pwc-archive/papers-with-abstracts huggingface dataset:
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition
Authors: Sudipta Banerjee, Vahid Mirjalili, Arun Ross
Date: 2018-08-31 00:00:00
Tasks: Iris Recognition

The principle of Photo Response Non-Uniformity (PRNU) is used to link an
image with its source, i.e., the sensor that produced it. In this work, we
investigate if it is possible to modify an iris image acquired using one sensor
in order to spoof the PRNU noise pattern of a different sensor. In this regard,
we develop an image perturbation routine that iteratively modifies blocks of
pixels in the original iris image such that its PRNU pattern approaches that of
a target sensor. Experiments indicate the efficacy of the proposed perturbation
method in spoofing PRNU patterns present in an iris image whilst still
retaining its biometric content.
Autonomous Configuration of Network Parameters in Operating Systems using Evolutionary Algorithms
Authors: Bartosz Gembala, Anis Yazidi, Hårek Haugerud, Stefano Nichele
Date: 2018-08-31 00:00:00
Tasks: Evolutionary Algorithms

By default, the Linux network stack is not configured for highspeed large
file transfer. The reason behind this is to save memory resources. It is
possible to tune the Linux network stack by increasing the network buffers size
for high-speed networks that connect server systems in order to handle more
network packets. However, there are also several other TCP/IP parameters that
can be tuned in an Operating System (OS). In this paper, we leverage Genetic
Algorithms (GAs) to devise a system which learns from the history of the
network traffic and uses this knowledge to optimize the current performance by
adjusting the parameters. This can be done for a standard Linux kernel using
sysctl or /proc. For a Virtual Machine (VM), virtually any type of OS can be
installed and an image can swiftly be compiled and deployed. By being a
sandboxed environment, risky configurations can be tested without the danger of
harming the system. Different scenarios for network parameter configurations
are thoroughly tested, and an increase of up to 65% throughput speed is
achieved compared to the default Linux configuration.
Learning Data-adaptive Nonparametric Kernels
Authors: Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
Date: 2018-08-31 00:00:00

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.
MobiBits: Multimodal Mobile Biometric Database
Authors: Ewelina Bartuzi, Katarzyna Roszczewska, Mateusz Trokielewicz, Radosław Białobrzeski
Date: 2018-08-31 00:00:00

This paper presents a novel database comprising representations of five
different biometric characteristics, collected in a mobile, unconstrained or
semi-constrained setting with three different mobile devices, including
characteristics previously unavailable in existing datasets, namely hand
images, thermal hand images, and thermal face images, all acquired with a
mobile, off-the-shelf device. In addition to this collection of data we perform
an extensive set of experiments providing insight on benchmark recognition
performance that can be achieved with these data, carried out with existing
commercial and academic biometric solutions. This is the first known to us
mobile biometric database introducing samples of biometric traits such as
thermal hand images and thermal face images. We hope that this contribution
will make a valuable addition to the already existing databases and enable new
experiments and studies in the field of mobile authentication. The MobiBits
database is made publicly available to the research community at no cost for
non-commercial purposes.
Bayesian Classifier for Route Prediction with Markov Chains
Authors: Jonathan P. Epperlein, Julien Monteil, Ming-ming Liu, Yingqi Gu, Sergiy Zhuk, Robert Shorten
Date: 2018-08-31 00:00:00
Tasks: Prediction

We present here a general framework and a specific algorithm for predicting
the destination, route, or more generally a pattern, of an ongoing journey,
building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R.
Shorten, and M. Mevissen, "Hidden Markov model for route and destination
prediction," in IEEE International Conference on Intelligent Transportation
Systems, 2017]. In the presented framework, known journey patterns are modelled
as stochastic processes, emitting the road segments visited during the journey,
and the ongoing journey is predicted by updating the posterior probability of
each journey pattern given the road segments visited so far. In this
contribution, we use Markov chains as models for the journey patterns, and
consider the prediction as final, once one of the posterior probabilities
crosses a predefined threshold. Despite the simplicity of both, examples run on
a synthetic dataset demonstrate high accuracy of the made predictions.
Imitation Learning for Neural Morphological String Transduction
Authors: Peter Makarov, Simon Clematide
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Imitation Learning, Lemmatization

We employ imitation learning to train a neural transition-based string
transducer for morphological tasks such as inflection generation and
lemmatization. Previous approaches to training this type of model either rely
on an external character aligner for the production of gold action sequences,
which results in a suboptimal model due to the unwarranted dependence on a
single gold action sequence despite spurious ambiguity, or require warm
starting with an MLE model. Our approach only requires a simple expert policy,
eliminating the need for a character aligner or warm start. It also addresses
familiar MLE training biases and leads to strong and state-of-the-art
performance on several benchmarks.
How agents see things: On visual representations in an emergent language game
Authors: Diane Bouchacourt, Marco Baroni
Date: 2018-08-31 00:00:00
Proceeding: EMNLP 2018 10

There is growing interest in the language developed by agents interacting in
emergent-communication settings. Earlier studies have focused on the agents'
symbol usage, rather than on their representation of visual input. In this
paper, we consider the referential games of Lazaridou et al. (2017) and
investigate the representations the agents develop during their evolving
interaction. We find that the agents establish successful communication by
inducing visual representations that almost perfectly align with each other,
but, surprisingly, do not capture the conceptual properties of the objects
depicted in the input images. We conclude that, if we are interested in
developing language-like communication systems, we must pay more attention to
the visual semantics agents associate to the symbols they use.
APES: a Python toolbox for simulating reinforcement learning environments
Authors: Aqeel Labash, Ardi Tampuu, Tambet Matiisen, Jaan Aru, Raul Vicente
Date: 2018-08-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Assisted by neural networks, reinforcement learning agents have been able to
solve increasingly complex tasks over the last years. The simulation
environment in which the agents interact is an essential component in any
reinforcement learning problem. The environment simulates the dynamics of the
agents' world and hence provides feedback to their actions in terms of state
observations and external rewards. To ease the design and simulation of such
environments this work introduces $\texttt{APES}$, a highly customizable and
open source package in Python to create 2D grid-world environments for
reinforcement learning problems. $\texttt{APES}$ equips agents with algorithms
to simulate any field of vision, it allows the creation and positioning of
items and rewards according to user-defined rules, and supports the interaction
of multiple agents.
Extracting Keywords from Open-Ended Business Survey Questions
Authors: Barbara McGillivray, Gard Jenset, Dominik Heil
Date: 2018-08-31 00:00:00
Tasks: Multiple-choice, Survey

Open-ended survey data constitute an important basis in research as well as for making business decisions. Collecting and manually analysing free-text survey data is generally more costly than collecting and analysing survey data consisting of answers to multiple-choice questions. Yet free-text data allow for new content to be expressed beyond predefined categories and are a very valuable source of new insights into people's opinions. At the same time, surveys always make ontological assumptions about the nature of the entities that are researched, and this has vital ethical consequences. Human interpretations and opinions can only be properly ascertained in their richness using textual data sources; if these sources are analyzed appropriately, the essential linguistic nature of humans and social entities is safeguarded. Natural Language Processing (NLP) offers possibilities for meeting this ethical business challenge by automating the analysis of natural language and thus allowing for insightful investigations of human judgements. We present a computational pipeline for analysing large amounts of responses to open-ended questions in surveys and extract keywords that appropriately represent people's opinions. This pipeline addresses the need to perform such tasks outside the scope of both commercial software and bespoke analysis, exceeds the performance to state-of-the-art systems, and performs this task in a transparent way that allows for scrutinising and exposing potential biases in the analysis. Following the principle of Open Data Science, our code is open-source and generalizable to other datasets.
Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation
Authors: Nikolaos Pappas, Lesly Miculicich Werlen, James Henderson
Date: 2018-08-31 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, Machine Translation, Translation, Word Embeddings

Tying the weights of the target word embeddings with the target word
classifiers of neural machine translation models leads to faster training and
often to better translation quality. Given the success of this parameter
sharing, we investigate other forms of sharing in between no sharing and hard
equality of parameters. In particular, we propose a structure-aware output
layer which captures the semantic structure of the output space of words within
a joint input-output embedding. The model is a generalized form of weight tying
which shares parameters but allows learning a more flexible relationship with
input word embeddings and allows the effective capacity of the output layer to
be controlled. In addition, the model shares weights across output classifiers
and translation contexts which allows it to better leverage prior knowledge
about them. Our evaluation on English-to-Finnish and English-to-German datasets
shows the effectiveness of the method against strong encoder-decoder baselines
trained with or without weight tying.
A novel graph-based model for hybrid recommendations in cold-start scenarios
Authors: Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Date: 2018-08-31 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, Information Retrieval, Recommendation Systems, Retrieval

Cold-start is a very common and still open problem in the Recommender Systems
literature. Since cold start items do not have any interaction, collaborative
algorithms are not applicable. One of the main strategies is to use pure or
hybrid content-based approaches, which usually yield to lower recommendation
quality than collaborative ones. Some techniques to optimize performance of
this type of approaches have been studied in recent past. One of them is called
feature weighting, which assigns to every feature a real value, called weight,
that estimates its importance. Statistical techniques for feature weighting
commonly used in Information Retrieval, like TF-IDF, have been adapted for
Recommender Systems, but they often do not provide sufficient quality
improvements. More recent approaches, FBSM and LFW, estimate weights by
leveraging collaborative information via machine learning, in order to learn
the importance of a feature based on other users opinions. This type of models
have shown promising results compared to classic statistical analyzes cited
previously. We propose a novel graph, feature-based machine learning model to
face the cold-start item scenario, learning the relevance of features from
probabilities of item-based collaborative filtering algorithms.
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease
Authors: Muriel Lang, Franz M. J. Pfister, Jakob Fröhner, Kian Abedinpour, Daniel Pichler, Urban Fietzek, Terry T. Um, Dana Kulić, Satoshi Endo, Sandra Hirche
Date: 2018-08-31 00:00:00
Tasks: Management

The assessment of Parkinson's disease (PD) poses a significant challenge as
it is influenced by various factors which lead to a complex and fluctuating
symptom manifestation. Thus, a frequent and objective PD assessment is highly
valuable for effective health management of people with Parkinson's disease
(PwP). Here, we propose a method for monitoring PwP by stochastically modeling
the relationships between their wrist movements during unscripted daily
activities and corresponding annotations about clinical displays of movement
abnormalities. We approach the estimation of PD motor signs by independently
modeling and hierarchically stacking Gaussian process models for three classes
of commonly observed movement abnormalities in PwP including tremor,
(non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically
adopted severity measures as annotations for training the models, thus allowing
our multi-layer Gaussian process prediction models to estimate not only their
presence but also their severities. The experimental validation of our approach
demonstrates strong agreement of the model predictions with these PD
annotations. Our results show the proposed method produces promising results in
objective monitoring of movement abnormalities of PD in the presence of
arbitrary and unknown voluntary motions, and makes an important step towards
continuous monitoring of PD in the home environment.
Gibson Env: Real-World Perception for Embodied Agents
Authors: Fei Xia, Amir Zamir, Zhi-Yang He, Alexander Sax, Jitendra Malik, Silvio Savarese
Date: 2018-08-31 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, General Reinforcement Learning, Robot Navigation

Developing visual perception models for active agents and sensorimotor
control are cumbersome to be done in the physical world, as existing algorithms
are too slow to efficiently learn in real-time and robots are fragile and
costly. This has given rise to learning-in-simulation which consequently casts
a question on whether the results transfer to real-world. In this paper, we are
concerned with the problem of developing real-world perception for active
agents, propose Gibson Virtual Environment for this purpose, and showcase
sample perceptual tasks learned therein. Gibson is based on virtualizing real
spaces, rather than using artificially designed ones, and currently includes
over 1400 floor spaces from 572 full buildings. The main characteristics of
Gibson are: I. being from the real-world and reflecting its semantic
complexity, II. having an internal synthesis mechanism, "Goggles", enabling
deploying the trained models in real-world without needing further domain
adaptation, III. embodiment of agents and making them subject to constraints of
physics and space.
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
Authors: Florian Strohm, Roman Klinger
Date: 2018-08-31 00:00:00
Tasks: Emotion Classification, General Classification, Sentiment Analysis

The effect of amplifiers, downtoners, and negations has been studied in
general and particularly in the context of sentiment analysis. However, there
is only limited work which aims at transferring the results and methods to
discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and
disgust. For instance, it is not straight-forward to interpret which emotion
the phrase "not happy" expresses. With this paper, we aim at obtaining a better
understanding of such modifiers in the context of emotion-bearing words and
their impact on document-level emotion classification, namely, microposts on
Twitter. We select an appropriate scope detection method for modifiers of
emotion words, incorporate it in a document-level emotion classification model
as additional bag of words and show that this approach improves the performance
of emotion classification. In addition, we build a term weighting approach
based on the different modifiers into a lexical model for the analysis of the
semantics of modifiers and their impact on emotion meaning. We show that
amplifiers separate emotions expressed with an emotion- bearing word more
clearly from other secondary connotations. Downtoners have the opposite effect.
In addition, we discuss the meaning of negations of emotion-bearing words. For
instance we show empirically that "not happy" is closer to sadness than to
anger and that fear-expressing words in the scope of downtoners often express
surprise.
Graph reduction with spectral and cut guarantees
Authors: Andreas Loukas
Date: 2018-08-31 00:00:00

Can one reduce the size of a graph without significantly altering its basic
properties? The graph reduction problem is hereby approached from the
perspective of restricted spectral approximation, a modification of the
spectral similarity measure used for graph sparsification. This choice is
motivated by the observation that restricted approximation carries strong
spectral and cut guarantees, and that it implies approximation results for
unsupervised learning problems relying on spectral embeddings.
  The paper then focuses on coarsening---the most common type of graph
reduction. Sufficient conditions are derived for a small graph to approximate a
larger one in the sense of restricted similarity. These findings give rise to
nearly-linear algorithms that, compared to both standard and advanced graph
reduction methods, find coarse graphs of improved quality, often by a large
margin, without sacrificing speed.
Adaptation and Robust Learning of Probabilistic Movement Primitives
Authors: Sebastian Gomez-Gonzalez, Gerhard Neumann, Bernhard Schölkopf, Jan Peters
Date: 2018-08-31 00:00:00

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, that focus on modeling only the mean behavior. In this paper, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics.
A Unified Mammogram Analysis Method via Hybrid Deep Supervision
Authors: Rongzhao Zhang, Han Zhang, Albert C. S. Chung
Date: 2018-08-31 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Segmentation, Whole Mammogram Classification

Automatic mammogram classification and mass segmentation play a critical role
in a computer-aided mammogram screening system. In this work, we present a
unified mammogram analysis framework for both whole-mammogram classification
and segmentation. Our model is designed based on a deep U-Net with residual
connections, and equipped with the novel hybrid deep supervision (HDS) scheme
for end-to-end multi-task learning. As an extension of deep supervision (DS),
HDS not only can force the model to learn more discriminative features like DS,
but also seamlessly integrates segmentation and classification tasks into one
model, thus the model can benefit from both pixel-wise and image-wise
supervisions. We extensively validate the proposed method on the widely-used
INbreast dataset. Ablation study corroborates that pixel-wise and image-wise
supervisions are mutually beneficial, evidencing the efficacy of HDS. The
results of 5-fold cross validation indicate that our unified model matches
state-of-the-art performance on both mammogram segmentation and classification
tasks, which achieves an average segmentation Dice similarity coefficient (DSC)
of 0.85 and a classification accuracy of 0.89. The code is available at
https://github.com/angrypudding/hybrid-ds.
A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations
Authors: Soheil Ahmadi, Mansoor Rezghi
Date: 2018-08-31 00:00:00
Tasks: Dimensionality Reduction

Dimensionality reduction is a main step in the learning process which plays
an essential role in many applications. The most popular methods in this field
like SVD, PCA, and LDA, only can be applied to data with vector format. This
means that for higher order data like matrices or more generally tensors, data
should be fold to the vector format. So, in this approach, the spatial
relations of features are not considered and also the probability of
over-fitting is increased. Due to these issues, in recent years some methods
like Generalized low-rank approximation of matrices (GLRAM) and Multilinear PCA
(MPCA) are proposed which deal with the data in their own format. So, in these
methods, the spatial relationships of features are preserved and the
probability of overfitting could be fallen. Also, their time and space
complexities are less than vector-based ones. However, because of the fewer
parameters, the search space in a multilinear approach is much smaller than the
search space of the vector-based approach. To overcome this drawback of
multilinear methods like GLRAM, we proposed a new method which is a general
form of GLRAM and by preserving the merits of it have a larger search space.
Experimental results confirm the quality of the proposed method. Also, applying
this approach to the other multilinear dimensionality reduction methods like
MPCA and MLDA is straightforward.