pwc-archive/papers-with-abstracts huggingface dataset:
On Inversely Proportional Hypermutations with Mutation Potential
Authors: Dogan Corus, Pietro S. Oliveto, Donya Yazdani
Date: 2019-03-27 00:00:00
Tasks: Evolutionary Algorithms

Artificial Immune Systems (AIS) employing hypermutations with linear static
mutation potential have recently been shown to be very effective at escaping
local optima of combinatorial optimisation problems at the expense of being
slower during the exploitation phase compared to standard evolutionary
algorithms. In this paper we prove that considerable speed-ups in the
exploitation phase may be achieved with dynamic inversely proportional mutation
potentials (IPM) and argue that the potential should decrease inversely to the
distance to the optimum rather than to the difference in fitness. Afterwards we
define a simple (1+1)~Opt-IA, that uses IPM hypermutations and ageing, for
realistic applications where optimal solutions are unknown. The aim of the AIS
is to approximate the ideal behaviour of the inversely proportional
hypermutations better and better as the search space is explored. We prove that
such desired behaviour, and related speed-ups, occur for a well-studied bimodal
benchmark function called \textsc{TwoMax}. Furthermore, we prove that the
(1+1)~Opt-IA with IPM efficiently optimises a third bimodal function,
\textsc{Cliff}, by escaping its local optima while Opt-IA with static potential
cannot, thus requires exponential expected runtime in the distance between the
cliff and the optimum.
Adversarial Deep Learning in EEG Biometrics
Authors: Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Date: 2019-03-27 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Person Identification, Representation Learning

Deep learning methods for person identification based on
electroencephalographic (EEG) brain activity encounters the problem of
exploiting the temporally correlated structures or recording session specific
variability within EEG. Furthermore, recent methods have mostly trained and
evaluated based on single session EEG data. We address this problem from an
invariant representation learning perspective. We propose an adversarial
inference approach to extend such deep learning models to learn
session-invariant person-discriminative representations that can provide
robustness in terms of longitudinal usability. Using adversarial learning
within a deep convolutional network, we empirically assess and show
improvements with our approach based on longitudinally collected EEG data for
person identification from half-second EEG epochs.
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment
Authors: Samyak Datta, Karan Sikka, Anirban Roy, Karuna Ahuja, Devi Parikh, Ajay Divakaran
Date: 2019-03-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Retrieval, Phrase Grounding, Retrieval, Visual Grounding

We address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image retrieval as a `downstream' task to guide the process of phrase localization. Our method, as a first step, infers the latent correspondences between regions-of-interest (RoIs) and phrases in the caption and creates a discriminative image representation using these matched RoIs. In a subsequent step, this (learned) representation is aligned with the caption. Our key contribution lies in building this `caption-conditioned' image encoding which tightly couples both the tasks and allows the weak supervision to effectively guide visual grounding. We provide an extensive empirical and qualitative analysis to investigate the different components of our proposed model and compare it with competitive baselines. For phrase localization, we report an improvement of 4.9% (absolute) over the prior state-of-the-art on the VisualGenome dataset. We also report results that are at par with the state-of-the-art on the downstream caption-to-image retrieval task on COCO and Flickr30k datasets.
Laplace Landmark Localization
Authors: Joseph P. Robinson, Yuncheng Li, Ning Zhang, Yun Fu, and Sergey Tulyakov
Date: 2019-03-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: CPU, Face Alignment, Facial Landmark Detection

Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data. Most efforts use network objectives based on L1 or L2 norms, which have several disadvantages. First of all, the locations of landmarks are determined from generated heatmaps (i.e., confidence maps) from which predicted landmark locations (i.e., the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. For this, we introduce a LaplaceKL objective that penalizes for a low confidence. Another issue is a dependency on labeled data, which are expensive to obtain and susceptible to error. To address both issues we propose an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims state-of-the-art on all of the 300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size: 1/8 the number of channels (i.e., 0.0398MB) is comparable to state-of-that-art in real-time on CPU. Thus, we show that our method is of high practical value to real-life application.
Bridging Adversarial Robustness and Gradient Interpretability
Authors: Beomsu Kim, Junghoon Seo, Taegyun Jeon
Date: 2019-03-27 00:00:00
Tasks: Adversarial Robustness

Adversarial training is a training scheme designed to counter adversarial
attacks by augmenting the training dataset with adversarial examples.
Surprisingly, several studies have observed that loss gradients from
adversarially trained DNNs are visually more interpretable than those from
standard DNNs. Although this phenomenon is interesting, there are only few
works that have offered an explanation. In this paper, we attempted to bridge
this gap between adversarial robustness and gradient interpretability. To this
end, we identified that loss gradients from adversarially trained DNNs align
better with human perception because adversarial training restricts gradients
closer to the image manifold. We then demonstrated that adversarial training
causes loss gradients to be quantitatively meaningful. Finally, we showed that
under the adversarial training framework, there exists an empirical trade-off
between test accuracy and loss gradient interpretability and proposed two
potential approaches to resolving this trade-off.
Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team
Authors: Nunzia Palmieri, Xin-She Yang, Floriano De Rango, Amilcare Francesco Santamaria
Date: 2019-03-27 00:00:00
Tasks: Decision Making

This work addresses the coordination problem of multiple robots with the goal
of finding specific hazardous targets in an unknown area and dealing with them
cooperatively. The desired behaviour for the robotic system entails multiple
requirements, which may also be conflicting. The paper presents the problem as
a constrained bi-objective optimization problem in which mobile robots must
perform two specific tasks of exploration and at same time cooperation and
coordination for disarming the hazardous targets. These objectives are opposed
goals, in which one may be favored, but only at the expense of the other.
Therefore, a good trade-off must be found. For this purpose, a nature-inspired
approach and an analytical mathematical model to solve this problem considering
a single equivalent weighted objective function are presented. The results of
proposed coordination model, simulated in a two dimensional terrain, are showed
in order to assess the behaviour of the proposed solution to tackle this
problem. We have analyzed the performance of the approach and the influence of
the weights of the objective function under different conditions: static and
dynamic. In this latter situation, the robots may fail under the stringent
limited budget of energy or for hazardous events. The paper concludes with a
critical discussion of the experimental results.
The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory
Authors: Si Chen, Guo-Hua Peng, Xing-Shi He, Xin-She Yang
Date: 2019-03-27 00:00:00

The bat algorithm (BA) has been shown to be effective to solve a wider range
of optimization problems. However, there is not much theoretical analysis
concerning its convergence and stability. In order to prove the convergence of
the bat algorithm, we have built a Markov model for the algorithm and proved
that the state sequence of the bat population forms a finite homogeneous Markov
chain, satisfying the global convergence criteria. Then, we prove that the bat
algorithm can have global convergence. In addition, in order to enhance the
convergence performance of the algorithm, we have designed an updated model
using the dynamical system theory in terms of a dynamic matrix, and the
parameter ranges for the algorithm stability are then obtained. We then use
some benchmark functions to demonstrate that BA can indeed achieve global
optimality efficiently for these functions.
A Simple Haploid-Diploid Evolutionary Algorithm
Authors: Larry Bull
Date: 2019-03-27 00:00:00

It has recently been suggested that evolution exploits a form of fitness
landscape smoothing within eukaryotic sex due to the haploid-diploid cycle.
This short paper presents a simple modification to the standard evolutionary
computing algorithm to similarly benefit from the process. Using the well-known
NK model of fitness landscapes it is shown that the benefit emerges as
ruggedness is increased.
Deep segmentation networks predict survival of non-small cell lung cancer
Authors: Stephen Baek, Yusen He, Bryan G. Allen, John M. Buatti, Brian J. Smith, Ling Tong, Zhiyu Sun, Jia Wu, Maximilian Diehn, Billy W. Loo, Kristin A. Plichta, Steven N. Seyedin, Maggie Gannon, Katherine R. Cabel, Yusung Kim, Xiaodong Wu
Date: 2019-03-26 00:00:00
Tasks: Prognosis, Segmentation, Tumor Segmentation

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new premise for cancer image analysis, with significantly enhanced predictive power compared to other hand-crafted radiomics features. Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET/CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-net algorithm has not seen any other clinical information (e.g. survival, age, smoking history) than the images and the corresponding tumor contours provided by physicians. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of progression appear to match with the regions where the U-Net features identified patterns that predicted higher likelihood of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination.
Novel Artificial Human Optimization Field Algorithms - The Beginning
Authors: Satish Gajawada, Hassan Mustafa
Date: 2019-03-26 00:00:00
Tasks: Articles

New Artificial Human Optimization (AHO) Field Algorithms can be created from
scratch or by adding the concept of Artificial Humans into other existing
Optimization Algorithms. Particle Swarm Optimization (PSO) has been very
popular for solving complex optimization problems due to its simplicity. In
this work, new Artificial Human Optimization Field Algorithms are created by
modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO
Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO
research articles based on Human Behavior, Human Cognition and Human Thinking
etc. But there are no Hybrid PSO articles which based on concepts like Human
Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field
algorithms based on these research gaps. Some existing Hybrid PSO algorithms
are given a new name in this work so that it will be easy for future AHO
researchers to find these novel Artificial Human Optimization Field Algorithms.
A total of 6 Artificial Human Optimization Field algorithms titled "Human
Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm
Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)",
"Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking
Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm
Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley,
Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results
obtained are compared with PSO algorithm.
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
Authors: Nameer Al Khafaf, Mahdi Jalili, Peter Sokolowski
Date: 2019-03-04 00:00:00
Tasks: Demand Forecasting, Management, Scheduling, Time Series, Time Series Analysis

The smart metering infrastructure has changed how electricity is measured in
both residential and industrial application. The large amount of data collected
by smart meter per day provides a huge potential for analytics to support the
operation of a smart grid, an example of which is energy demand forecasting.
Short term energy forecasting can be used by utilities to assess if any
forecasted peak energy demand would have an adverse effect on the power system
transmission and distribution infrastructure. It can also help in load
scheduling and demand side management. Many techniques have been proposed to
forecast time series including Support Vector Machine, Artificial Neural
Network and Deep Learning. In this work we use Long Short Term Memory
architecture to forecast 3-day ahead energy demand across each month in the
year. The results show that 3-day ahead demand can be accurately forecasted
with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper
proposes way to quantify the time as a feature to be used in the training phase
which is shown to affect the network performance.
Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques
Authors: İlhan Tarımer, Adil Çoban, Arif Emre Kocaman
Date: 2019-03-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Sentiment Analysis

This study's goal is to create a model of sentiment analysis on a 2000 rows
IMDB movie comments and 3200 Twitter data by using machine learning and vector
space techniques; positive or negative preliminary information about the text
is to provide. In the study, a vector space was created in the KNIME Analytics
platform, and a classification study was performed on this vector space by
Decision Trees, Na\"ive Bayes and Support Vector Machines classification
algorithms. The conclusions obtained were compared in terms of each algorithms.
The classification results for IMDB movie comments are obtained as 94,00%,
73,20%, and 85,50% by Decision Tree, Naive Bayes and SVM algorithms. The
classification results for Twitter data set are presented as 82,76%, 75,44% and
72,50% by Decision Tree, Naive Bayes SVM algorithms as well. It is seen that
the best classification results presented in both data sets are which
calculated by SVM algorithm.
The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes
Authors: Sebastian Spänig, Agnes Emberger-Klein, Jan-Peter Sowa, Ali Canbay, Klaus Menrad, Dominik Heider
Date: 2019-03-09 00:00:00
Tasks: Prognosis, speech-recognition, Speech Recognition, Speech Synthesis

Artificial intelligence (AI) will pave the way to a new era in medicine.
However, currently available AI systems do not interact with a patient, e.g.,
for anamnesis, and thus are only used by the physicians for predictions in
diagnosis or prognosis. However, these systems are widely used, e.g., in
diabetes or cancer prediction. In the current study, we developed an AI that is
able to interact with a patient (virtual doctor) by using a speech recognition
and speech synthesis system and thus can autonomously interact with the
patient, which is particularly important for, e.g., rural areas, where the
availability of primary medical care is strongly limited by low population
densities. As a proof-of-concept, the system is able to predict type 2 diabetes
mellitus (T2DM) based on non-invasive sensors and deep neural networks.
Moreover, the system provides an easy-to-interpret probability estimation for
T2DM for a given patient. Besides the development of the AI, we further
analyzed the acceptance of young people for AI in healthcare to estimate the
impact of such system in the future.
Comprehensive Analysis of Dynamic Message Sign Impact on Driver Behavior: A Random Forest Approach
Authors: Snehanshu Banerjee, Mansoureh Jeihani, Danny D. Brown, Samira Ahangari
Date: 2019-03-10 00:00:00

This study investigates the potential effects of different Dynamic Message
Signs (DMSs) on driver behavior using a full-scale high-fidelity driving
simulator. Different DMSs are categorized by their content, structure, and type
of messages. A random forest algorithm is used for three separate behavioral
analyses; a route diversion analysis, a route choice analysis and a compliance
analysis; to identify the potential and relative influences of different DMSs
on these aspects of driver behavior. A total of 390 simulation runs are
conducted using a sample of 65 participants from diverse socioeconomic
backgrounds. Results obtained suggest that DMSs displaying lane closure and
delay information with advisory messages are most influential with regards to
diversion while color-coded DMSs and DMSs with avoid route advice are the top
contributors impacting route choice decisions and DMS compliance. In this
first-of-a-kind study, based on the responses to the pre and post simulation
surveys as well as results obtained from the analysis of
driving-simulation-session data, the authors found that color-blind-friendly,
color-coded DMSs are more effective than alphanumeric DMSs - especially in
scenarios that demand high compliance from drivers. The increased effectiveness
may be attributed to reduced comprehension time and ease with which such DMSs
are understood by a greater percentage of road users.
Big Data Analytics and AI in Mental Healthcare
Authors: Ariel Rosenfeld, David Benrimoh, Caitrin Armstrong, Nykan Mirchi, Timothe Langlois-Therrien, Colleen Rollins, Myriam Tanguay-Sela, Joseph Mehltretter, Robert Fratila, Sonia Israel, Emily Snook, Kelly Perlman, Akiva Kleinerman, Bechara Saab, Mark Thoburn, Cheryl Gabbay, Amit Yaniv-Rosenfeld
Date: 2019-03-12 00:00:00

Mental health conditions cause a great deal of distress or impairment;
depression alone will affect 11% of the world's population. The application of
Artificial Intelligence (AI) and big-data technologies to mental health has
great potential for personalizing treatment selection, prognosticating,
monitoring for relapse, detecting and helping to prevent mental health
conditions before they reach clinical-level symptomatology, and even delivering
some treatments. However, unlike similar applications in other fields of
medicine, there are several unique challenges in mental health applications
which currently pose barriers towards the implementation of these technologies.
Specifically, there are very few widely used or validated biomarkers in mental
health, leading to a heavy reliance on patient and clinician derived
questionnaire data as well as interpretation of new signals such as digital
phenotyping. In addition, diagnosis also lacks the same objective 'gold
standard' as in other conditions such as oncology, where clinicians and
researchers can often rely on pathological analysis for confirmation of
diagnosis. In this chapter we discuss the major opportunities, limitations and
techniques used for improving mental healthcare through AI and big-data. We
explore both the computational, clinical and ethical considerations and best
practices as well as lay out the major researcher directions for the near
future.
Interpretation of machine learning predictions for patient outcomes in electronic health records
Authors: William La Cava, Christopher Bauer, Jason H. Moore, Sarah A Pendergrass
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning, Feature Importance

Electronic health records are an increasingly important resource for
understanding the interactions between patient health, environment, and
clinical decisions. In this paper we report an empirical study of predictive
modeling of several patient outcomes using three state-of-the-art machine
learning methods. Our primary goal is to validate the models by interpreting
the importance of predictors in the final models. Central to interpretation is
the use of feature importance scores, which vary depending on the underlying
methodology. In order to assess feature importance, we compared univariate
statistical tests, information-theoretic measures, permutation testing, and
normalized coefficients from multivariate logistic regression models. In
general we found poor correlation between methods in their assessment of
feature importance, even when their performance is comparable and relatively
good. However, permutation tests applied to random forest and gradient boosting
models showed the most agreement, and the importance scores matched the
clinical interpretation most frequently.