pwc-archive/papers-with-abstracts huggingface dataset:
Mining and Analyzing the Future Works in Scientific Articles
Authors: Yue Hu, Xiaojun Wan
Date: 2015-07-08 00:00:00
Tasks: Articles, Classification, General Classification

Future works in scientific articles are valuable for researchers and they can
guide researchers to new research directions or ideas. In this paper, we mine
the future works in scientific articles in order to 1) provide an insight for
future work analysis and 2) facilitate researchers to search and browse future
works in a research area. First, we study the problem of future work extraction
and propose a regular expression based method to address the problem. Second,
we define four different categories for the future works by observing the data
and investigate the multi-class future work classification problem. Third, we
apply the extraction method and the classification model to a paper dataset in
the computer science field and conduct a further analysis of the future works.
Finally, we design a prototype system to search and demonstrate the future
works mined from the scientific papers. Our evaluation results show that our
extraction method can get high precision and recall values and our
classification model can also get good results and it outperforms several
baseline models. Further analysis of the future work sentences also indicates
interesting results.
Application of Deep Neural Network in Estimation of the Weld Bead Parameters
Authors: Soheil Keshmiri, Xin Zheng, Chee Meng Chew, Chee Khiang Pang
Date: 2015-02-14 00:00:00

We present a deep learning approach to estimation of the bead parameters in
welding tasks. Our model is based on a four-hidden-layer neural network
architecture. More specifically, the first three hidden layers of this
architecture utilize Sigmoid function to produce their respective intermediate
outputs. On the other hand, the last hidden layer uses a linear transformation
to generate the final output of this architecture. This transforms our deep
network architecture from a classifier to a non-linear regression model. We
compare the performance of our deep network with a selected number of results
in the literature to show a considerable improvement in reducing the errors in
estimation of these values. Furthermore, we show its scalability on estimating
the weld bead parameters with same level of accuracy on combination of datasets
that pertain to different welding techniques. This is a nontrivial result that
is counter-intuitive to the general belief in this field of research.
The Role of Pragmatics in Legal Norm Representation
Authors: Shashishekar Ramakrishna, Lukasz Gorski, Adrian Paschke
Date: 2015-07-08 00:00:00
Tasks: Decision Making

Despite the 'apparent clarity' of a given legal provision, its application
may result in an outcome that does not exactly conform to the semantic level of
a statute. The vagueness within a legal text is induced intentionally to
accommodate all possible scenarios under which such norms should be applied,
thus making the role of pragmatics an important aspect also in the
representation of a legal norm and reasoning on top of it. The notion of
pragmatics considered in this paper does not focus on the aspects associated
with judicial decision making. The paper aims to shed light on the aspects of
pragmatics in legal linguistics, mainly focusing on the domain of patent law,
only from a knowledge representation perspective. The philosophical discussions
presented in this paper are grounded based on the legal theories from Grice and
Marmor.
Shedding Light on the Asymmetric Learning Capability of AdaBoost
Authors: Iago Landesa-Vázquez, José Luis Alba-Castro
Date: 2015-07-08 00:00:00

In this paper, we propose a different insight to analyze AdaBoost. This
analysis reveals that, beyond some preconceptions, AdaBoost can be directly
used as an asymmetric learning algorithm, preserving all its theoretical
properties. A novel class-conditional description of AdaBoost, which models the
actual asymmetric behavior of the algorithm, is presented.
Ego-Object Discovery
Authors: Marc Bolaños, Petia Radeva
Date: 2015-04-07 00:00:00
Tasks: Object, Object Discovery

Lifelogging devices are spreading faster everyday. This growth can represent
great benefits to develop methods for extraction of meaningful information
about the user wearing the device and his/her environment. In this paper, we
propose a semi-supervised strategy for easily discovering objects relevant to
the person wearing a first-person camera. Given an egocentric video/images
sequence acquired by the camera, our algorithm uses both the appearance
extracted by means of a convolutional neural network and an object refill
methodology that allows to discover objects even in case of small amount of
object appearance in the collection of images. An SVM filtering strategy is
applied to deal with the great part of the False Positive object candidates
found by most of the state of the art object detectors. We validate our method
on a new egocentric dataset of 4912 daily images acquired by 4 persons as well
as on both PASCAL 2012 and MSRC datasets. We obtain for all of them results
that largely outperform the state of the art approach. We make public both the
EDUB dataset and the algorithm code.
Multi-Document Summarization via Discriminative Summary Reranking
Authors: Xiaojun Wan, Ziqiang Cao, Furu Wei, Sujian Li, Ming Zhou
Date: 2015-07-08 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Reranking, Sentence

Existing multi-document summarization systems usually rely on a specific
summarization model (i.e., a summarization method with a specific parameter
setting) to extract summaries for different document sets with different
topics. However, according to our quantitative analysis, none of the existing
summarization models can always produce high-quality summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach.
Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem
Authors: Ruggero Bellio, Sara Ceschia, Luca Di Gaspero, Andrea Schaerf, Tommaso Urli
Date: 2014-09-25 00:00:00

We consider the university course timetabling problem, which is one of the
most studied problems in educational timetabling. In particular, we focus our
attention on the formulation known as the curriculum-based course timetabling
problem, which has been tackled by many researchers and for which there are
many available benchmarks.
  The contribution of this paper is twofold. First, we propose an effective and
robust single-stage simulated annealing method for solving the problem.
Secondly, we design and apply an extensive and statistically-principled
methodology for the parameter tuning procedure. The outcome of this analysis is
a methodology for modeling the relationship between search method parameters
and instance features that allows us to set the parameters for unseen instances
on the basis of a simple inspection of the instance itself. Using this
methodology, our algorithm, despite its apparent simplicity, has been able to
achieve high quality results on a set of popular benchmarks.
  A final contribution of the paper is a novel set of real-world instances,
which could be used as a benchmark for future comparison.
On Graduated Optimization for Stochastic Non-Convex Problems
Authors: Elad Hazan, Kfir. Y. Levy, Shai Shalev-Shwartz
Date: 2015-03-12 00:00:00

The graduated optimization approach, also known as the continuation method,
is a popular heuristic to solving non-convex problems that has received renewed
interest over the last decade. Despite its popularity, very little is known in
terms of theoretical convergence analysis. In this paper we describe a new
first-order algorithm based on graduated optimiza- tion and analyze its
performance. We characterize a parameterized family of non- convex functions
for which this algorithm provably converges to a global optimum. In particular,
we prove that the algorithm converges to an {\epsilon}-approximate solution
within O(1/\epsilon^2) gradient-based steps. We extend our algorithm and
analysis to the setting of stochastic non-convex optimization with noisy
gradient feedback, attaining the same convergence rate. Additionally, we
discuss the setting of zero-order optimization, and devise a a variant of our
algorithm which converges at rate of O(d^2/\epsilon^4).
Archaeology in the Digital Age: From Paper to Databases
Authors: Frédérique Mélanie-Becquet, Johan Ferguth, Katherine Gruel, Thierry Poibeau
Date: 2015-07-08 00:00:00

Research units in archaeology often manage large and precious archives
containing various documents, including reports on fieldwork, scholarly studies
and reference books. These archives are of course invaluable, recording decades
of work, but are generally hard to consult and access. In this context,
digitizing full text documents is not enough: information must be formalized,
structured and easy to access thanks to friendly user interfaces.
Generating Navigable Semantic Maps from Social Sciences Corpora
Authors: Thierry Poibeau, Pablo Ruiz
Date: 2015-07-08 00:00:00
Tasks: Navigate

It is now commonplace to observe that we are facing a deluge of online
information. Researchers have of course long acknowledged the potential value
of this information since digital traces make it possible to directly observe,
describe and analyze social facts, and above all the co-evolution of ideas and
communities over time. However, most online information is expressed through
text, which means it is not directly usable by machines, since computers
require structured, organized and typed information in order to be able to
manipulate it. Our goal is thus twofold: 1. Provide new natural language
processing techniques aiming at automatically extracting relevant information
from texts, especially in the context of social sciences, and connect these
pieces of information so as to obtain relevant socio-semantic networks; 2.
Provide new ways of exploring these socio-semantic networks, thanks to tools
allowing one to dynamically navigate these networks, de-construct and
re-construct them interactively, from different points of view following the
needs expressed by domain experts.
Hindi to English Transfer Based Machine Translation System
Authors: Akanksha Gehlot, Vaishali Sharma, Shashi Pal Singh, Ajai Kumar
Date: 2015-07-08 00:00:00
Tasks: Machine Translation, Translation

In large societies like India there is a huge demand to convert one human
language into another. Lots of work has been done in this area. Many transfer
based MTS have developed for English to other languages, as MANTRA CDAC Pune,
MATRA CDAC Pune, SHAKTI IISc Bangalore and IIIT Hyderabad. Still there is a
little work done for Hindi to other languages. Currently we are working on it.
In this paper we focus on designing a system, that translate the document from
Hindi to English by using transfer based approach. This system takes an input
text check its structure through parsing. Reordering rules are used to generate
the text in target language. It is better than Corpus Based MTS because Corpus
Based MTS require large amount of word aligned data for translation that is not
available for many languages while Transfer Based MTS requires only knowledge
of both the languages(source language and target language) to make transfer
rules. We get correct translation for simple assertive sentences and almost
correct for complex and compound sentences.
A Bayesian Approach for Online Classifier Ensemble
Authors: Qinxun Bai, Henry Lam, Stan Sclaroff
Date: 2015-07-08 00:00:00

We propose a Bayesian approach for recursively estimating the classifier
weights in online learning of a classifier ensemble. In contrast with past
methods, such as stochastic gradient descent or online boosting, our approach
estimates the weights by recursively updating its posterior distribution. For a
specified class of loss functions, we show that it is possible to formulate a
suitably defined likelihood function and hence use the posterior distribution
as an approximation to the global empirical loss minimizer. If the stream of
training data is sampled from a stationary process, we can also show that our
approach admits a superior rate of convergence to the expected loss minimizer
than is possible with standard stochastic gradient descent. In experiments with
real-world datasets, our formulation often performs better than
state-of-the-art stochastic gradient descent and online boosting algorithms.
Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks
Authors: Michael Giering, Vivek Venugopalan, Kishore Reddy
Date: 2014-12-22 00:00:00

The ability to simultaneously leverage multiple modes of sensor information
is critical for perception of an automated vehicle's physical surroundings.
Spatio-temporal alignment of registration of the incoming information is often
a prerequisite to analyzing the fused data. The persistence and reliability of
multi-modal registration is therefore the key to the stability of decision
support systems ingesting the fused information. LiDAR-video systems like on
those many driverless cars are a common example of where keeping the LiDAR and
video channels registered to common physical features is important. We develop
a deep learning method that takes multiple channels of heterogeneous data, to
detect the misalignment of the LiDAR-video inputs. A number of variations were
tested on the Ford LiDAR-video driving test data set and will be discussed. To
the best of our knowledge the use of multi-modal deep convolutional neural
networks for dynamic real-time LiDAR-video registration has not been presented.
Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks
Authors: Soumik Sarkar, Vivek Venugopalan, Kishore Reddy, Michael Giering, Julian Ryde, Navdeep Jaitly
Date: 2014-12-22 00:00:00
Tasks: Edge Detection

Occlusion edges in images which correspond to range discontinuity in the
scene from the point of view of the observer are an important prerequisite for
many vision and mobile robot tasks. Although they can be extracted from range
data however extracting them from images and videos would be extremely
beneficial. We trained a deep convolutional neural network (CNN) to identify
occlusion edges in images and videos with both RGB-D and RGB inputs. The use of
CNN avoids hand-crafting of features for automatically isolating occlusion
edges and distinguishing them from appearance edges. Other than quantitative
occlusion edge detection results, qualitative results are provided to
demonstrate the trade-off between high resolution analysis and frame-level
computation time which is critical for real-time robotics applications.
Toward Idealized Decision Theory
Authors: Nate Soares, Benja Fallenstein
Date: 2015-07-07 00:00:00

This paper motivates the study of decision theory as necessary for aligning
smarter-than-human artificial systems with human interests. We discuss the
shortcomings of two standard formulations of decision theory, and demonstrate
that they cannot be used to describe an idealized decision procedure suitable
for approximation by artificial systems. We then explore the notions of policy
selection and logical counterfactuals, two recent insights into decision theory
that point the way toward promising paths for future research.
Comparing persistence diagrams through complex vectors
Authors: Barbara Di Fabio, Massimo Ferri
Date: 2015-05-06 00:00:00
Tasks: General Classification, Retrieval

The natural pseudo-distance of spaces endowed with filtering functions is
precious for shape classification and retrieval; its optimal estimate coming
from persistence diagrams is the bottleneck distance, which unfortunately
suffers from combinatorial explosion. A possible algebraic representation of
persistence diagrams is offered by complex polynomials; since far polynomials
represent far persistence diagrams, a fast comparison of the coefficient
vectors can reduce the size of the database to be classified by the bottleneck
distance. This article explores experimentally three transformations from
diagrams to polynomials and three distances between the complex vectors of
coefficients.
Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states
Authors: Fabio L. Traversa, Chiara Ramella, Fabrizio Bonani, Massimiliano Di Ventra
Date: 2014-11-18 00:00:00

Memcomputing is a novel non-Turing paradigm of computation that uses
interacting memory cells (memprocessors for short) to store and process
information on the same physical platform. It was recently proved
mathematically that memcomputing machines have the same computational power of
non-deterministic Turing machines. Therefore, they can solve NP-complete
problems in polynomial time and, using the appropriate architecture, with
resources that only grow polynomially with the input size. The reason for this
computational power stems from properties inspired by the brain and shared by
any universal memcomputing machine, in particular intrinsic parallelism and
information overhead, namely the capability of compressing information in the
collective state of the memprocessor network. Here, we show an experimental
demonstration of an actual memcomputing architecture that solves the
NP-complete version of the subset-sum problem in only one step and is composed
of a number of memprocessors that scales linearly with the size of the problem.
We have fabricated this architecture using standard microelectronic technology
so that it can be easily realized in any laboratory setting. Even though the
particular machine presented here is eventually limited by noise--and will thus
require error-correcting codes to scale to an arbitrary number of
memprocessors--it represents the first proof-of-concept of a machine capable of
working with the collective state of interacting memory cells, unlike the
present-day single-state machines built using the von Neumann architecture.
Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis
Authors: Vaibhav Srivastava, Paul Reverdy, Naomi Ehrich Leonard
Date: 2015-07-05 00:00:00
Tasks: Decision Making

We consider the correlated multiarmed bandit (MAB) problem in which the
rewards associated with each arm are modeled by a multivariate Gaussian random
variable, and we investigate the influence of the assumptions in the Bayesian
prior on the performance of the upper credible limit (UCL) algorithm and a new
correlated UCL algorithm. We rigorously characterize the influence of accuracy,
confidence, and correlation scale in the prior on the decision-making
performance of the algorithms. Our results show how priors and correlation
structure can be leveraged to improve performance.
Memcomputing with membrane memcapacitive systems
Authors: Yuriy V. Pershin, Fabio L. Traversa, Massimiliano Di Ventra
Date: 2014-10-14 00:00:00

We show theoretically that networks of membrane memcapacitive systems --
capacitors with memory made out of membrane materials -- can be used to perform
a complete set of logic gates in a massively parallel way by simply changing
the external input amplitudes, but not the topology of the network. This
polymorphism is an important characteristic of memcomputing (computing with
memories) that closely reproduces one of the main features of the brain. A
practical realization of these membrane memcapacitive systems, using, e.g.,
graphene or other 2D materials, would be a step forward towards a solid-state
realization of memcomputing with passive devices.
Wasserstein Training of Boltzmann Machines
Authors: Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi
Date: 2015-07-07 00:00:00
Tasks: Denoising

The Boltzmann machine provides a useful framework to learn highly complex,
multimodal and multiscale data distributions that occur in the real world. The
default method to learn its parameters consists of minimizing the
Kullback-Leibler (KL) divergence from training samples to the Boltzmann model.
We propose in this work a novel approach for Boltzmann training which assumes
that a meaningful metric between observations is given. This metric can be
represented by the Wasserstein distance between distributions, for which we
derive a gradient with respect to the model parameters. Minimization of this
new Wasserstein objective leads to generative models that are better when
considering the metric and that have a cluster-like structure. We demonstrate
the practical potential of these models for data completion and denoising, for
which the metric between observations plays a crucial role.
Incremental Adaptation Strategies for Neural Network Language Models
Authors: Aram Ter-Sarkisov, Holger Schwenk, Loic Barrault, Fethi Bougares
Date: 2014-12-20 00:00:00
Proceeding: WS 2015 7
Tasks: Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

It is today acknowledged that neural network language models outperform
backoff language models in applications like speech recognition or statistical
machine translation. However, training these models on large amounts of data
can take several days. We present efficient techniques to adapt a neural
network language model to new data. Instead of training a completely new model
or relying on mixture approaches, we propose two new methods: continued
training on resampled data or insertion of adaptation layers. We present
experimental results in an CAT environment where the post-edits of professional
translators are used to improve an SMT system. Both methods are very fast and
achieve significant improvements without overfitting the small adaptation data.
Top-N recommendations in the presence of sparsity: An NCD-based approach
Authors: Athanasios N. Nikolakopoulos, John D. Garofalakis
Date: 2015-06-30 00:00:00
Tasks: Collaborative Filtering, Diversity

Making recommendations in the presence of sparsity is known to present one of
the most challenging problems faced by collaborative filtering methods. In this
work we tackle this problem by exploiting the innately hierarchical structure
of the item space following an approach inspired by the theory of
Decomposability. We view the itemspace as a Nearly Decomposable system and we
define blocks of closely related elements and corresponding indirect proximity
components. We study the theoretical properties of the decomposition and we
derive sufficient conditions that guarantee full item space coverage even in
cold-start recommendation scenarios. A comprehensive set of experiments on the
MovieLens and the Yahoo!R2Music datasets, using several widely applied
performance metrics, support our model's theoretically predicted properties and
verify that NCDREC outperforms several state-of-the-art algorithms, in terms of
recommendation accuracy, diversity and sparseness insensitivity.