pwc-archive/papers-with-abstracts huggingface dataset:
Making the Dynamic Time Warping Distance Warping-Invariant
Authors: Brijnesh Jain
Date: 2019-03-04 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

The literature postulates that the dynamic time warping (dtw) distance can
cope with temporal variations but stores and processes time series in a form as
if the dtw-distance cannot cope with such variations. To address this
inconsistency, we first show that the dtw-distance is not warping-invariant.
The lack of warping-invariance contributes to the inconsistency mentioned above
and to a strange behavior. To eliminate these peculiarities, we convert the
dtw-distance to a warping-invariant semi-metric, called time-warp-invariant
(twi) distance. Empirical results suggest that the error rates of the twi and
dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense.
However, the twi-distance requires less storage and computation time than the
dtw-distance for a broad range of problems. These results challenge the current
practice of applying the dtw-distance in nearest-neighbor classification and
suggest the proposed twi-distance as a more efficient and consistent option.
1D Convolutional Neural Network Models for Sleep Arousal Detection
Authors: Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Simo Särkkä, Moncef Gabbouj
Date: 2019-03-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Arousal Detection, Sleep Quality

Sleep arousals transition the depth of sleep to a more superficial stage. The
occurrence of such events is often considered as a protective mechanism to
alert the body of harmful stimuli. Thus, accurate sleep arousal detection can
lead to an enhanced understanding of the underlying causes and influencing the
assessment of sleep quality. Previous studies and guidelines have suggested
that sleep arousals are linked mainly to abrupt frequency shifts in EEG
signals, but the proposed rules are shown to be insufficient for a
comprehensive characterization of arousals. This study investigates the
application of five recent convolutional neural networks (CNNs) for sleep
arousal detection and performs comparative evaluations to determine the best
model for this task. The investigated state-of-the-art CNN models have
originally been designed for image or speech processing. A detailed set of
evaluations is performed on the benchmark dataset provided by
PhysioNet/Computing in Cardiology Challenge 2018, and the results show that the
best 1D CNN model has achieved an average of 0.31 and 0.84 for the area under
the precision-recall and area under the ROC curves, respectively.
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
Authors: Bryse Flowers, R. Michael Buehrer, William C. Headley
Date: 2019-03-01 00:00:00
Tasks: BIG-bench Machine Learning

Recent advancements in radio frequency machine learning (RFML) have
demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple
spectrum sensing tasks. Yet, deep learning techniques have been shown, in other
applications, to be vulnerable to adversarial machine learning (ML) techniques,
which seek to craft small perturbations that are added to the input to cause a
misclassification. The current work differentiates the threats that adversarial
ML poses to RFML systems based on where the attack is executed from: direct
access to classifier input, synchronously transmitted over the air (OTA), or
asynchronously transmitted from a separate device. Additionally, the current
work develops a methodology for evaluating adversarial success in the context
of wireless communications, where the primary metric of interest is bit error
rate and not human perception, as is the case in image recognition. The
methodology is demonstrated using the well known Fast Gradient Sign Method to
evaluate the vulnerabilities of raw IQ based Automatic Modulation
Classification and concludes RFML is vulnerable to adversarial examples, even
in OTA attacks. However, RFML domain specific receiver effects, which would be
encountered in an OTA attack, can present significant impairments to
adversarial evasion.
Genetic-Gated Networks for Deep Reinforcement
Authors: Simyung Chang, John Yang, Jae-Seok Choi, Nojun Kwak
Date: 2018-11-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that
combine a gate vector composed of binary genetic genes in the hidden layer(s)
of networks. Our method can take both advantages of gradient-free optimization
and gradient-based optimization methods, of which the former is effective for
problems with multiple local minima, while the latter can quickly find local
minima. In addition, multiple chromosomes can define different models, making
it easy to construct multiple models and can be effectively applied to problems
that require multiple models. We show that this G2N can be applied to typical
reinforcement learning algorithms to achieve a large improvement in sample
efficiency and performance.
Evolutionary Construction of Convolutional Neural Networks
Authors: Marijn van Knippenberg, Vlado Menkovski, Sergio Consoli
Date: 2019-01-02 00:00:00
Tasks: Evolutionary Algorithms

Neuro-Evolution is a field of study that has recently gained significantly
increased traction in the deep learning community. It combines deep neural
networks and evolutionary algorithms to improve and/or automate the
construction of neural networks. Recent Neuro-Evolution approaches have shown
promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created
that efficiently compresses the input data in the first step, and a
convolutional neural network is created to classify the compressed data in the
second step. The creation of networks in both steps is guided by by an
evolutionary process, where new networks are constantly being generated by
mutating members of a collection of existing networks. Additionally, a method
is introduced that considers the trade-off between compression and information
loss of different convolutional autoencoders. This is used to select the
optimal convolutional autoencoder from among those evolved to compress the data
for the second step. The complete framework is implemented, tested on the
popular CIFAR-10 data set, and the results are discussed. Finally, a number of
possible directions for future work with this particular framework in mind are
considered, including opportunities to improve its efficiency and its
application in particular areas.
Deep Generative Design: Integration of Topology Optimization and Generative Models
Authors: Sangeun Oh, Yongsu Jung, Seongsin Kim, Ikjin Lee, Namwoo Kang
Date: 2019-03-01 00:00:00
Tasks: Anomaly Detection, Deep Learning, Diversity

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.
A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation
Authors: Abdelkerim Amari, Xiang Lin, Octavia A. Dobre, Ramachandran Venkatesan, Alex Alvarado
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We investigate the performance of a machine learning classification
technique, called the Parzen window, to mitigate the fiber nonlinearity in the
context of dispersion managed and dispersion unmanaged systems. The technique
is applied for detection at the receiver side, and deals with the non-Gaussian
nonlinear effects by designing improved decision boundaries. We also propose a
two-stage mitigation technique using digital back propagation and Parzen window
for dispersion unmanaged systems. In this case, digital back propagation
compensates for the deterministic nonlinearity and the Parzen window deals with
the stochastic nonlinear signal-noise interactions, which are not taken into
account by digital back propagation. A performance improvement up to 0:4 dB in
terms of Q factor is observed.
Detecting Target-Area Link-Flooding DDoS Attacks using Traffic Analysis and Supervised Learning
Authors: Mostafa Rezazad, Matthias R. Brust, Mohammad Akbari, Pascal Bouvry, Ngai-Man Cheung
Date: 2019-03-01 00:00:00
Tasks: BIG-bench Machine Learning

A novel class of extreme link-flooding DDoS (Distributed Denial of Service)
attacks is designed to cut off entire geographical areas such as cities and
even countries from the Internet by simultaneously targeting a selected set of
network links. The Crossfire attack is a target-area link-flooding attack,
which is orchestrated in three complex phases. The attack uses a massively
distributed large-scale botnet to generate low-rate benign traffic aiming to
congest selected network links, so-called target links. The adoption of benign
traffic, while simultaneously targeting multiple network links, makes detecting
the Crossfire attack a serious challenge. In this paper, we present analytical
and emulated results showing hitherto unidentified vulnerabilities in the
execution of the attack, such as a correlation between coordination of the
botnet traffic and the quality of the attack, and a correlation between the
attack distribution and detectability of the attack. Additionally, we
identified a warm-up period due to the bot synchronization. For attack
detection, we report results of using two supervised machine learning
approaches: Support Vector Machine (SVM) and Random Forest (RF) for
classification of network traffic to normal and abnormal traffic, i.e, attack
traffic. These machine learning models have been trained in various scenarios
using the link volume as the main feature set.
Extreme Learning Machine-Based Receiver for MIMO LED Communications
Authors: Dawei Gao, Qinghua Guo
Date: 2019-02-27 00:00:00

This work concerns receiver design for light-emitting diode (LED) multiple
input multiple output (MIMO) communications where the LED nonlinearity can
severely degrade the performance of communications. In this paper, we propose
an extreme learning machine (ELM) based receiver to jointly handle the LED
nonlinearity and cross-LED interference, and a circulant input weight matrix is
employed, which significantly reduces the complexity of the receiver with the
fast Fourier transform (FFT). It is demonstrated that the proposed receiver can
efficiently handle the LED nonlinearity and cross-LED interference.
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
Authors: Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga, Shanqing Cai
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language
for hardware-accelerated machine learning, suitable for both interactive
research and production. TensorFlow, which TensorFlow Eager extends, requires
users to represent computations as dataflow graphs; this permits compiler
optimizations and simplifies deployment but hinders rapid prototyping and
run-time dynamism. TensorFlow Eager eliminates these usability costs without
sacrificing the benefits furnished by graphs: It provides an imperative
front-end to TensorFlow that executes operations immediately and a JIT tracer
that translates Python functions composed of TensorFlow operations into
executable dataflow graphs. TensorFlow Eager thus offers a multi-stage
programming model that makes it easy to interpolate between imperative and
staged execution in a single package.
A Machine-learning Based Ensemble Method For Anti-patterns Detection
Authors: Antoine Barbez, Foutse khomh, Yann-Gaël Guéhéneuc
Date: 2019-01-29 00:00:00
Tasks: BIG-bench Machine Learning

Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. However, such algorithms require large sets of manually-produced training-data, which often limits their application in practice. In this paper, we present SMAD (SMart Aggregation of Anti-patterns Detectors), a machine-learning based ensemble method to aggregate various anti-patterns detection approaches on the basis of their internal detection rules. Thus, our method uses several detection tools to produce an improved prediction from a reasonable number of training examples. We implemented SMAD for the detection of two well known anti-patterns: God Class and Feature Envy. With the results of our experiments conducted on eight java projects, we show that: (1) our method clearly improves the so aggregated tools; (2) SMAD significantly outperforms other ensemble methods.
Grammar Based Directed Testing of Machine Learning Systems
Authors: Sakshi Udeshi, Sudipta Chattopadhyay
Date: 2019-02-26 00:00:00
Tasks: BIG-bench Machine Learning

The massive progress of machine learning has seen its application over a variety of domains in the past decade. But how do we develop a systematic, scalable and modular strategy to validate machine-learning systems? We present, to the best of our knowledge, the first approach, which provides a systematic test framework for machine-learning systems that accepts grammar-based inputs. Our OGMA approach automatically discovers erroneous behaviours in classifiers and leverages these erroneous behaviours to improve the respective models. OGMA leverages inherent robustness properties present in any well trained machine-learning model to direct test generation and thus, implementing a scalable test generation methodology. To evaluate our OGMA approach, we have tested it on three real world natural language processing (NLP) classifiers. We have found thousands of erroneous behaviours in these systems. We also compare OGMA with a random test generation approach and observe that OGMA is more effective than such random test generation by up to 489%.
Evolutionary framework for two-stage stochastic resource allocation problems
Authors: Pedro H. D. B. Hokama, Mário C. San Felice, Evandro C. Bracht, Fábio L. Usberti
Date: 2018-11-29 00:00:00
Tasks: Steiner Tree Problem, Vocal Bursts Valence Prediction

Resource allocation problems are a family of problems in which resources must
be selected to satisfy given demands. This paper focuses on the two-stage
stochastic generalization of resource allocation problems where future demands
are expressed in a finite number of possible scenarios. The goal is to select
cost effective resources to be acquired in the present time (first stage), and
to implement a complete solution for each scenario (second stage), while
minimizing the total expected cost of the choices in both stages.
  We propose an evolutionary framework for solving general two-stage stochastic
resource allocation problems. In each iteration of our framework, a local
search algorithm selects resources to be acquired in the first stage. A genetic
metaheuristic then completes the solutions for each scenario and relevant
information is passed onto the next iteration, thereby supporting the
acquisition of promising resources in the following first stage.
Experimentation on numerous instances of the two-stage stochastic Steiner tree
problem suggests that our evolutionary framework is powerful enough to address
large instances of a wide variety of two-stage stochastic resource allocation
problems.
Algorithms Inspired by Nature: A Survey
Authors: Pranshu Gupta
Date: 2018-12-13 00:00:00
Tasks: Combinatorial Optimization, Job Shop Scheduling, Scheduling, Survey

Nature is known to be the best optimizer. Natural processes most often than
not reach an optimal equilibrium. Scientists have always strived to understand
and model such processes.Thus, many algorithms exist today that are inspired by
nature. Many of these algorithms and heuristics can be used to solve problems
for which no polynomial time algorithms exist,such as Job Shop Scheduling and
many other Combinatorial Optimization problems. We will discuss some of these
algorithms and heuristics and how they help us solve complex problems of
practical importance.
Boundary Evolution Algorithm for SAT-NP
Authors: Zhaoyang Ai, Chaodong Fan, Yingjie Zhang, Huigui Rong, Ze'an Tian, Haibing Fu
Date: 2018-12-22 00:00:00

A boundary evolution Algorithm (BEA) is proposed by simultaneously taking
into account the bottom and the high-level crossover and mutation, ie., the
boundary of the hierarchical genetic algorithm. Operators and optimal
individuals based on optional annealing are designed. Based on the numerous
versions of genetic algorithm, the boundary evolution approach with crossover
and mutation has been tested on the SAT problem and compared with two competing
methods: a traditional genetic algorithm and another traditional hierarchical
genetic algorithm, and among some others. The results of the comparative
experiments in solving SAT problem have proved that the new hierarchical
genetic algorithm based on simulated annealing and optimal individuals (BEA)
can improve the success rate and convergence speed considerably for SAT problem
due to its avoidance of both divergence and loss of optimal individuals, and by
coronary, conducive to NP problem. Though more extensive comparisons are to be
made on more algorithms, the consideration of the boundary elasticity of
hierarchical genetic algorithm is an implication of evolutionary algorithm.
Chaotic Genetic Algorithm and The Effects of Entropy in Performance Optimization
Authors: Guillermo Fuertes, Manuel Vargas, Miguel Alfaro, Rodrigo Soto-Garrido, Jorge Sabattin, Maria Alejandra Peralta
Date: 2019-01-16 00:00:00

This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and
the importance of the entropy in the initial population. Inspired by chaos
theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic
Algorithm (GA). The algorithm modifies the parameters of the initial population
using chaotic series and then analyzes the entropy of such population. This
strategy exhibits the relationship between entropy and performance optimization
in complex search spaces. Our study includes the optimization of nine benchmark
functions using eight different chaotic maps for each of the benchmark
functions. The numerical experiment demonstrates a direct relation between
entropy and performance of the algorithm.
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction
Authors: Hao Xue Du Q. Huynh Mark Reynolds
Date: 2018-03-12 00:00:00
Proceeding: IEEE Winter Conference on Applications of Computer Vision 2018 3
Tasks: Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of
the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians
but ignore the scene layouts in pedestrian trajectory prediction. In this paper, a novel hierarchical LSTM-based network is proposed to consider both the influence of social
neighbourhood and scene layouts. Our SS-LSTM, which
stands for Social-Scene-LSTM, uses three different LSTMs
to capture person, social and scene scale information. We
also use a circular shape neighbourhood setting instead of
the traditional rectangular shape neighbourhood in the social scale. We evaluate our proposed method against two
baseline methods and a state-of-art technique on three public datasets. The results show that our method outperforms
other methods and that using circular shape neighbourhood
improves the prediction accuracy
“Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention
Authors: Irtiza Hasan, Francesco Setti, Theodore Tsesmelis, Alessio Del Bue, Marco Cristani, Fabio Galasso
Date: 2018-03-12 00:00:00
Proceeding: IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 3
Tasks: Head Pose Estimation, Pose Estimation, Trajectory Forecasting

In this paper we show the importance of the head
pose estimation in the task of trajectory forecasting. This
cue, when produced by an oracle and injected in a novel
socially-based energy minimization approach, allows to get
state-of-the-art performances on four different forecasting
benchmarks, without relying on additional information such
as expected destination and desired speed, which are supposed to be know beforehand for most of the current forecasting techniques. Our approach uses the head pose estimation for two aims: 1) to define a view frustum of attention, highlighting the people a given subject is more interested about, in order to avoid collisions; 2) to give a shorttime estimation of what would be the desired destination
point. Moreover, we show that when the head pose estimation is given by a real detector, though the performance
decreases, it still remains at the level of the top score forecasting systems
Activation Atlas
Authors: Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, Chris Olah
Date: 2019-03-06 00:00:00
Proceeding: Distill 2019 3
Tasks: General Classification, image-classification, Image Classification

By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts.