pwc-archive/papers-with-abstracts huggingface dataset:
Application of backpropagation neural networks to both stages of fingerprinting based WIPS
Authors: Caifa Zhou, Andreas Wieser
Date: 2017-03-14 00:00:00
Tasks: Position

We propose a scheme to employ backpropagation neural networks (BPNNs) for
both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal
strengths (FWIPS): radio map construction during the offline stage, and
localization during the online stage. Given a training radio map (TRM), i.e., a
set of coordinate vectors and associated WLAN/WiFi signal strengths of the
available access points, a BPNN can be trained to output the expected signal
strengths for any input position within the region of interest (BPNN-RM). This
can be used to provide a continuous representation of the radio map and to
filter, densify or decimate a discrete radio map. Correspondingly, the TRM can
also be used to train another BPNN to output the expected position within the
region of interest for any input vector of recorded signal strengths and thus
carry out localization (BPNN-LA).Key aspects of the design of such artificial
neural networks for a specific application are the selection of design
parameters like the number of hidden layers and nodes within the network, and
the training procedure. Summarizing extensive numerical simulations, based on
real measurements in a testbed, we analyze the impact of these design choices
on the performance of the BPNN and compare the results in particular to those
obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest
neighbors approaches to FWIPS.
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
Authors: Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M. Kaplan, Timothy P. Hanratty, Jiawei Han
Date: 2017-03-13 00:00:00
Tasks: Dependency Parsing

Mining textual patterns in news, tweets, papers, and many other kinds of text
corpora has been an active theme in text mining and NLP research. Previous
studies adopt a dependency parsing-based pattern discovery approach. However,
the parsing results lose rich context around entities in the patterns, and the
process is costly for a corpus of large scale. In this study, we propose a
novel typed textual pattern structure, called meta pattern, which is extended
to a frequent, informative, and precise subsequence pattern in certain context.
We propose an efficient framework, called MetaPAD, which discovers meta
patterns from massive corpora with three techniques: (1) it develops a
context-aware segmentation method to carefully determine the boundaries of
patterns with a learnt pattern quality assessment function, which avoids costly
dependency parsing and generates high-quality patterns; (2) it identifies and
groups synonymous meta patterns from multiple facets---their types, contexts,
and extractions; and (3) it examines type distributions of entities in the
instances extracted by each group of patterns, and looks for appropriate type
levels to make discovered patterns precise. Experiments demonstrate that our
proposed framework discovers high-quality typed textual patterns efficiently
from different genres of massive corpora and facilitates information
extraction.
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition
Authors: Cheng Zhao, Li Sun, Rustam Stolkin
Date: 2017-03-14 00:00:00
Tasks: 3D Reconstruction, GPU, Material Recognition

This paper addresses the problem of simultaneous 3D reconstruction and
material recognition and segmentation. Enabling robots to recognise different
materials (concrete, metal etc.) in a scene is important for many tasks, e.g.
robotic interventions in nuclear decommissioning. Previous work on 3D semantic
reconstruction has predominantly focused on recognition of everyday domestic
objects (tables, chairs etc.), whereas previous work on material recognition
has largely been confined to single 2D images without any 3D reconstruction.
Meanwhile, most 3D semantic reconstruction methods rely on computationally
expensive post-processing, using Fully-Connected Conditional Random Fields
(CRFs), to achieve consistent segmentations. In contrast, we propose a deep
learning method which performs 3D reconstruction while simultaneously
recognising different types of materials and labelling them at the pixel level.
Unlike previous methods, we propose a fully end-to-end approach, which does not
require hand-crafted features or CRF post-processing. Instead, we use only
learned features, and the CRF segmentation constraints are incorporated inside
the fully end-to-end learned system. We present the results of experiments, in
which we trained our system to perform real-time 3D semantic reconstruction for
23 different materials in a real-world application. The run-time performance of
the system can be boosted to around 10Hz, using a conventional GPU, which is
enough to achieve real-time semantic reconstruction using a 30fps RGB-D camera.
To the best of our knowledge, this work is the first real-time end-to-end
system for simultaneous 3D reconstruction and material recognition.
On the benefits of output sparsity for multi-label classification
Authors: Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Joseph Salmon
Date: 2017-03-14 00:00:00
Tasks: Classification, General Classification, Generalization Bounds, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The multi-label classification framework, where each observation can be
associated with a set of labels, has generated a tremendous amount of attention
over recent years. The modern multi-label problems are typically large-scale in
terms of number of observations, features and labels, and the amount of labels
can even be comparable with the amount of observations. In this context,
different remedies have been proposed to overcome the curse of dimensionality.
In this work, we aim at exploiting the output sparsity by introducing a new
loss, called the sparse weighted Hamming loss. This proposed loss can be seen
as a weighted version of classical ones, where active and inactive labels are
weighted separately. Leveraging the influence of sparsity in the loss function,
we provide improved generalization bounds for the empirical risk minimizer, a
suitable property for large-scale problems. For this new loss, we derive rates
of convergence linear in the underlying output-sparsity rather than linear in
the number of labels. In practice, minimizing the associated risk can be
performed efficiently by using convex surrogates and modern convex optimization
algorithms. We provide experiments on various real-world datasets demonstrating
the pertinence of our approach when compared to non-weighted techniques.
6-DoF Object Pose from Semantic Keypoints
Authors: Georgios Pavlakos, Xiaowei Zhou, Aaron Chan, Konstantinos G. Derpanis, Kostas Daniilidis
Date: 2017-03-14 00:00:00
Tasks: Keypoint Detection, Object, Pose Estimation, Translation

This paper presents a novel approach to estimating the continuous six degree
of freedom (6-DoF) pose (3D translation and rotation) of an object from a
single RGB image. The approach combines semantic keypoints predicted by a
convolutional network (convnet) with a deformable shape model. Unlike prior
work, we are agnostic to whether the object is textured or textureless, as the
convnet learns the optimal representation from the available training image
data. Furthermore, the approach can be applied to instance- and class-based
pose recovery. Empirically, we show that the proposed approach can accurately
recover the 6-DoF object pose for both instance- and class-based scenarios with
a cluttered background. For class-based object pose estimation,
state-of-the-art accuracy is shown on the large-scale PASCAL3D+ dataset.
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
Authors: Cheng Ju, Mary Combs, Samuel D Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. Van Der Laan
Date: 2017-03-07 00:00:00
Tasks: Ensemble Learning, Prediction, Variable Selection

The optimal learner for prediction modeling varies depending on the
underlying data-generating distribution. Super Learner (SL) is a generic
ensemble learning algorithm that uses cross-validation to select among a
"library" of candidate prediction models. The SL is not restricted to a single
prediction model, but uses the strengths of a variety of learning algorithms to
adapt to different databases. While the SL has been shown to perform well in a
number of settings, it has not been thoroughly evaluated in large electronic
healthcare databases that are common in pharmacoepidemiology and comparative
effectiveness research. In this study, we applied and evaluated the performance
of the SL in its ability to predict treatment assignment using three electronic
healthcare databases. We considered a library of algorithms that consisted of
both nonparametric and parametric models. We also considered a novel strategy
for prediction modeling that combines the SL with the high-dimensional
propensity score (hdPS) variable selection algorithm. Predictive performance
was assessed using three metrics: the negative log-likelihood, area under the
curve (AUC), and time complexity. Results showed that the best individual
algorithm, in terms of predictive performance, varied across datasets. The SL
was able to adapt to the given dataset and optimize predictive performance
relative to any individual learner. Combining the SL with the hdPS was the most
consistent prediction method and may be promising for PS estimation and
prediction modeling in electronic healthcare databases.
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
Authors: Alexander Broad, Brenna Argall
Date: 2017-03-14 00:00:00
Tasks: Novel Object Detection, Object, object-detection, Object Detection, Object Recognition, Region Proposal

We present a novel object detection pipeline for localization and recognition
in three dimensional environments. Our approach makes use of an RGB-D sensor
and combines state-of-the-art techniques from the robotics and computer vision
communities to create a robust, real-time detection system. We focus
specifically on solving the object detection problem for tabletop scenes, a
common environment for assistive manipulators. Our detection pipeline locates
objects in a point cloud representation of the scene. These clusters are
subsequently used to compute a bounding box around each object in the RGB
space. Each defined patch is then fed into a Convolutional Neural Network (CNN)
for object recognition. We also demonstrate that our region proposal method can
be used to develop novel datasets that are both large and diverse enough to
train deep learning models, and easy enough to collect that end-users can
develop their own datasets. Lastly, we validate the resulting system through an
extensive analysis of the accuracy and run-time of the full pipeline.
Optimal Densification for Fast and Accurate Minwise Hashing
Authors: Anshumali Shrivastava
Date: 2017-03-14 00:00:00
Proceeding: ICML 2017 8

Minwise hashing is a fundamental and one of the most successful hashing
algorithm in the literature. Recent advances based on the idea of
densification~\cite{Proc:OneHashLSH_ICML14,Proc:Shrivastava_UAI14} have shown
that it is possible to compute $k$ minwise hashes, of a vector with $d$
nonzeros, in mere $(d + k)$ computations, a significant improvement over the
classical $O(dk)$. These advances have led to an algorithmic improvement in the
query complexity of traditional indexing algorithms based on minwise hashing.
Unfortunately, the variance of the current densification techniques is
unnecessarily high, which leads to significantly poor accuracy compared to
vanilla minwise hashing, especially when the data is sparse. In this paper, we
provide a novel densification scheme which relies on carefully tailored
2-universal hashes. We show that the proposed scheme is variance-optimal, and
without losing the runtime efficiency, it is significantly more accurate than
existing densification techniques. As a result, we obtain a significantly
efficient hashing scheme which has the same variance and collision probability
as minwise hashing. Experimental evaluations on real sparse and
high-dimensional datasets validate our claims. We believe that given the
significant advantages, our method will replace minwise hashing implementations
in practice.
A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)
Authors: G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic, Michael A. Groeber, Gregery T. Buzzard, Charles A. Bouman
Date: 2017-03-14 00:00:00
Tasks: regression

Sparse sampling schemes have the potential to dramatically reduce image
acquisition time while simultaneously reducing radiation damage to samples.
However, for a sparse sampling scheme to be useful it is important that we are
able to reconstruct the underlying object with sufficient clarity using the
sparse measurements. In dynamic sampling, each new measurement location is
selected based on information obtained from previous measurements. Therefore,
dynamic sampling schemes have the potential to dramatically reduce the number
of measurements needed for high fidelity reconstructions. However, most
existing dynamic sampling methods for point-wise measurement acquisition tend
to be computationally expensive and are therefore too slow for practical
applications.
  In this paper, we present a framework for dynamic sampling based on machine
learning techniques, which we call a supervised learning approach for dynamic
sampling (SLADS). In each step of SLADS, the objective is to find the pixel
that maximizes the expected reduction in distortion (ERD) given previous
measurements. SLADS is fast because we use a simple regression function to
compute the ERD, and it is accurate because the regression function is trained
using data sets that are representative of the specific application. In
addition, we introduce a method to terminate dynamic sampling at a desired
level of distortion, and we extended the SLADS methodology to sample groups of
pixels at each step. Finally, we present results on computationally-generated
synthetic data and experimentally-collected data to demonstrate a dramatic
improvement over state-of-the-art static sampling methods.
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
Authors: Luca D'Amiano, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva
Date: 2017-03-14 00:00:00

We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-field approach, with invariant features that guarantee
robustness to several post-processing operations. To limit complexity, a
suitable video-oriented version of PatchMatch is used, with a multiresolution
search strategy, and a focus on volumes of interest. Performance assessment
relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide
variety of challenging situations. Experimental results show the proposed
method to detect and localize video copy-moves with good accuracy even in
adverse conditions.
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Authors: Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva
Date: 2017-03-14 00:00:00
Tasks: Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely
effective for a number of forensic applications, like forgery detection and
localization. Nonetheless, motivated by promising results in computer vision,
the focus of the research community is now shifting on deep learning. In this
paper we show that a class of residual-based descriptors can be actually
regarded as a simple constrained convolutional neural network (CNN). Then, by
relaxing the constraints, and fine-tuning the net on a relatively small
training set, we obtain a significant performance improvement with respect to
the conventional detector.
Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making
Authors: Qi Zhang, Satinder Singh, Edmund Durfee
Date: 2017-03-14 00:00:00
Tasks: Decision Making, Sequential Decision Making

In cooperative multiagent planning, it can often be beneficial for an agent
to make commitments about aspects of its behavior to others, allowing them in
turn to plan their own behaviors without taking the agent's detailed behavior
into account. Extending previous work in the Bayesian setting, we consider
instead a worst-case setting in which the agent has a set of possible
environments (MDPs) it could be in, and develop a commitment semantics that
allows for probabilistic guarantees on the agent's behavior in any of the
environments it could end up facing. Crucially, an agent receives observations
(of reward and state transitions) that allow it to potentially eliminate
possible environments and thus obtain higher utility by adapting its policy to
the history of observations. We develop algorithms and provide theory and some
preliminary empirical results showing that they ensure an agent meets its
commitments with history-dependent policies while minimizing maximum regret
over the possible environments.
Multi-objective design of quantum circuits using genetic programming
Authors: Moein Sarvaghad-Moghaddam
Date: 2016-04-03 00:00:00
Tasks: Evolutionary Algorithms

Quantum computing is a new way of data processing based on the concept of
quantum mechanics. Quantum circuit design is a process of converting a quantum
gate to a series of basic gates and is divided into two general categories
based on the decomposition and composition. In the second group, using
evolutionary algorithms and especially genetic algorithms, multiplication of
matrix gates was used to achieve the final characteristic of quantum circuit.
Genetic programming is a subfield of evolutionary computing in which computer
programs evolve to solve studied problems. In past research that has been done
in the field of quantum circuits design, only one cost metrics (usually quantum
cost) has been investigated. In this paper for the first time, a
multi-objective approach has been provided to design quantum circuits using
genetic programming that considers the depth and the cost of nearest neighbor
metrics in addition to quantum cost metric. Another innovation of this article
is the use of two-step fitness function and taking into account the equivalence
of global phase in quantum gates. The results show that the proposed method is
able to find a good answer in a short time.
II-FCN for skin lesion analysis towards melanoma detection
Authors: Hongdiao Wen
Date: 2017-02-28 00:00:00
Tasks: Lesion Segmentation

Dermoscopy image detection stays a tough task due to the weak distinguishable
property of the object.Although the deep convolution neural network
signifigantly boosted the performance on prevelance computer vision tasks in
recent years,there remains a room to explore more robust and precise models to
the problem of low contrast image segmentation.Towards the challenge of Lesion
Segmentation in ISBI 2017,we built a symmetrical identity inception fully
convolution network which is based on only 10 reversible inception blocks,every
block composed of four convolution branches with combination of different layer
depth and kernel size to extract sundry semantic features.Then we proposed an
approximate loss function for jaccard index metrics to train our model.To
overcome the drawbacks of traditional convolution,we adopted the dilation
convolution and conditional random field method to rectify our segmentation.We
also introduced multiple ways to prevent the problem of overfitting.The
experimental results shows that our model achived jaccard index of 0.82 and
kept learning from epoch to epoch.
Improving Chinese SRL with Heterogeneous Annotations
Authors: Qiaolin Xia, Baobao Chang, Zhifang Sui
Date: 2017-02-22 00:00:00
Tasks: Chinese Semantic Role Labeling, Semantic Role Labeling

Previous studies on Chinese semantic role labeling (SRL) have concentrated on
single semantically annotated corpus. But the training data of single corpus is
often limited. Meanwhile, there usually exists other semantically annotated
corpora for Chinese SRL scattered across different annotation frameworks. Data
sparsity remains a bottleneck. This situation calls for larger training
datasets, or effective approaches which can take advantage of highly
heterogeneous data. In these papers, we focus mainly on the latter, that is, to
improve Chinese SRL by using heterogeneous corpora together. We propose a novel
progressive learning model which augments the Progressive Neural Network with
Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and
effectively transfer knowledge between them. We also release a new corpus,
Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that ours model
outperforms state-of-the-art methods.
A New Bengali Readability Score
Authors: Shanta Phani, Shibamouli Lahiri, Arindam Biswas
Date: 2016-07-10 00:00:00

In this paper we have proposed methods to analyze the readability of Bengali
language texts. We have got some exceptionally good results out of the
experiments.
Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization
Authors: Vinícius Veloso de Melo, Wolfgang Banzhaf
Date: 2017-03-14 00:00:00
Tasks: global-optimization

This paper proposes Drone Squadron Optimization, a new self-adaptive
metaheuristic for global numerical optimization which is updated online by a
hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many
algorithms used nowadays, which are nature-inspired. DSO is very flexible
because it is not related to behaviors or natural phenomena. DSO has two core
parts: the semi-autonomous drones that fly over a landscape to explore, and the
Command Center that processes the retrieved data and updates the drones'
firmware whenever necessary. The self-adaptive aspect of DSO in this work is
the perturbation/movement scheme, which is the procedure used to generate
target coordinates. This procedure is evolved by the Command Center during the
global optimization process in order to adapt DSO to the search landscape. DSO
was evaluated on a set of widely employed benchmark functions. The statistical
analysis of the results shows that the proposed method is competitive with the
other methods in the comparison, the performance is promising, but several
future improvements are planned.
Time-dependent Hierarchical Dirichlet Model for Timeline Generation
Authors: Rumeng Li, Tao Wang, Xun Wang
Date: 2013-12-08 00:00:00
Tasks: Novelty Detection, Sentence

Timeline Generation aims at summarizing news from different epochs and
telling readers how an event evolves. It is a new challenge that combines
salience ranking with novelty detection. For long-term public events, the main
topic usually includes various aspects across different epochs and each aspect
has its own evolving pattern. Existing approaches neglect such hierarchical
topic structure involved in the news corpus in timeline generation. In this
paper, we develop a novel time-dependent Hierarchical Dirichlet Model (HDM) for
timeline generation. Our model can aptly detect different levels of topic
information across corpus and such structure is further used for sentence
selection. Based on the topic mined fro HDM, sentences are selected by
considering different aspects such as relevance, coherence and coverage. We
develop experimental systems to evaluate 8 long-term events that public
concern. Performance comparison between different systems demonstrates the
effectiveness of our model in terms of ROUGE metrics.