pwc-archive/papers-with-abstracts huggingface dataset:
Online Feature Ranking for Intrusion Detection Systems
Authors: Buse Gul Atli, Alexander Jung
Date: 2018-03-01 00:00:00
Tasks: feature selection, Incremental Learning, Intrusion Detection

Many current approaches to the design of intrusion detection systems apply
feature selection in a static, non-adaptive fashion. These methods often
neglect the dynamic nature of network data which requires to use adaptive
feature selection techniques. In this paper, we present a simple technique
based on incremental learning of support vector machines in order to rank the
features in real time within a streaming model for network data. Some
illustrative numerical experiments with two popular benchmark datasets show
that our approach allows to adapt to the changes in normal network behaviour
and novel attack patterns which have not been experienced before.
An Empirical Analysis of the Correlation of Syntax and Prosody
Authors: Arne Köhn, Timo Baumann, Oskar Dörfler
Date: 2018-06-15 00:00:00

The relation of syntax and prosody (the syntax--prosody interface) has been
an active area of research, mostly in linguistics and typically studied under
controlled conditions. More recently, prosody has also been successfully used
in the data-based training of syntax parsers. However, there is a gap between
the controlled and detailed study of the individual effects between syntax and
prosody and the large-scale application of prosody in syntactic parsing with
only a shallow analysis of the respective influences. In this paper, we close
the gap by investigating the significance of correlations of prosodic
realization with specific syntactic functions using linear mixed effects models
in a very large corpus of read-out German encyclopedic texts. Using this
corpus, we are able to analyze prosodic structuring performed by a diverse set
of speakers while they try to optimize factual content delivery. After
normalization by speaker, we obtain significant effects, e.g. confirming that
the subject function, as compared to the object function, has a positive effect
on pitch and duration of a word, but a negative effect on loudness.
MAGIX: Model Agnostic Globally Interpretable Explanations
Authors: Nikaash Puri, Piyush Gupta, Pratiksha Agarwal, Sukriti Verma, Balaji Krishnamurthy
Date: 2017-06-22 00:00:00
Tasks: BIG-bench Machine Learning, Marketing, model

Explaining the behavior of a black box machine learning model at the instance
level is useful for building trust. However, it is also important to understand
how the model behaves globally. Such an understanding provides insight into
both the data on which the model was trained and the patterns that it learned.
We present here an approach that learns if-then rules to globally explain the
behavior of black box machine learning models that have been used to solve
classification problems. The approach works by first extracting conditions that
were important at the instance level and then evolving rules through a genetic
algorithm with an appropriate fitness function. Collectively, these rules
represent the patterns followed by the model for decisioning and are useful for
understanding its behavior. We demonstrate the validity and usefulness of the
approach by interpreting black box models created using publicly available data
sets as well as a private digital marketing data set.
Improving width-based planning with compact policies
Authors: Miquel Junyent, Anders Jonsson, Vicenç Gómez
Date: 2018-06-15 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimal action selection in decision problems characterized by sparse,
delayed rewards is still an open challenge. For these problems, current deep
reinforcement learning methods require enormous amounts of data to learn
controllers that reach human-level performance. In this work, we propose a
method that interleaves planning and learning to address this issue. The
planning step hinges on the Iterated-Width (IW) planner, a state of the art
planner that makes explicit use of the state representation to perform
structured exploration. IW is able to scale up to problems independently of the
size of the state space. From the state-actions visited by IW, the learning
step estimates a compact policy, which in turn is used to guide the planning
step. The type of exploration used by our method is radically different than
the standard random exploration used in RL. We evaluate our method in simple
problems where we show it to have superior performance than the
state-of-the-art reinforcement learning algorithms A2C and Alpha Zero. Finally,
we present preliminary results in a subset of the Atari games suite.
Mining Rank Data
Authors: Sascha Henzgen, Eyke Hüllermeier
Date: 2018-06-15 00:00:00
Tasks: Form

The problem of frequent pattern mining has been studied quite extensively for
various types of data, including sets, sequences, and graphs. Somewhat
surprisingly, another important type of data, namely rank data, has received
very little attention in data mining so far. In this paper, we therefore
addresses the problem of mining rank data, that is, data in the form of
rankings (total orders) of an underlying set of items. More specifically, two
types of patterns are considered, namely frequent rankings and dependencies
between such rankings in the form of association rules. Algorithms for mining
frequent rankings and frequent closed rankings are proposed and tested
experimentally, using both synthetic and real data.
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection
Authors: Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Zhe Feng, Taufiq Hasan
Date: 2018-06-15 00:00:00
Tasks: Anomaly Detection

Automatic heart sound abnormality detection can play a vital role in the
early diagnosis of heart diseases, particularly in low-resource settings. The
state-of-the-art algorithms for this task utilize a set of Finite Impulse
Response (FIR) band-pass filters as a front-end followed by a Convolutional
Neural Network (CNN) model. In this work, we propound a novel CNN architecture
that integrates the front-end bandpass filters within the network using
time-convolution (tConv) layers, which enables the FIR filter-bank parameters
to become learnable. Different initialization strategies for the learnable
filters, including random parameters and a set of predefined FIR filter-bank
coefficients, are examined. Using the proposed tConv layers, we add constraints
to the learnable FIR filters to ensure linear and zero phase responses.
Experimental evaluations are performed on a balanced 4-fold cross-validation
task prepared using the PhysioNet/CinC 2016 dataset. Results demonstrate that
the proposed models yield superior performance compared to the state-of-the-art
system, while the linear phase FIR filterbank method provides an absolute
improvement of 9.54% over the baseline in terms of an overall accuracy metric.
Structured low-rank matrix learning: algorithms and applications
Authors: Pratik Jawanpuria, Bamdev Mishra
Date: 2017-04-24 00:00:00
Tasks: Matrix Completion, Multi-Task Learning

We consider the problem of learning a low-rank matrix, constrained to lie in
a linear subspace, and introduce a novel factorization for modeling such
matrices. A salient feature of the proposed factorization scheme is it
decouples the low-rank and the structural constraints onto separate factors. We
formulate the optimization problem on the Riemannian spectrahedron manifold,
where the Riemannian framework allows to develop computationally efficient
conjugate gradient and trust-region algorithms. Experiments on problems such as
standard/robust/non-negative matrix completion, Hankel matrix learning and
multi-task learning demonstrate the efficacy of our approach. A shorter version
of this work has been published in ICML'18.
Hierarchical Novelty Detection for Visual Object Recognition
Authors: Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generalized Zero-Shot Learning, Novelty Detection, Object, Object Recognition, Zero-Shot Learning

Deep neural networks have achieved impressive success in large-scale visual
object recognition tasks with a predefined set of classes. However, recognizing
objects of novel classes unseen during training still remains challenging. The
problem of detecting such novel classes has been addressed in the literature,
but most prior works have focused on providing simple binary or regressive
decisions, e.g., the output would be "known," "novel," or corresponding
confidence intervals. In this paper, we study more informative novelty
detection schemes based on a hierarchical classification framework. For an
object of a novel class, we aim for finding its closest super class in the
hierarchical taxonomy of known classes. To this end, we propose two different
approaches termed top-down and flatten methods, and their combination as well.
The essential ingredients of our methods are confidence-calibrated classifiers,
data relabeling, and the leave-one-out strategy for modeling novel classes
under the hierarchical taxonomy. Furthermore, our method can generate a
hierarchical embedding that leads to improved generalized zero-shot learning
performance in combination with other commonly-used semantic embeddings.
Automated Image Data Preprocessing with Deep Reinforcement Learning
Authors: Tran Ngoc Minh, Mathieu Sinn, Hoang Thanh Lam, Martin Wistuba
Date: 2018-06-15 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Data preparation, i.e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. For image data, preprocessing typically involves a sequence of basic transformations such as cropping, filtering, rotating or flipping images. Currently, data scientists decide manually based on their experience which transformations to apply in which particular order to a given image data set. Besides constituting a bottleneck in real-world data science projects, manual image data preprocessing may yield suboptimal results as data scientists need to rely on intuition or trial-and-error approaches when exploring the space of possible image transformations and thus might not be able to discover the most effective ones. To mitigate the inefficiency and potential ineffectiveness of manual data preprocessing, this paper proposes a deep reinforcement learning framework to automatically discover the optimal data preprocessing steps for training an image classifier. The framework takes as input sets of labeled images and predefined preprocessing transformations. It jointly learns the classifier and the optimal preprocessing transformations for individual images. Experimental results show that the proposed approach not only improves the accuracy of image classifiers, but also makes them substantially more robust to noisy inputs at test time.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Authors: Yang Yue, Liuyuan He, Gan He, Jian. K. Liu, Kai Du, Yonghong Tian, Tiejun Huang
Date: 2018-06-15 00:00:00
Tasks: Denoising, SSIM

Photoreceptors in the retina are coupled by electrical synapses called "gap
junctions". It has long been established that gap junctions increase the
signal-to-noise ratio of photoreceptors. Inspired by electrically coupled
photoreceptors, we introduced a simple filter, the PR-filter, with only one
variable. On BSD68 dataset, PR-filter showed outstanding performance in SSIM
during blind denoising tasks. It also significantly improved the performance of
state-of-the-art convolutional neural network blind denosing on non-Gaussian
noise. The performance of keeping more details might be attributed to small
receptive field of the photoreceptors.
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Authors: Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner
Date: 2018-06-14 00:00:00

Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be guided. To this end, we introduce neural stethoscopes as a general purpose framework for quantifying the degree of importance of specific factors of influence in deep neural networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We apply neural stethoscopes to analyse the state-of-the-art neural network for stability prediction. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.
Financial Risk and Returns Prediction with Modular Networked Learning
Authors: Carlos Pedro Gonçalves
Date: 2018-06-15 00:00:00
Tasks: Prediction

An artificial agent for financial risk and returns' prediction is built with
a modular cognitive system comprised of interconnected recurrent neural
networks, such that the agent learns to predict the financial returns, and
learns to predict the squared deviation around these predicted returns. These
two expectations are used to build a volatility-sensitive interval prediction
for financial returns, which is evaluated on three major financial indices and
shown to be able to predict financial returns with higher than 80% success rate
in interval prediction in both training and testing, raising into question the
Efficient Market Hypothesis. The agent is introduced as an example of a class
of artificial intelligent systems that are equipped with a Modular Networked
Learning cognitive system, defined as an integrated networked system of machine
learning modules, where each module constitutes a functional unit that is
trained for a given specific task that solves a subproblem of a complex main
problem expressed as a network of linked subproblems. In the case of neural
networks, these systems function as a form of an "artificial brain", where each
module is like a specialized brain region comprised of a neural network with a
specific architecture.
Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Authors: Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Date: 2018-06-15 00:00:00

Design optimization techniques are often used at the beginning of the design
process to explore the space of possible designs. In these domains illumination
algorithms, such as MAP-Elites, are promising alternatives to classic
optimization algorithms because they produce diverse, high-quality solutions in
a single run, instead of only a single near-optimal solution. Unfortunately,
these algorithms currently require a large number of function evaluations,
limiting their applicability. In this article we introduce a new illumination
algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate
modeling techniques to create a map of the design space according to
user-defined features while minimizing the number of fitness evaluations. On a
2-dimensional airfoil optimization problem SAIL produces hundreds of diverse
but high-performing designs with several orders of magnitude fewer evaluations
than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of
producing maps of high-performing designs in realistic 3-dimensional
aerodynamic tasks with an accurate flow simulation. Data-efficient design
exploration with SAIL can help designers understand what is possible, beyond
what is optimal, by considering more than pure objective-based optimization.
DeepLaser: Practical Fault Attack on Deep Neural Networks
Authors: Jakub Breier, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, Yang Liu
Date: 2018-06-15 00:00:00
Tasks: Autonomous Vehicles

As deep learning systems are widely adopted in safety- and security-critical
applications, such as autonomous vehicles, banking systems, etc., malicious
faults and attacks become a tremendous concern, which potentially could lead to
catastrophic consequences. In this paper, we initiate the first study of
leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by
using laser injection technique on embedded systems. In particular, our
exploratory study targets four widely used activation functions in DNNs
development, that are the general main building block of DNNs that creates
non-linear behaviors -- ReLu, softmax, sigmoid, and tanh. Our results show that
by targeting these functions, it is possible to achieve a misclassification by
injecting faults into the hidden layer of the network. Such result can have
practical implications for real-world applications, where faults can be
introduced by simpler means (such as altering the supply voltage).
Semantic Variation in Online Communities of Practice
Authors: Marco Del Tredici, Raquel Fernández
Date: 2018-06-15 00:00:00
Proceeding: WS 2017 1
Tasks: Language Modelling

We introduce a framework for quantifying semantic variation of common words
in Communities of Practice and in sets of topic-related communities. We show
that while some meaning shifts are shared across related communities, others
are community-specific, and therefore independent from the discussed topic. We
propose such findings as evidence in favour of sociolinguistic theories of
socially-driven semantic variation. Results are evaluated using an independent
language modelling task. Furthermore, we investigate extralinguistic features
and show that factors such as prominence and dissemination of words are related
to semantic variation.
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
Authors: Patrick Spettel, Hans-Georg Beyer, Michael Hellwig
Date: 2018-06-15 00:00:00

This paper addresses the development of a covariance matrix self-adaptation
evolution strategy (CMSA-ES) for solving optimization problems with linear
constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES
(lcCMSA-ES). It uses a specially built mutation operator together with repair
by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a
linear manifold defined by the constraints. The objective function is only
evaluated at feasible search points (interior point method). This is a property
often required in application domains such as simulation optimization and
finite element methods. The algorithm is tested on a variety of different test
problems revealing considerable results.
Co-Clustering via Information-Theoretic Markov Aggregation
Authors: Clemens Bloechl, Rana Ali Amjad, Bernhard C. Geiger
Date: 2018-01-02 00:00:00
Tasks: Clustering

We present an information-theoretic cost function for co-clustering, i.e.,
for simultaneous clustering of two sets based on similarities between their
elements. By constructing a simple random walk on the corresponding bipartite
graph, our cost function is derived from a recently proposed generalized
framework for information-theoretic Markov chain aggregation. The goal of our
cost function is to minimize relevant information loss, hence it connects to
the information bottleneck formalism. Moreover, via the connection to Markov
aggregation, our cost function is not ad hoc, but inherits its justification
from the operational qualities associated with the corresponding Markov
aggregation problem. We furthermore show that, for appropriate parameter
settings, our cost function is identical to well-known approaches from the
literature, such as Information-Theoretic Co-Clustering of Dhillon et al.
Hence, understanding the influence of this parameter admits a deeper
understanding of the relationship between previously proposed
information-theoretic cost functions. We highlight some strengths and
weaknesses of the cost function for different parameters. We also illustrate
the performance of our cost function, optimized with a simple sequential
heuristic, on several synthetic and real-world data sets, including the
Newsgroup20 and the MovieLens100k data sets.
Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments
Authors: Maxime Ferrera, Julien Moras, Pauline Trouvé-Peloux, Vincent Creuze
Date: 2018-06-15 00:00:00
Tasks: Monocular Visual Odometry, Optical Flow Estimation, Visual Odometry

In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odometry and visual SLAM have been exhaustively studied for aerial or terrestrial applications, but state-of-the-art algorithms fail underwater. In this paper we tackle the problem of using a simple low-cost camera for underwater localization and propose a new monocular visual odometry method dedicated to the underwater environment. We evaluate different tracking methods and show that optical flow based tracking is more suited to underwater images than classical approaches based on descriptors. We also propose a keyframe-based visual odometry approach highly relying on nonlinear optimization. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles (ROVs) used for underwater archaeological missions but the developed system can be used in any other applications as long as visual information is available.
The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities
Authors: Marco Del Tredici, Raquel Fernández
Date: 2018-06-15 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation

We investigate the birth and diffusion of lexical innovations in a large
dataset of online social communities. We build on sociolinguistic theories and
focus on the relation between the spread of a novel term and the social role of
the individuals who use it, uncovering characteristics of innovators and
adopters. Finally, we perform a prediction task that allows us to anticipate
whether an innovation will successfully spread within a community.
On the exact minimization of saturated loss functions for robust regression and subspace estimation
Authors: Fabien Lauer
Date: 2018-06-15 00:00:00
Tasks: General Classification, regression

This paper deals with robust regression and subspace estimation and more
precisely with the problem of minimizing a saturated loss function. In
particular, we focus on computational complexity issues and show that an exact
algorithm with polynomial time-complexity with respect to the number of data
can be devised for robust regression and subspace estimation. This result is
obtained by adopting a classification point of view and relating the problems
to the search for a linear model that can approximate the maximal number of
points with a given error. Approximate variants of the algorithms based on
ramdom sampling are also discussed and experiments show that it offers an
accuracy gain over the traditional RANSAC for a similar algorithmic simplicity.