pwc-archive/papers-with-abstracts huggingface dataset:
Conceptual Analysis of Hypertext
Authors: Robert E. Kent, Christian Neuss
Date: 2018-10-16 00:00:00

In this chapter tools and techniques from the mathematical theory of formal
concept analysis are applied to hypertext systems in general, and the World
Wide Web in particular. Various processes for the conceptual structuring of
hypertext are discussed: summarization, conceptual scaling, and the creation of
conceptual links. Well-known interchange formats for summarizing networked
information resources as resource meta-information are reviewed, and two new
interchange formats originating from formal concept analysis are advocated.
Also reviewed is conceptual scaling, which provides a principled approach to
the faceted analysis techniques in library science classification. The
important notion of conceptual linkage is introduced as a generalization of a
hyperlink. The automatic hyperization of the content of legacy data is
described, and the composite conceptual structuring with hypertext linkage is
defined. For the conceptual empowerment of the Web user, a new technique called
conceptual browsing is advocated. Conceptual browsing, which browses over
conceptual links, is dual mode (extensional versus intensional) and dual scope
(global versus local).
Hybrid Feature Based SLAM Prototype
Authors: V. I Mebin Jose, D. J Binoj
Date: 2018-10-16 00:00:00
Tasks: Management

The development of data innovation as of late and the expanded limit, has
permitted the acquaintance of artificial vision connected with SLAM, offering
ascend to what is known as Visual SLAM. The objective of this paper is to build
up a route framework dependent on Visual SLAM to get a robot to a fundamental
and new condition, have the capacity to set and make a three-dimensional guide
thereof, utilizing just as sources of info recording your way with a stereo
vision camera. The consequence of this analysis is that the framework Visual
SLAM together with the combination of Fast SLAM (combination of kalman with
particulate filter and SIFT) perceive and recognize characteristic points in
images so adequately exact and unambiguous. This framework uses MATLAB, since
its adaptability and comfort for performing a wide range of tests. The program
has been tested by inserting a prerecorded video input with a camera stereo in
which a course is done by an office environment. The algorithm initially
locates points of interest in a stereo frame captured by the camera. These will
be located in 3D and they associate an identification descriptor. In the next
frame, the camera likewise identified points of interest and it will be
compared which of them have been previously detected by comparing their
descriptors. This process is known as "data association" and its successful
completion is fundamental to the SLAM algorithm. The position data of the robot
and points interest stored in data structures known as "particles" that evolve
independently. Its management is very important for the proper functioning of
the algorithm Fast SLAM. The results are found to be satisfactory.
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
Authors: Yanfu Zhang, Wenshan Wang, Rogerio Bonatti, Daniel Maturana, Sebastian Scherer
Date: 2018-10-16 00:00:00
Tasks: Autonomous Navigation, motion prediction, Reinforcement Learning, Trajectory Prediction

Predicting the motion of a mobile agent from a third-person perspective is an
important component for many robotics applications, such as autonomous
navigation and tracking. With accurate motion prediction of other agents,
robots can plan for more intelligent behaviors to achieve specified objectives,
instead of acting in a purely reactive way. Previous work addresses motion
prediction by either only filtering kinematics, or using hand-designed and
learned representations of the environment. Instead of separating kinematic and
environmental context, we propose a novel approach to integrate both into an
inverse reinforcement learning (IRL) framework for trajectory prediction.
Instead of exponentially increasing the state-space complexity with kinematics,
we propose a two-stage neural network architecture that considers motion and
environment together to recover the reward function. The first-stage network
learns feature representations of the environment using low-level LiDAR
statistics and the second-stage network combines those learned features with
kinematics data. We collected over 30 km of off-road driving data and validated
experimentally that our method can effectively extract useful environmental and
kinematic features. We generate accurate predictions of the distribution of
future trajectories of the vehicle, encoding complex behaviors such as
multi-modal distributions at road intersections, and even show different
predictions at the same intersection depending on the vehicle's speed.
Incremental Few-Shot Learning with Attention Attractor Networks
Authors: Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
Date: 2018-10-16 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Few-Shot Learning, General Classification, Meta-Learning

Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning additional concepts, with limited data and without re-training on the full training set. This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of these parameters. We demonstrate that the learned attractor network can help recognize novel classes while remembering old classes without the need to review the original training set, outperforming various baselines.
Hierarchical Generative Modeling for Controllable Speech Synthesis
Authors: Wei-Ning Hsu, Yu Zhang, Ron J. Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang
Date: 2018-10-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Attribute, Speech Synthesis, text-to-speech, Text to Speech

This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model
which can control latent attributes in the generated speech that are rarely
annotated in the training data, such as speaking style, accent, background
noise, and recording conditions. The model is formulated as a conditional
generative model based on the variational autoencoder (VAE) framework, with two
levels of hierarchical latent variables. The first level is a categorical
variable, which represents attribute groups (e.g. clean/noisy) and provides
interpretability. The second level, conditioned on the first, is a multivariate
Gaussian variable, which characterizes specific attribute configurations (e.g.
noise level, speaking rate) and enables disentangled fine-grained control over
these attributes. This amounts to using a Gaussian mixture model (GMM) for the
latent distribution. Extensive evaluation demonstrates its ability to control
the aforementioned attributes. In particular, we train a high-quality
controllable TTS model on real found data, which is capable of inferring
speaker and style attributes from a noisy utterance and use it to synthesize
clean speech with controllable speaking style.
Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences
Authors: Hannah Druckenmiller, Solomon Hsiang
Date: 2018-10-16 00:00:00
Tasks: Management, Time Series Analysis

We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifying assumptions of SFD are similar in mathematical structure and plausibility to other quasi-experimental designs. We use SFD to obtain new estimates for the effects of time-invariant geographic factors, soil and climate, on long-run agricultural productivities --- relationships crucial for economic decisions, such as land management and climate policy, but notoriously confounded by unobservables.
Cross-Modal and Hierarchical Modeling of Video and Text
Authors: Bowen Zhang, Hexiang Hu, Fei Sha
Date: 2018-10-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Retrieval, Temporal Action Localization, Video Captioning, Zero-Shot Action Recognition

Visual data and text data are composed of information at multiple
granularities. A video can describe a complex scene that is composed of
multiple clips or shots, where each depicts a semantically coherent event or
action. Similarly, a paragraph may contain sentences with different topics,
which collectively conveys a coherent message or story. In this paper, we
investigate the modeling techniques for such hierarchical sequential data where
there are correspondences across multiple modalities. Specifically, we
introduce hierarchical sequence embedding (HSE), a generic model for embedding
sequential data of different modalities into hierarchically semantic spaces,
with either explicit or implicit correspondence information. We perform
empirical studies on large-scale video and paragraph retrieval datasets and
demonstrated superior performance by the proposed methods. Furthermore, we
examine the effectiveness of our learned embeddings when applied to downstream
tasks. We show its utility in zero-shot action recognition and video
captioning.
Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
Authors: Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert
Date: 2018-10-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Topological error correcting codes, and particularly the surface code,
currently provide the most feasible roadmap towards large-scale fault-tolerant
quantum computation. As such, obtaining fast and flexible decoding algorithms
for these codes, within the experimentally relevant context of faulty syndrome
measurements, is of critical importance. In this work, we show that the problem
of decoding such codes, in the full fault-tolerant setting, can be naturally
reformulated as a process of repeated interactions between a decoding agent and
a code environment, to which the machinery of reinforcement learning can be
applied to obtain decoding agents. As a demonstration, by using deepQ learning,
we obtain fast decoding agents for the surface code, for a variety of
noise-models.
Study of Sparsity-Aware Subband Adaptive Filtering Algorithms with Adjustable Penalties
Authors: Y. Yu, H. Zhao, R. C. de Lamare
Date: 2018-10-16 00:00:00

We propose two sparsity-aware normalized subband adaptive filter (NSAF)
algorithms by using the gradient descent method to minimize a combination of
the original NSAF cost function and the l1-norm penalty function on the filter
coefficients. This l1-norm penalty exploits the sparsity of a system in the
coefficients update formulation, thus improving the performance when
identifying sparse systems. Compared with prior work, the proposed algorithms
have lower computational complexity with comparable performance. We study and
devise statistical models for these sparsity-aware NSAF algorithms in the mean
square sense involving their transient and steady -state behaviors. This study
relies on the vectorization argument and the paraunitary assumption imposed on
the analysis filter banks, and thus does not restrict the input signal to being
Gaussian or having another distribution. In addition, we propose to adjust
adaptively the intensity parameter of the sparsity attraction term. Finally,
simulation results in sparse system identification demonstrate the
effectiveness of our theoretical results.
Malware triage for early identification of Advanced Persistent Threat activities
Authors: Giuseppe Laurenza, Riccardo Lazzeretti, Luca Mazzotti
Date: 2018-10-16 00:00:00
Tasks: Malware Analysis

In the last decade, a new class of cyber-threats has emerged. This new
cybersecurity adversary is known with the name of "Advanced Persistent Threat"
(APT) and is referred to different organizations that in the last years have
been "in the center of the eye" due to multiple dangerous and effective attacks
targeting financial and politic, news headlines, embassies, critical
infrastructures, TV programs, etc. In order to early identify APT related
malware, a semi-automatic approach for malware samples analysis is needed. In
our previous work we introduced a "malware triage" step for a semi-automatic
malware analysis architecture. This step has the duty to analyze as fast as
possible new incoming samples and to immediately dispatch the ones that deserve
a deeper analysis, among all the malware delivered per day in the cyber-space,
the ones that really worth to be further examined by analysts. Our paper
focuses on malware developed by APTs, and we build our knowledge base, used in
the triage, on known APTs obtained from publicly available reports. In order to
have the triage as fast as possible, we only rely on static malware features,
that can be extracted with negligible delay, and use machine learning
techniques for the identification. In this work we move from multiclass
classification to a group of oneclass classifier, which simplify the training
and allows higher modularity. The results of the proposed framework highlight
high performances, reaching a precision of 100% and an accuracy over 95%
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018
Authors: Mattia Antonino Di Gangi, Roberto Dessì, Roldano Cattoni, Matteo Negri, Marco Turchi
Date: 2018-10-16 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, Translation

This paper describes FBK's submission to the end-to-end English-German speech
translation task at IWSLT 2018. Our system relies on a state-of-the-art model
based on LSTMs and CNNs, where the CNNs are used to reduce the temporal
dimension of the audio input, which is in general much higher than machine
translation input. Our model was trained only on the audio-to-text parallel
data released for the task, and fine-tuned on cleaned subsets of the original
training corpus. The addition of weight normalization and label smoothing
improved the baseline system by 1.0 BLEU point on our validation set. The final
submission also featured checkpoint averaging within a training run and
ensemble decoding of models trained during multiple runs. On test data, our
best single model obtained a BLEU score of 9.7, while the ensemble obtained a
BLEU score of 10.24.
Security Matters: A Survey on Adversarial Machine Learning
Authors: Guofu Li, Pengjia Zhu, Jin Li, Zhemin Yang, Ning Cao, Zhiyi Chen
Date: 2018-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Survey

Adversarial machine learning is a fast growing research area, which considers
the scenarios when machine learning systems may face potential adversarial
attackers, who intentionally synthesize input data to make a well-trained model
to make mistake. It always involves a defending side, usually a classifier, and
an attacking side that aims to cause incorrect output. The earliest studies on
the adversarial examples for machine learning algorithms start from the
information security area, which considers a much wider varieties of attacking
methods. But recent research focus that popularized by the deep learning
community places strong emphasis on how the "imperceivable" perturbations on
the normal inputs may cause dramatic mistakes by the deep learning with
supposed super-human accuracy. This paper serves to give a comprehensive
introduction to a range of aspects of the adversarial deep learning topic,
including its foundations, typical attacking and defending strategies, and some
extended studies.
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification
Authors: Ahmed Elnaggar, Christoph Gebendorfer, Ingo Glaser, Florian Matthes
Date: 2018-10-16 00:00:00
Tasks: Document Translation, General Classification, Legal Document Translation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Transfer Learning, Translation

The digitalization of the legal domain has been ongoing for a couple of
years. In that process, the application of different machine learning (ML)
techniques is crucial. Tasks such as the classification of legal documents or
contract clauses as well as the translation of those are highly relevant. On
the other side, digitized documents are barely accessible in this field,
particularly in Germany. Today, deep learning (DL) is one of the hot topics
with many publications and various applications. Sometimes it provides results
outperforming the human level. Hence this technique may be feasible for the
legal domain as well. However, DL requires thousands of samples to provide
decent results. A potential solution to this problem is multi-task DL to enable
transfer learning. This approach may be able to overcome the data scarcity
problem in the legal domain, specifically for the German language. We applied
the state of the art multi-task model on three tasks: translation,
summarization, and multi-label classification. The experiments were conducted
on legal document corpora utilizing several task combinations as well as
various model parameters. The goal was to find the optimal configuration for
the tasks at hand within the legal domain. The multi-task DL approach
outperformed the state of the art results in all three tasks. This opens a new
direction to integrate DL technology more efficiently in the legal domain.
Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions
Authors: Chuang Ye, M. Cenk Gursoy, Senem Velipasalar
Date: 2018-10-16 00:00:00

In this paper, wireless video transmission to multiple users under total
transmission power and minimum required video quality constraints is studied.
In order to provide the desired performance levels to the end-users in
real-time video transmissions while using the energy resources efficiently, we
assume that power control is employed. Due to the presence of interference,
determining the optimal power control is a non-convex problem but can be solved
via monotonic optimization framework. However, monotonic optimization is an
iterative algorithm and can often entail considerable computational complexity,
making it not suitable for real-time applications. To address this, we propose
a learning-based approach that treats the input and output of a resource
allocation algorithm as an unknown nonlinear mapping and a deep neural network
(DNN) is employed to learn this mapping. This learned mapping via DNN can
provide the optimal power level quickly for given channel conditions.
The Newton Scheme for Deep Learning
Authors: Junqing Qiu, Guoren Zhong, Yihua Lu, Kun Xin, Huihuan Qian, Xi Zhu
Date: 2018-10-16 00:00:00
Tasks: Deep Learning, Object Recognition

We introduce a neural network (NN) strictly governed by Newton's Law, with
the nature required basis functions derived from the fundamental classic
mechanics. Then, by classifying the training model as a quick procedure of
'force pattern' recognition, we developed the Newton physics-based NS scheme.
Once the force pattern is confirmed, the neuro network simply does the checking
of the 'pattern stability' instead of the continuous fitting by computational
resource consuming big data-driven processing. In the given physics's law
system, once the field is confirmed, the mathematics bases for the force field
description actually are not diverged but denumerable, which can save the
function representations from the exhaustible available mathematics bases. In
this work, we endorsed Newton's Law into the deep learning technology and
proposed Newton Scheme (NS). Under NS, the user first identifies the path
pattern, like the constant acceleration movement.The object recognition
technology first loads mass information, then, the NS finds the matched
physical pattern and describe and predict the trajectory of the movements with
nearly zero error. We compare the major contribution of this NS with the TCN,
GRU and other physics inspired 'FIND-PDE' methods to demonstrate fundamental
and extended applications of how the NS works for the free-falling, pendulum
and curve soccer balls.The NS methodology provides more opportunity for the
future deep learning advances.
Super Characters: A Conversion from Sentiment Classification to Image Classification
Authors: Baohua Sun, Lin Yang, Patrick Dong, Wenhan Zhang, Jason Dong, Charles Young
Date: 2018-10-15 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, image-classification, Image Classification, Sentiment Analysis, Sentiment Classification, Topic Classification

We propose a method named Super Characters for sentiment classification. This
method converts the sentiment classification problem into image classification
problem by projecting texts into images and then applying CNN models for
classification. Text features are extracted automatically from the generated
Super Characters images, hence there is no need of any explicit step of
embedding the words or characters into numerical vector representations.
Experimental results on large social media corpus show that the Super
Characters method consistently outperforms other methods for sentiment
classification and topic classification tasks on ten large social media
datasets of millions of contents in four different languages, including
Chinese, Japanese, Korean and English.
Conceptual Collectives
Authors: Robert E. Kent
Date: 2018-10-14 00:00:00

The notions of formal contexts and concept lattices, although introduced by
Wille only ten years ago, already have proven to be of great utility in various
applications such as data analysis and knowledge representation. In this paper
we give arguments that Wille's original notion of formal context, although
quite appealing in its simplicity, now should be replaced by a more semantic
notion. This new notion of formal context entails a modified approach to
concept construction. We base our arguments for these new versions of formal
context and concept construction upon Wille's philosophical attitude with
reference to the intensional aspect of concepts. We give a brief development of
the relational theory of formal contexts and concept construction,
demonstrating the equivalence of "concept-lattice construction" of Wille with
the well-known "completion by cuts" of MacNeille. Generalization and
abstraction of these formal contexts offers a powerful approach to knowledge
representation.