pwc-archive/papers-with-abstracts huggingface dataset:
Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives
Authors: Krishnendu Chatterjee, Adrián Elgyütt, Petr Novotný, Owen Rouillé
Date: 2018-04-27 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Partially-observable Markov decision processes (POMDPs) with discounted-sum
payoff are a standard framework to model a wide range of problems related to
decision making under uncertainty. Traditionally, the goal has been to obtain
policies that optimize the expectation of the discounted-sum payoff. A key
drawback of the expectation measure is that even low probability events with
extreme payoff can significantly affect the expectation, and thus the obtained
policies are not necessarily risk-averse. An alternate approach is to optimize
the probability that the payoff is above a certain threshold, which allows
obtaining risk-averse policies, but ignores optimization of the expectation. We
consider the expectation optimization with probabilistic guarantee (EOPG)
problem, where the goal is to optimize the expectation ensuring that the payoff
is above a given threshold with at least a specified probability. We present
several results on the EOPG problem, including the first algorithm to solve it.
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
Authors: Thulasi Tholeti, Vishnu Raj, Sheetal Kalyani
Date: 2018-04-30 00:00:00

Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (SUs) connecting to the cognitive radio network is on the rise, there is an imminent need for centralized algorithms that provide high throughput and energy efficiency of the SUs while ensuring minimum interference to the licensed users. In this work, we propose a multi-stage algorithm that - 1) effectively assigns the available channel to the SUs, 2) employs a non-parametric learning framework to estimate the primary traffic distribution to minimize sensing, and 3) proposes an adaptive framework to ensure that the collision to the primary user is below the specified threshold. We provide comprehensive empirical validation of the method with other approaches.
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
Authors: Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon
Date: 2018-04-30 00:00:00
Tasks: Hyperspectral Unmixing

Spectral variability is one of the major issue when conducting hyperspectral
unmixing. Within a given image composed of some elementary materials (herein
referred to as endmember classes), the spectral signature characterizing these
classes may spatially vary due to intrinsic component fluctuations or external
factors (illumination). These redundant multiple endmember spectra within each
class adversely affect the performance of unmixing methods. This paper proposes
a mixing model that explicitly incorporates a hierarchical structure of
redundant multiple spectra representing each class. The proposed method is
designed to promote sparsity on the selection of both spectra and classes
within each pixel. The resulting unmixing algorithm is able to adaptively
recover several bundles of endmember spectra associated with each class and
robustly estimate abundances. In addition, its flexibility allows a variable
number of classes to be present within each pixel of the hyperspectral image to
be unmixed. The proposed method is compared with other state-of-the-art
unmixing methods that incorporate sparsity using both simulated and real
hyperspectral data. The results show that the proposed method can successfully
determine the variable number of classes present within each class and estimate
the corresponding class abundances.
Optimal Neural Network Feature Selection for Spatial-Temporal Forecasting
Authors: Eurico Covas, Emmanouil Benetos
Date: 2018-04-30 00:00:00
Tasks: feature selection

In this paper, we show empirical evidence on how to construct the optimal
feature selection or input representation used by the input layer of a
feedforward neural network for the propose of forecasting spatial-temporal
signals. The approach is based on results from dynamical systems theory, namely
the non-linear embedding theorems. We demonstrate it for a variety of
spatial-temporal signals, with one spatial and one temporal dimensions, and
show that the optimal input layer representation consists of a grid, with
spatial/temporal lags determined by the minimum of the mutual information of
the spatial/temporal signals and the number of points taken in space/time
decided by the embedding dimension of the signal. We present evidence of this
proposal by running a Monte Carlo simulation of several combinations of input
layer feature designs and show that the one predicted by the non-linear
embedding theorems seems to be optimal or close of optimal. In total we show
evidence in four unrelated systems: a series of coupled Henon maps; a series of
couple Ordinary Differential Equations (Lorenz-96) phenomenologically modelling
atmospheric dynamics; the Kuramoto-Sivashinsky equation, a partial differential
equation used in studies of instabilities in laminar flame fronts and finally
real physical data from sunspot areas in the Sun (in latitude and time) from
1874 to 2015.
Investigations on End-to-End Audiovisual Fusion
Authors: Michael Wand, Ngoc Thang Vu, Juergen Schmidhuber
Date: 2018-04-30 00:00:00
Tasks: speech-recognition, Speech Recognition

Audiovisual speech recognition (AVSR) is a method to alleviate the adverse
effect of noise in the acoustic signal. Leveraging recent developments in deep
neural network-based speech recognition, we present an AVSR neural network
architecture which is trained end-to-end, without the need to separately model
the process of decision fusion as in conventional (e.g. HMM-based) systems. The
fusion system outperforms single-modality recognition under all noise
conditions. Investigation of the saliency of the input features shows that the
neural network automatically adapts to different noise levels in the acoustic
signal.
OMG - Emotion Challenge Solution
Authors: Yuqi Cui, Xiao Zhang, Yang Wang, Chenfeng Guo, Dongrui Wu
Date: 2018-04-30 00:00:00
Tasks: regression

This short paper describes our solution to the 2018 IEEE World Congress on
Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge,
whose goal was to estimate continuous arousal and valence values from short
videos. We designed four base regression models using visual and audio
features, and then used a spectral approach to fuse them to obtain improved
performance.
Demand-Weighted Completeness Prediction for a Knowledge Base
Authors: Andrew Hopkinson, Amit Gurdasani, Dave Palfrey, Arpit Mittal
Date: 2018-04-30 00:00:00
Proceeding: NAACL 2018 6
Tasks: Prediction

In this paper we introduce the notion of Demand-Weighted Completeness,
allowing estimation of the completeness of a knowledge base with respect to how
it is used. Defining an entity by its classes, we employ usage data to predict
the distribution over relations for that entity. For example, instances of
person in a knowledge base may require a birth date, name and nationality to be
considered complete. These predicted relation distributions enable detection of
important gaps in the knowledge base, and define the required facts for unseen
entities. Such characterisation of the knowledge base can also quantify how
usage and completeness change over time. We demonstrate a method to measure
Demand-Weighted Completeness, and show that a simple neural network model
performs well at this prediction task.
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge
Authors: Asan Agibetov, Matthias Samwald
Date: 2018-04-30 00:00:00
Tasks: Entity Embeddings, Link Prediction, Representation Learning

In this work we address the problem of fast and scalable learning of
neuro-symbolic representations for general biological knowledge. Based on a
recently published comprehensive biological knowledge graph (Alshahrani, 2017)
that was used for demonstrating neuro-symbolic representation learning, we show
how to train fast (under 1 minute) log-linear neural embeddings of the
entities. We utilize these representations as inputs for machine learning
classifiers to enable important tasks such as biological link prediction.
Classifiers are trained by concatenating learned entity embeddings to represent
entity relations, and training classifiers on the concatenated embeddings to
discern true relations from automatically generated negative examples. Our
simple embedding methodology greatly improves on classification error compared
to previously published state-of-the-art results, yielding a maximum increase
of $+0.28$ F-measure and $+0.22$ ROC AUC scores for the most difficult
biological link prediction problem. Finally, our embedding approach is orders
of magnitude faster to train ($\leq$ 1 minute vs. hours), much more economical
in terms of embedding dimensions ($d=50$ vs. $d=512$), and naturally encodes
the directionality of the asymmetric biological relations, that can be
controlled by the order with which we concatenate the embeddings.
An Evolutionary Computing Enriched RS Attack Resilient Medical Image Steganography Model for Telemedicine Applications
Authors: Romany F. Mansour, Elsaid MD. Abdelrahim
Date: 2017-09-25 00:00:00
Tasks: Image Steganography

The recent advancement in computing technologies and resulting vision based
applications have gives rise to a novel practice called telemedicine that
requires patient diagnosis images or allied information to recommend or even
perform diagnosis practices being located remotely. However, to ensure accurate
and optimal telemedicine there is the requirement of seamless or flawless
biomedical information about patient. On the contrary, medical data transmitted
over insecure channel often remains prone to get manipulated or corrupted by
attackers. The existing cryptosystems alone are not sufficient to deal with
these issues and hence in this paper a highly robust reversible image
steganography model has been developed for secret information hiding. Unlike
traditional wavelet transform techniques, we incorporated Discrete Ripplet
Transformation (DRT) technique for message embedding in the medical cover
images. In addition, to assure seamless communication over insecure channel, a
dual cryptosystem model containing proposed steganography scheme and RSA
cryptosystem has been developed. One of the key novelties of the proposed
research work is the use of adaptive genetic algorithm (AGA) for optimal pixel
adjustment process (OPAP) that enriches data hiding capacity as well as
imperceptibility features. The performance assessment reveals that the proposed
steganography model outperforms other wavelet transformation based approaches
in terms of high PSNR, embedding capacity, imperceptibility etc.
K-means clustering for efficient and robust registration of multi-view point sets
Authors: Zutao Jiang, Jihua Zhu, Georgios D. Evangelidis, Changqing Zhang, Shanmin Pang, Yaochen Li
Date: 2017-10-14 00:00:00
Tasks: Clustering

Generally, there are three main factors that determine the practical
usability of registration, i.e., accuracy, robustness, and efficiency. In
real-time applications, efficiency and robustness are more important. To
promote these two abilities, we cast the multi-view registration into a
clustering task. All the centroids are uniformly sampled from the initially
aligned point sets involved in the multi-view registration, which makes it
rather efficient and effective for the clustering. Then, each point is assigned
to a single cluster and each cluster centroid is updated accordingly.
Subsequently, the shape comprised by all cluster centroids is used to
sequentially estimate the rigid transformation for each point set. For accuracy
and stability, clustering and transformation estimation are alternately and
iteratively applied to all point sets. We tested our proposed approach on
several benchmark datasets and compared it with state-of-the-art approaches.
Experimental results validate its efficiency and robustness for the
registration of multi-view point sets.
Staircase Network: structural language identification via hierarchical attentive units
Authors: Trung Ngo Trong, Ville Hautamäki, Kristiina Jokinen
Date: 2018-04-30 00:00:00
Tasks: General Classification, Language Identification

Language recognition system is typically trained directly to optimize
classification error on the target language labels, without using the external,
or meta-information in the estimation of the model parameters. However labels
are not independent of each other, there is a dependency enforced by, for
example, the language family, which affects negatively on classification. The
other external information sources (e.g. audio encoding, telephony or video
speech) can also decrease classification accuracy. In this paper, we attempt to
solve these issues by constructing a deep hierarchical neural network, where
different levels of meta-information are encapsulated by attentive prediction
units and also embedded into the training progress. The proposed method learns
auxiliary tasks to obtain robust internal representation and to construct a
variant of attentive units within the hierarchical model. The final result is
the structural prediction of the target language and a closely related language
family. The algorithm reflects a "staircase" way of learning in both its
architecture and training, advancing from the fundamental audio encoding to the
language family level and finally to the target language level. This process
not only improves generalization but also tackles the issues of imbalanced
class priors and channel variability in the deep neural network model. Our
experimental findings show that the proposed architecture outperforms the
state-of-the-art i-vector approaches on both small and big language corpora by
a significant margin.
The Case for Learned Index Structures
Authors: Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis
Date: 2017-12-04 00:00:00
Tasks: Management, Position

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
position of a record within a sorted array, a Hash-Index as a model to map a
key to a position of a record within an unsorted array, and a BitMap-Index as a
model to indicate if a data record exists or not. In this exploratory research
paper, we start from this premise and posit that all existing index structures
can be replaced with other types of models, including deep-learning models,
which we term learned indexes. The key idea is that a model can learn the sort
order or structure of lookup keys and use this signal to effectively predict
the position or existence of records. We theoretically analyze under which
conditions learned indexes outperform traditional index structures and describe
the main challenges in designing learned index structures. Our initial results
show, that by using neural nets we are able to outperform cache-optimized
B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
several real-world data sets. More importantly though, we believe that the idea
of replacing core components of a data management system through learned models
has far reaching implications for future systems designs and that this work
just provides a glimpse of what might be possible.
An Ensemble Generation Method Based on Instance Hardness
Authors: Felipe N. Walmsley, George D. C. Cavalcanti, Dayvid V. R. Oliveira, Rafael M. O. Cruz, Robert Sabourin
Date: 2018-04-20 00:00:00

In Machine Learning, ensemble methods have been receiving a great deal of
attention. Techniques such as Bagging and Boosting have been successfully
applied to a variety of problems. Nevertheless, such techniques are still
susceptible to the effects of noise and outliers in the training data. We
propose a new method for the generation of pools of classifiers based on
Bagging, in which the probability of an instance being selected during the
resampling process is inversely proportional to its instance hardness, which
can be understood as the likelihood of an instance being misclassified,
regardless of the choice of classifier. The goal of the proposed method is to
remove noisy data without sacrificing the hard instances which are likely to be
found on class boundaries. We evaluate the performance of the method in
nineteen public data sets, and compare it to the performance of the Bagging and
Random Subspace algorithms. Our experiments show that in high noise scenarios
the accuracy of our method is significantly better than that of Bagging.
Equivalent Lipschitz surrogates for zero-norm and rank optimization problems
Authors: Yulan Liu, Shujun Bi, Shaohua Pan
Date: 2018-04-30 00:00:00

This paper proposes a mechanism to produce equivalent Lipschitz surrogates
for zero-norm and rank optimization problems by means of the global exact
penalty for their equivalent mathematical programs with an equilibrium
constraint (MPECs). Specifically, we reformulate these combinatorial problems
as equivalent MPECs by the variational characterization of the zero-norm and
rank function, show that their penalized problems, yielded by moving the
equilibrium constraint into the objective, are the global exact penalization,
and obtain the equivalent Lipschitz surrogates by eliminating the dual variable
in the global exact penalty. These surrogates, including the popular SCAD
function in statistics, are also difference of two convex functions (D.C.) if
the function and constraint set involved in zero-norm and rank optimization
problems are convex. We illustrate an application by designing a multi-stage
convex relaxation approach to the rank plus zero-norm regularized minimization
problem.
Non-Intrusive Signature Extraction for Major Residential Loads
Authors: M. Dong, P. C. M. Meira, W. Xu, C. Y. Chung
Date: 2018-04-30 00:00:00

The data collected by smart meters contain a lot of useful information. One
potential use of the data is to track the energy consumptions and operating
statuses of major home appliances.The results will enable homeowners to make
sound decisions on how to save energy and how to participate in demand response
programs. This paper presents a new method to breakdown the total power demand
measured by a smart meter to those used by individual appliances. A unique
feature of the proposed method is that it utilizes diverse signatures
associated with the entire operating window of an appliance for identification.
As a result, appliances with complicated middle process can be tracked. A novel
appliance registration device and scheme is also proposed to automate the
creation of appliance signature database and to eliminate the need of massive
training before identification. The software and system have been developed and
deployed to real houses in order to verify the proposed method.
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
Authors: Evair Severo, Rayson Laroca, Cides S. Bezerra, Luiz A. Zanlorensi, Daniel Weingaertner, Gladston Moreira, David Menotti
Date: 2018-03-03 00:00:00
Tasks: Iris Recognition

The iris is considered as the biometric trait with the highest unique
probability. The iris location is an important task for biometrics systems,
affecting directly the results obtained in specific applications such as iris
recognition, spoofing and contact lenses detection, among others. This work
defines the iris location problem as the delimitation of the smallest squared
window that encompasses the iris region. In order to build a benchmark for iris
location we annotate (iris squared bounding boxes) four databases from
different biometric applications and make them publicly available to the
community. Besides these 4 annotated databases, we include 2 others from the
literature. We perform experiments on these six databases, five obtained with
near infra-red sensors and one with visible light sensor. We compare the
classical and outstanding Daugman iris location approach with two window based
detectors: 1) a sliding window detector based on features from Histogram of
Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier;
2) a deep learning based detector fine-tuned from YOLO object detector.
Experimental results showed that the deep learning based detector outperforms
the other ones in terms of accuracy and runtime (GPUs version) and should be
chosen whenever possible.
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
Authors: Chong Zhang, Geok Soon Hong, Jun-Hong Zhou, Kay Chen Tan, Haizhou Li, Huan Xu, Jihoon Hong, Hian-Leng Chan
Date: 2018-04-30 00:00:00
Tasks: Fault Diagnosis, Prognosis, Representation Learning, State Estimation

In this paper, a multi-state diagnosis and prognosis (MDP) framework is
proposed for tool condition monitoring via a deep belief network based
multi-state approach (DBNMS). For fault diagnosis, a cost-sensitive deep belief
network (namely ECS-DBN) is applied to deal with the imbalanced data problem
for tool state estimation. An appropriate prognostic degradation model is then
applied for tool wear estimation based on the different tool states. The
proposed framework has the advantage of automatic feature representation
learning and shows better performance in accuracy and robustness. The
effectiveness of the proposed DBNMS is validated using a real-world dataset
obtained from the gun drilling process. This dataset contains a large amount of
measured signals involving different tool geometries under various operating
conditions. The DBNMS is examined for both the tool state estimation and tool
wear estimation tasks. In the experimental studies, the prediction results are
evaluated and compared with popular machine learning approaches, which show the
superior performance of the proposed DBNMS approach.
Automatic Documentation of ICD Codes with Far-Field Speech Recognition
Authors: Albert Haque, Corinna Fukushima
Date: 2018-04-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Documentation errors increase healthcare costs and cause unnecessary patient
deaths. As the standard language for diagnoses and billing, ICD codes serve as
the foundation for medical documentation worldwide. Despite the prevalence of
electronic medical records, hospitals still witness high levels of ICD
miscoding. In this paper, we propose to automatically document ICD codes with
far-field speech recognition. Far-field speech occurs when the microphone is
located several meters from the source, as is common with smart homes and
security systems. Our method combines acoustic signal processing with recurrent
neural networks to recognize and document ICD codes in real time. To evaluate
our model, we collected a far-field speech dataset of ICD-10 codes and found
our model to achieve 87% accuracy with a BLEU score of 85%. By sampling from an
unsupervised medical language model, our method is able to outperform existing
methods. Overall, this work shows the potential of automatic speech recognition
to provide efficient, accurate, and cost-effective healthcare documentation.