pwc-archive/papers-with-abstracts huggingface dataset:
Process Monitoring on Sequences of System Call Count Vectors
Authors: Michael Dymshits, Ben Myara, David Tolpin
Date: 2017-07-12 00:00:00

We introduce a methodology for efficient monitoring of processes running on
hosts in a corporate network. The methodology is based on collecting streams of
system calls produced by all or selected processes on the hosts, and sending
them over the network to a monitoring server, where machine learning algorithms
are used to identify changes in process behavior due to malicious activity,
hardware failures, or software errors. The methodology uses a sequence of
system call count vectors as the data format which can handle large and varying
volumes of data.
  Unlike previous approaches, the methodology introduced in this paper is
suitable for distributed collection and processing of data in large corporate
networks. We evaluate the methodology both in a laboratory setting on a
real-life setup and provide statistics characterizing performance and accuracy
of the methodology.
Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition
Authors: Chunyu Xie, Ce Li, Baochang Zhang, Chen Chen, Jungong Han
Date: 2017-07-12 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Gesture recognition is a challenging problem in the field of biometrics. In
this paper, we integrate Fisher criterion into Bidirectional Long-Short Term
Memory (BLSTM) network and Bidirectional Gated Recurrent Unit (BGRU),thus
leading to two new deep models termed as F-BLSTM and F-BGRU. BothFisher
discriminative deep models can effectively classify the gesture based on
analyzing the acceleration and angular velocity data of the human gestures.
Moreover, we collect a large Mobile Gesture Database (MGD) based on the
accelerations and angular velocities containing 5547 sequences of 12 gestures.
Extensive experiments are conducted to validate the superior performance of the
proposed networks as compared to the state-of-the-art BLSTM and BGRU on MGD
database and two benchmark databases (i.e. BUAA mobile gesture and SmartWatch
gesture).
Subspace Learning in The Presence of Sparse Structured Outliers and Noise
Authors: Shervin Minaee, Yao Wang
Date: 2017-03-14 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Subspace learning is an important problem, which has many applications in
image and video processing. It can be used to find a low-dimensional
representation of signals and images. But in many applications, the desired
signal is heavily distorted by outliers and noise, which negatively affect the
learned subspace. In this work, we present a novel algorithm for learning a
subspace for signal representation, in the presence of structured outliers and
noise. The proposed algorithm tries to jointly detect the outliers and learn
the subspace for images. We present an alternating optimization algorithm for
solving this problem, which iterates between learning the subspace and finding
the outliers. This algorithm has been trained on a large number of image
patches, and the learned subspace is used for image segmentation, and is shown
to achieve better segmentation results than prior methods, including least
absolute deviation fitting, k-means clustering based segmentation in DjVu, and
shape primitive extraction and coding algorithm.
Deep Q-Networks for Accelerating the Training of Deep Neural Networks
Authors: Jie Fu
Date: 2016-06-05 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a principled deep reinforcement learning (RL)
approach that is able to accelerate the convergence rate of general deep neural
networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in
this work) is used to automatically learn policies about how to schedule
learning rates during the optimization of a DNN. The state features of the
agent are learned from the weight statistics of the optimizee during training.
The reward function of this agent is designed to learn policies that minimize
the optimizee's training time given a certain performance goal. The actions of
the agent correspond to changing the learning rate for the optimizee during
training. As far as we know, this is the first attempt to use deep RL to learn
how to optimize a large-sized DNN. We perform extensive experiments on a
standard benchmark dataset and demonstrate the effectiveness of the policies
learned by our approach.
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Authors: Joris Guérin, Olivier Gibaru, Eric Nyiri, Stéphane Thiery
Date: 2017-07-10 00:00:00
Tasks: General Classification, Object, Object Localization, Position

Unlike classification, position labels cannot be assigned manually by humans.
For this reason, generating supervision for precise object localization is a
hard task. This paper details a method to create large datasets for 3D object
localization, with real world images, using an industrial robot to generate
position labels. By knowledge of the geometry of the robot, we are able to
automatically synchronize the images of the two cameras and the object 3D
position. We applied it to generate a screw-driver localization dataset with
stereo images, using a KUKA LBR iiwa robot. This dataset could then be used to
train a CNN regressor to learn end-to-end stereo object localization from a set
of two standard uncalibrated cameras.
Importance Sampled Stochastic Optimization for Variational Inference
Authors: Joseph Sakaya, Arto Klami
Date: 2017-04-19 00:00:00
Tasks: Probabilistic Programming, Stochastic Optimization, Variational Inference

Variational inference approximates the posterior distribution of a
probabilistic model with a parameterized density by maximizing a lower bound
for the model evidence. Modern solutions fit a flexible approximation with
stochastic gradient descent, using Monte Carlo approximation for the gradients.
This enables variational inference for arbitrary differentiable probabilistic
models, and consequently makes variational inference feasible for probabilistic
programming languages. In this work we develop more efficient inference
algorithms for the task by considering importance sampling estimates for the
gradients. We show how the gradient with respect to the approximation
parameters can often be evaluated efficiently without needing to re-compute
gradients of the model itself, and then proceed to derive practical algorithms
that use importance sampled estimates to speed up computation.We present
importance sampled stochastic gradient descent that outperforms standard
stochastic gradient descent by a clear margin for a range of models, and
provide a justifiable variant of stochastic average gradients for variational
inference.
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
Authors: Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
Date: 2017-06-13 00:00:00

A key part of any evolutionary algorithm is fitness evaluation. When fitness
evaluations are corrupted by noise, as happens in many real-world problems as a
consequence of various types of uncertainty, a strategy is needed in order to
cope with this. Resampling is one of the most common strategies, whereby each
solution is evaluated many times in order to reduce the variance of the fitness
estimates. When evaluating the performance of a noisy optimisation algorithm, a
key consideration is the stopping condition for the algorithm. A frequently
used stopping condition in runtime analysis, known as "First Hitting Time", is
to stop the algorithm as soon as it encounters the optimal solution. However,
this is unrealistic for real-world problems, as if the optimal solution were
already known, there would be no need to search for it. This paper argues that
the use of First Hitting Time, despite being a commonly used approach, is
significantly flawed and overestimates the quality of many algorithms in
real-world cases, where the optimum is not known in advance and has to be
genuinely searched for. A better alternative is to measure the quality of the
solution an algorithm returns after a fixed evaluation budget, i.e., to focus
on final solution quality. This paper argues that focussing on final solution
quality is more realistic and demonstrates cases where the results produced by
each algorithm evaluation method lead to very different conclusions regarding
the quality of each noisy optimisation algorithm.
A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration
Authors: Andreas Mang, Lars Ruthotto
Date: 2017-03-13 00:00:00
Tasks: Image Registration

We present an efficient solver for diffeomorphic image registration problems
in the framework of Large Deformations Diffeomorphic Metric Mappings (LDDMM).
We use an optimal control formulation, in which the velocity field of a
hyperbolic PDE needs to be found such that the distance between the final state
of the system (the transformed/transported template image) and the observation
(the reference image) is minimized. Our solver supports both stationary and
non-stationary (i.e., transient or time-dependent) velocity fields. As
transformation models, we consider both the transport equation (assuming
intensities are preserved during the deformation) and the continuity equation
(assuming mass-preservation).
  We consider the reduced form of the optimal control problem and solve the
resulting unconstrained optimization problem using a discretize-then-optimize
approach. A key contribution is the elimination of the PDE constraint using a
Lagrangian hyperbolic PDE solver. Lagrangian methods rely on the concept of
characteristic curves that we approximate here using a fourth-order Runge-Kutta
method. We also present an efficient algorithm for computing the derivatives of
final state of the system with respect to the velocity field. This allows us to
use fast Gauss-Newton based methods. We present quickly converging iterative
linear solvers using spectral preconditioners that render the overall
optimization efficient and scalable. Our method is embedded into the image
registration framework FAIR and, thus, supports the most commonly used
similarity measures and regularization functionals. We demonstrate the
potential of our new approach using several synthetic and real world test
problems with up to 14.7 million degrees of freedom.
Machine Learning for RealisticBall Detection in RoboCup SPL
Authors: Domenico Bloisi, Francesco Del Duchetto, Tiziano Manoni, Vincenzo Suriani
Date: 2017-07-12 00:00:00
Tasks: BIG-bench Machine Learning

In this technical report, we describe the use of a machine learning approach
for detecting the realistic black and white ball currently in use in the
RoboCup Standard Platform League. Our aim is to provide a ready-to-use software
module that can be useful for the RoboCup SPL community. To this end, the
approach is integrated within the official B-Human code release 2016. The
complete code for the approach presented in this work can be downloaded from
the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team
GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The
approach has been tested in multiple environments, both indoor and outdoor.
Furthermore, the ball detector described in this technical report has been used
by the SPQR Robot Soccer Team during the competitions of the Robocup German
Open 2017. To facilitate the use of our code by other teams, we have prepared a
step-by-step installation guide.
A Critique of a Critique of Word Similarity Datasets: Sanity Check or Unnecessary Confusion?
Authors: Minh Le
Date: 2017-07-12 00:00:00
Tasks: Word Similarity

Critical evaluation of word similarity datasets is very important for
computational lexical semantics. This short report concerns the sanity check
proposed in Batchkarov et al. (2016) to evaluate several popular datasets such
as MC, RG and MEN -- the first two reportedly failed. I argue that this test is
unstable, offers no added insight, and needs major revision in order to fulfill
its purported goal.
The detector principle of constructing artificial neural networks as an alternative to the connectionist paradigm
Authors: Yuri Parzhin
Date: 2017-07-12 00:00:00

Artificial neural networks (ANN) are inadequate to biological neural
networks. This inadequacy is manifested in the use of the obsolete model of the
neuron and the connectionist paradigm of constructing ANN. The result of this
inadequacy is the existence of many shortcomings of the ANN and the problems of
their practical implementation. The alternative principle of ANN construction
is proposed in the article. This principle was called the detector principle.
The basis of the detector principle is the consideration of the binding
property of the input signals of a neuron. A new model of the neuron-detector,
a new approach to teaching ANN - counter training and a new approach to the
formation of the ANN architecture are used in this principle.
Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
Authors: Flavian Vasile, Damien Lefortier, Olivier Chapelle
Date: 2016-03-11 00:00:00

One of the most challenging problems in computational advertising is the
prediction of click-through and conversion rates for bidding in online
advertising auctions. An unaddressed problem in previous approaches is the
existence of highly non-uniform misprediction costs. While for model evaluation
these costs have been taken into account through recently proposed
business-aware offline metrics -- such as the Utility metric which measures the
impact on advertiser profit -- this is not the case when training the models
themselves. In this paper, to bridge the gap, we formally analyze the
relationship between optimizing the Utility metric and the log loss, which is
considered as one of the state-of-the-art approaches in conversion modeling.
Our analysis motivates the idea of weighting the log loss with the business
value of the predicted outcome. We present and analyze a new cost weighting
scheme and show that significant gains in offline and online performance can be
achieved.
Stochastic Bandit Models for Delayed Conversions
Authors: Claire Vernade, Olivier Cappé, Vianney Perchet
Date: 2017-06-28 00:00:00
Tasks: Product Recommendation

Online advertising and product recommendation are important domains of
applications for multi-armed bandit methods. In these fields, the reward that
is immediately available is most often only a proxy for the actual outcome of
interest, which we refer to as a conversion. For instance, in web advertising,
clicks can be observed within a few seconds after an ad display but the
corresponding sale --if any-- will take hours, if not days to happen. This
paper proposes and investigates a new stochas-tic multi-armed bandit model in
the framework proposed by Chapelle (2014) --based on empirical studies in the
field of web advertising-- in which each action may trigger a future reward
that will then happen with a stochas-tic delay. We assume that the probability
of conversion associated with each action is unknown while the distribution of
the conversion delay is known, distinguishing between the (idealized) case
where the conversion events may be observed whatever their delay and the more
realistic setting in which late conversions are censored. We provide
performance lower bounds as well as two simple but efficient algorithms based
on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
conversion rates are low, is based on a Poissonization argument, of independent
interest in other settings where aggregation of Bernoulli observations with
different success probabilities is required.
Elephant Search with Deep Learning for Microarray Data Analysis
Authors: Mrutyunjaya Panda
Date: 2017-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, General Classification

Even though there is a plethora of research in Microarray gene expression
data analysis, still, it poses challenges for researchers to effectively and
efficiently analyze the large yet complex expression of genes. The feature
(gene) selection method is of paramount importance for understanding the
differences in biological and non-biological variation between samples. In
order to address this problem, a novel elephant search (ES) based optimization
is proposed to select best gene expressions from the large volume of microarray
data. Further, a promising machine learning method is envisioned to leverage
such high dimensional and complex microarray dataset for extracting hidden
patterns inside to make a meaningful prediction and most accurate
classification. In particular, stochastic gradient descent based Deep learning
(DL) with softmax activation function is then used on the reduced features
(genes) for better classification of different samples according to their gene
expression levels. The experiments are carried out on nine most popular Cancer
microarray gene selection datasets, obtained from UCI machine learning
repository. The empirical results obtained by the proposed elephant search
based deep learning (ESDL) approach are compared with most recent published
article for its suitability in future Bioinformatics research.
Using RDF Summary Graph For Keyword-based Semantic Searches
Authors: Serkan Ayvaz, Mehmet Aydar
Date: 2017-07-12 00:00:00

The Semantic Web began to emerge as its standards and technologies developed
rapidly in the recent years. The continuing development of Semantic Web
technologies has facilitated publishing explicit semantics with data on the Web
in RDF data model. This study proposes a semantic search framework to support
efficient keyword-based semantic search on RDF data utilizing near neighbor
explorations. The framework augments the search results with the resources in
close proximity by utilizing the entity type semantics. Along with the search
results, the system generates a relevance confidence score measuring the
inferred semantic relatedness of returned entities based on the degree of
similarity. Furthermore, the evaluations assessing the effectiveness of the
framework and the accuracy of the results are presented.
Learning a CNN-based End-to-End Controller for a Formula SAE Racecar
Authors: Skanda Koppula
Date: 2017-07-12 00:00:00
Tasks: Benchmarking, GPU

We present a set of CNN-based end-to-end models for controls of a Formula SAE
racecar, along with various benchmarking and visualization tools to understand
model performance. We tackled three main problems in the context of
cone-delineated racetrack driving: (1) discretized steering, which translates a
first-person frame along to the track to a predicted steering direction. (2)
real-value steering, which translates a frame view to a real-value steering
angle, and (3) a network design for predicting brake and throttle. We
demonstrate high accuracy on our discretization task, low theoretical testing
errors with our model for real-value steering, and a starting point for future
work regarding a controller for our vehicle's brake and throttle. Timing
benchmarks suggests that the networks we propose have the latency and
throughput required for real-time controllers, when run on GPU-enabled
hardware.
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Authors: Jorge A. Balazs, Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo
Date: 2017-07-11 00:00:00
Proceeding: WS 2017 9
Tasks: General Classification, Natural Language Inference, Sentence

In this paper we present the model used by the team Rivercorners for the 2017
RepEval shared task. First, our model separately encodes a pair of sentences
into variable-length representations by using a bidirectional LSTM. Later, it
creates fixed-length raw representations by means of simple aggregation
functions, which are then refined using an attention mechanism. Finally it
combines the refined representations of both sentences into a single vector to
be used for classification. With this model we obtained test accuracies of
72.057% and 72.055% in the matched and mismatched evaluation tracks
respectively, outperforming the LSTM baseline, and obtaining performances
similar to a model that relies on shared information between sentences (ESIM).
When using an ensemble both accuracies increased to 72.247% and 72.827%
respectively.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Authors: Georgios Balikas, Simon Moura, Massih-Reza Amini
Date: 2017-07-12 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem.
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Authors: Burak Uzkent, Aneesh Rangnekar, M. J. Hoffman
Date: 2017-07-12 00:00:00
Tasks: object-detection, Object Detection, Object Tracking

Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.