
A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text
Authors: Sadikin Mujiono, Mohamad Ivan Fanany, Chan Basaruddin
Date: 2016-10-06 00:00:00
Tasks: Denoising, Sentence

One essential task in information extraction from the medical corpus is drug
name recognition. Compared with text sources come from other domains, the
medical text is special and has unique characteristics. In addition, the
medical text mining poses more challenges, e.g., more unstructured text, the
fast growing of new terms addition, a wide range of name variation for the same
drug. The mining is even more challenging due to the lack of labeled dataset
sources and external knowledge, as well as multiple token representations for a
single drug name that is more common in the real application setting. Although
many approaches have been proposed to overwhelm the task, some problems
remained with poor F-score performance (less than 0.75). This paper presents a
new treatment in data representation techniques to overcome some of those
challenges. We propose three data representation techniques based on the
characteristics of word distribution and word similarities as a result of word
embedding training. The first technique is evaluated with the standard NN
model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two
deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked
Denoising Encoders). The third technique represents the sentence as a sequence
that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term
Memory). In extracting the drug name entities, the third technique gives the
best F-score performance compared to the state of the art, with its average
F-score being 0.8645.
Neural-based Noise Filtering from Word Embeddings
Authors: Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Date: 2016-10-06 00:00:00
Proceeding: COLING 2016 12
Tasks: Denoising, Word Embeddings

Word embeddings have been demonstrated to benefit NLP tasks impressively.
Yet, there is room for improvement in the vector representations, because
current word embeddings typically contain unnecessary information, i.e., noise.
We propose two novel models to improve word embeddings by unsupervised
learning, in order to yield word denoising embeddings. The word denoising
embeddings are obtained by strengthening salient information and weakening
noise in the original word embeddings, based on a deep feed-forward neural
network filter. Results from benchmark tasks show that the filtered word
denoising embeddings outperform the original word embeddings.
LAYERS: Yet another Neural Network toolkit
Authors: Roberto Paredes, José-Miguel Benedí
Date: 2016-10-05 00:00:00

Layers is an open source neural network toolkit aim at providing an easy way
to implement modern neural networks. The main user target are students and to
this end layers provides an easy scriptting language that can be early adopted.
The user has to focus only on design details as network totpology and parameter
tunning.
Distortion Varieties
Authors: Joe Kileel, Zuzana Kukelova, Tomas Pajdla, Bernd Sturmfels
Date: 2016-10-06 00:00:00

The distortion varieties of a given projective variety are parametrized by
duplicating coordinates and multiplying them with monomials. We study their
degrees and defining equations. Exact formulas are obtained for the case of
one-parameter distortions. These are based on Chow polytopes and Gr\"obner
bases. Multi-parameter distortions are studied using tropical geometry. The
motivation for distortion varieties comes from multi-view geometry in computer
vision. Our theory furnishes a new framework for formulating and solving
minimal problems for camera models with image distortion.
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting
Authors: Muhammad Imran, Sanjay Chawla, Carlos Castillo
Date: 2016-10-06 00:00:00
Tasks: Clustering, Constrained Clustering, General Classification, Novel Concepts, Outlier Detection

An emerging challenge in the online classification of social media data
streams is to keep the categories used for classification up-to-date. In this
paper, we propose an innovative framework based on an Expert-Machine-Crowd
(EMC) triad to help categorize items by continuously identifying novel concepts
in heterogeneous data streams often riddled with outliers. We unify constrained
clustering and outlier detection by formulating a novel optimization problem:
COD-Means. We design an algorithm to solve the COD-Means problem and show that
COD-Means will not only help detect novel categories but also seamlessly
discover human annotation errors and improve the overall quality of the
categorization process. Experiments on diverse real data sets demonstrate that
our approach is both effective and efficient.
Parallel Large-Scale Attribute Reduction on Cloud Systems
Authors: Junbo Zhang, Tianrui Li, Yi Pan
Date: 2016-10-06 00:00:00
Tasks: Attribute, Cloud Computing, feature selection

The rapid growth of emerging information technologies and application
patterns in modern society, e.g., Internet, Internet of Things, Cloud Computing
and Tri-network Convergence, has caused the advent of the era of big data. Big
data contains huge values, however, mining knowledge from big data is a
tremendously challenging task because of data uncertainty and inconsistency.
Attribute reduction (also known as feature selection) can not only be used as
an effective preprocessing step, but also exploits the data redundancy to
reduce the uncertainty. However, existing solutions are designed 1) either for
a single machine that means the entire data must fit in the main memory and the
parallelism is limited; 2) or for the Hadoop platform which means that the data
have to be loaded into the distributed memory frequently and therefore become
inefficient. In this paper, we overcome these shortcomings for maximum
efficiency possible, and propose a unified framework for Parallel Large-scale
Attribute Reduction, termed PLAR, for big data analysis. PLAR consists of three
components: 1) Granular Computing (GrC)-based initialization: it converts a
decision table (i.e., original data representation) into a granularity
representation which reduces the amount of space and hence can be easily cached
in the distributed memory: 2) model-parallelism: it simultaneously evaluates
all feature candidates and makes attribute reduction highly parallelizable; 3)
data-parallelism: it computes the significance of an attribute in parallel
using a MapReduce-style manner. We implement PLAR with four representative
heuristic feature selection algorithms on Spark, and evaluate them on various
huge datasets, including UCI and astronomical datasets, finding our method's
advantages beyond existing solutions.
Searching Scenes by Abstracting Things
Authors: Svetlana Kordumova, Jan C. van Gemert, Cees G. M. Snoek, Arnold W. M. Smeulders
Date: 2016-10-06 00:00:00
Tasks: Attribute, Descriptive, Object

In this paper we propose to represent a scene as an abstraction of 'things'.
We start from 'things' as generated by modern object proposals, and we
investigate their immediately observable properties: position, size, aspect
ratio and color, and those only. Where the recent successes and excitement of
the field lie in object identification, we represent the scene composition
independent of object identities. We make three contributions in this work.
First, we study simple observable properties of 'things', and call it things
syntax. Second, we propose translating the things syntax in linguistic abstract
statements and study their descriptive effect to retrieve scenes. Thirdly, we
propose querying of scenes with abstract block illustrations and study their
effectiveness to discriminate among different types of scenes. The benefit of
abstract statements and block illustrations is that we generate them directly
from the images, without any learning beforehand as in the standard attribute
learning. Surprisingly, we show that even though we use the simplest of
features from 'things' layout and no learning at all, we can still retrieve
scenes reasonably well.
Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8
Authors: Ines Heidieni Ikasari, Vina Ayumi, Mohamad Ivan Fanany, Sidik Mulyono
Date: 2016-10-06 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

This study uses remote sensing technology that can provide information about
the condition of the earth's surface area, fast, and spatially. The study area
was in Karawang District, lying in the Northern part of West Java-Indonesia. We
address a paddy growth stages classification using LANDSAT 8 image data
obtained from multi-sensor remote sensing image taken in October 2015 to August
2016. This study pursues a fast and accurate classification of paddy growth
stages by employing multiple regularizations learning on some deep learning
methods such as DNN (Deep Neural Networks) and 1-D CNN (1-D Convolutional
Neural Networks). The used regularizations are Fast Dropout, Dropout, and Batch
Normalization. To evaluate the effectiveness, we also compared our method with
other machine learning methods such as (Logistic Regression, SVM, Random
Forest, and XGBoost). The data used are seven bands of LANDSAT-8 spectral data
samples that correspond to paddy growth stages data obtained from i-Sky (eye in
the sky) Innovation system. The growth stages are determined based on paddy
crop phenology profile from time series of LANDSAT-8 images. The classification
results show that MLP using multiple regularization Dropout and Batch
Normalization achieves the highest accuracy for this dataset.
Improved Knowledge Base Completion by Path-Augmented TransR Model
Authors: Wenhao Huang, Ge Li, Zhi Jin
Date: 2016-10-06 00:00:00
Tasks: Knowledge Base Completion, Link Prediction, Prediction, Relation

Knowledge base completion aims to infer new relations from existing
information. In this paper, we propose path-augmented TransR (PTransR) model to
improve the accuracy of link prediction. In our approach, we base PTransR model
on TransR, which is the best one-hop model at present. Then we regularize
TransR with information of relation paths. In our experiment, we evaluate
PTransR on the task of entity prediction. Experimental results show that
PTransR outperforms previous models.
The backtracking survey propagation algorithm for solving random K-SAT problems
Authors: Raffaele Marino, Giorgio Parisi, Federico Ricci-Tersenghi
Date: 2015-08-20 00:00:00
Tasks: Combinatorial Optimization

Discrete combinatorial optimization has a central role in many scientific
disciplines, however, for hard problems we lack linear time algorithms that
would allow us to solve very large instances. Moreover, it is still unclear
what are the key features that make a discrete combinatorial optimization
problem hard to solve. Here we study random K-satisfiability problems with
$K=3,4$, which are known to be very hard close to the SAT-UNSAT threshold,
where problems stop having solutions. We show that the backtracking survey
propagation algorithm, in a time practically linear in the problem size, is
able to find solutions very close to the threshold, in a region unreachable by
any other algorithm. All solutions found have no frozen variables, thus
supporting the conjecture that only unfrozen solutions can be found in linear
time, and that a problem becomes impossible to solve in linear time when all
solutions contain frozen variables.
Ischemic Stroke Identification Based on EEG and EOG using 1D Convolutional Neural Network and Batch Normalization
Authors: Endang Purnama Giri, Mohamad Ivan Fanany, Aniati Murni Arymurthy
Date: 2016-10-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In 2015, stroke was the number one cause of death in Indonesia. The majority
type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan.
For developing countries like Indonesia, the availability of CT-Scan is very
limited and still relatively expensive. Because of the availability, another
device that potential to diagnose stroke in Indonesia is EEG. Ischemic stroke
occurs because of obstruction that can make the cerebral blood flow (CBF) on a
person with stroke has become lower than CBF on a normal person (control) so
that the EEG signal have a deceleration. On this study, we perform the ability
of 1D Convolutional Neural Network (1DCNN) to construct classification model
that can distinguish the EEG and EOG stroke data from EEG and EOG control data.
To accelerate training process our model we use Batch Normalization. Involving
62 person data object and from leave one out the scenario with five times
repetition of measurement we obtain the average of accuracy 0.86 (F-Score
0.861) only at 200 epoch. This result is better than all over shallow and
popular classifiers as the comparator (the best result of accuracy 0.69 and
F-Score 0.72 ). The feature used in our study were only 24 handcrafted feature
with simple feature extraction process.