pwc-archive/papers-with-abstracts huggingface dataset:
Model-based Test Generation for Robotic Software: Automata versus Belief-Desire-Intention Agents
Authors: Dejanira Araiza-Illan, Anthony G. Pipe, Kerstin Eder
Date: 2016-09-16 00:00:00

Robotic code needs to be verified to ensure its safety and functional
correctness, especially when the robot is interacting with people. Testing real
code in simulation is a viable option. However, generating tests that cover
rare scenarios, as well as exercising most of the code, is a challenge
amplified by the complexity of the interactions between the environment and the
software. Model-based test generation methods can automate otherwise manual
processes and facilitate reaching rare scenarios during testing. In this paper,
we compare using Belief-Desire-Intention (BDI) agents as models for test
generation with more conventional automata-based techniques that exploit model
checking, in terms of practicality, performance, transferability to different
scenarios, and exploration (`coverage'), through two case studies: a
cooperative manufacturing task, and a home care scenario. The results highlight
the advantages of using BDI agents for test generation. BDI agents naturally
emulate the agency present in Human-Robot Interactions (HRIs), and are thus
more expressive than automata. The performance of the BDI-based test generation
is at least as high, and the achieved coverage is higher or equivalent,
compared to test generation based on model checking automata.
Search Personalization with Embeddings
Authors: Thanh Vu, Dat Quoc Nguyen, Mark Johnson, Dawei Song, Alistair Willis
Date: 2016-12-12 00:00:00

Recent research has shown that the performance of search personalization
depends on the richness of user profiles which normally represent the user's
topical interests. In this paper, we propose a new embedding approach to
learning user profiles, where users are embedded on a topical interest space.
We then directly utilize the user profiles for search personalization.
Experiments on query logs from a major commercial web search engine demonstrate
that our embedding approach improves the performance of the search engine and
also achieves better search performance than other strong baselines.
A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics
Authors: Friederike Laus, Mila Nikolova, Johannes Persch, Gabriele Steidl
Date: 2016-07-28 00:00:00
Tasks: Denoising

Nonlocal patch-based methods, in particular the Bayes' approach of Lebrun,
Buades and Morel (2013), are considered as state-of-the-art methods for
denoising (color) images corrupted by white Gaussian noise of moderate
variance. This paper is the first attempt to generalize this technique to
manifold-valued images. Such images, for example images with phase or
directional entries or with values in the manifold of symmetric positive
definite matrices, are frequently encountered in real-world applications.
Generalizing the normal law to manifolds is not canonical and different
attempts have been considered. Here we focus on a straightforward intrinsic
model and discuss the relation to other approaches for specific manifolds. We
reinterpret the Bayesian approach of Lebrun et al. (2013) in terms of minimum
mean squared error estimation, which motivates our definition of a
corresponding estimator on the manifold. With this estimator at hand we present
a nonlocal patch-based method for the restoration of manifold-valued images.
Various proof of concept examples demonstrate the potential of the proposed
algorithm.
An Artificial Neural Networks based Temperature Prediction Framework for Network-on-Chip based Multicore Platform
Authors: Sandeep Aswath Narayana
Date: 2016-12-12 00:00:00
Tasks: Management

Continuous improvement in silicon process technologies has made possible the
integration of hundreds of cores on a single chip. However, power and heat have
become dominant constraints in designing these massive multicore chips causing
issues with reliability, timing variations and reduced lifetime of the chips.
Dynamic Thermal Management (DTM) is a solution to avoid high temperatures on
the die. Typical DTM schemes only address core level thermal issues. However,
the Network-on-chip (NoC) paradigm, which has emerged as an enabling
methodology for integrating hundreds to thousands of cores on the same die can
contribute significantly to the thermal issues. Moreover, the typical DTM is
triggered reactively based on temperature measurements from on-chip thermal
sensor requiring long reaction times whereas predictive DTM method estimates
future temperature in advance, eliminating the chance of temperature overshoot.
Artificial Neural Networks (ANNs) have been used in various domains for
modeling and prediction with high accuracy due to its ability to learn and
adapt. This thesis concentrates on designing an ANN prediction engine to
predict the thermal profile of the cores and Network-on-Chip elements of the
chip. This thermal profile of the chip is then used by the predictive DTM that
combines both core level and network level DTM techniques. On-chip wireless
interconnect which is recently envisioned to enable energy-efficient data
exchange between cores in a multicore environment, will be used to provide a
broadcast-capable medium to efficiently distribute thermal control messages to
trigger and manage the DTM schemes.
Text-guided Attention Model for Image Captioning
Authors: Jonghwan Mun, Minsu Cho, Bohyung Han
Date: 2016-12-12 00:00:00
Tasks: Image Captioning, model

Visual attention plays an important role to understand images and
demonstrates its effectiveness in generating natural language descriptions of
images. On the other hand, recent studies show that language associated with an
image can steer visual attention in the scene during our cognitive process.
Inspired by this, we introduce a text-guided attention model for image
captioning, which learns to drive visual attention using associated captions.
For this model, we propose an exemplar-based learning approach that retrieves
from training data associated captions with each image, and use them to learn
attention on visual features. Our attention model enables to describe a
detailed state of scenes by distinguishing small or confusable objects
effectively. We validate our model on MS-COCO Captioning benchmark and achieve
the state-of-the-art performance in standard metrics.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
Authors: Dongkuan Xu, Jia Wu, Wei zhang, Yingjie Tian
Date: 2016-12-12 00:00:00
Tasks: Multiple Instance Learning

Positive instance detection, especially for these in positive bags (true
positive instances, TPIs), plays a key role for multiple instance learning
(MIL) arising from a specific classification problem only provided with bag (a
set of instances) label information. However, most previous MIL methods on this
issue ignore the global similarity among positive instances and that negative
instances are non-i.i.d., usually resulting in the detection of TPI not precise
and sensitive to outliers. To the end, we propose a positive instance detection
via graph updating for multiple instance learning, called PIGMIL, to detect TPI
accurately. PIGMIL selects instances from working sets (WSs) of some working
bags (WBs) as positive candidate pool (PCP). The global similarity among
positive instances and the robust discrimination of instances of PCP from
negative instances are measured to construct the consistent similarity and
discrimination graph (CSDG). As a result, the primary goal (i.e. TPI detection)
is transformed into PCP updating, which is approximated efficiently by updating
CSDG with a random walk ranking algorithm and an instance updating strategy. At
last bags are transformed into feature representation vector based on the
identified TPIs to train a classifier. Extensive experiments demonstrate the
high precision of PIGMIL's detection of TPIs and its excellent performance
compared to classic baseline MIL methods.
Quantification of observed prior and likelihood information in parametric Bayesian modeling
Authors: Giri Gopalan
Date: 2015-11-04 00:00:00
Tasks: Diagnostic

Two data-dependent information metrics are developed to quantify the
information of the prior and likelihood functions within a parametric Bayesian
model, one of which is closely related to the reference priors from Berger,
Bernardo, and Sun, and information measure introduced by Lindley. A combination
of theoretical, empirical, and computational support provides evidence that
these information-theoretic metrics may be useful diagnostic tools when
performing a Bayesian analysis.
Flu Detector: Estimating influenza-like illness rates from online user-generated content
Authors: Vasileios Lampos
Date: 2016-12-11 00:00:00

We provide a brief technical description of an online platform for disease
monitoring, titled as the Flu Detector (fludetector.cs.ucl.ac.uk). Flu
Detector, in its current version (v.0.5), uses either Twitter or Google search
data in conjunction with statistical Natural Language Processing models to
estimate the rate of influenza-like illness in the population of England. Its
back-end is a live service that collects online data, utilises modern
technologies for large-scale text processing, and finally applies statistical
inference models that are trained offline. The front-end visualises the various
disease rate estimates. Notably, the models based on Google data achieve a high
level of accuracy with respect to the most recent four flu seasons in England
(2012/13 to 2015/16). This highlighted Flu Detector as having a great potential
of becoming a complementary source to the domestic traditional flu surveillance
schemes.
Clustering under Perturbation Resilience
Authors: Maria Florina Balcan, YIngyu Liang
Date: 2011-12-05 00:00:00
Tasks: Clustering

Motivated by the fact that distances between data points in many real-world
clustering instances are often based on heuristic measures, Bilu and
Linial~\cite{BL} proposed analyzing objective based clustering problems under
the assumption that the optimum clustering to the objective is preserved under
small multiplicative perturbations to distances between points. The hope is
that by exploiting the structure in such instances, one can overcome worst case
hardness results.
  In this paper, we provide several results within this framework. For
center-based objectives, we present an algorithm that can optimally cluster
instances resilient to perturbations of factor $(1 + \sqrt{2})$, solving an
open problem of Awasthi et al.~\cite{ABS10}. For $k$-median, a center-based
objective of special interest, we additionally give algorithms for a more
relaxed assumption in which we allow the optimal solution to change in a small
$\epsilon$ fraction of the points after perturbation. We give the first bounds
known for $k$-median under this more realistic and more general assumption. We
also provide positive results for min-sum clustering which is typically a
harder objective than center-based objectives from approximability standpoint.
Our algorithms are based on new linkage criteria that may be of independent
interest.
  Additionally, we give sublinear-time algorithms, showing algorithms that can
return an implicit clustering from only access to a small random sample.
How smart does your profile image look? Estimating intelligence from social network profile images
Authors: Xingjie Wei, David Stillwell
Date: 2016-06-29 00:00:00

Profile images on social networks are users' opportunity to present
themselves and to affect how others judge them. We examine what Facebook images
say about users' perceived and measured intelligence. 1,122 Facebook users
completed a matrices intelligence test and shared their current Facebook
profile image. Strangers also rated the images for perceived intelligence. We
use automatically extracted image features to predict both measured and
perceived intelligence. Intelligence estimation from images is a difficult task
even for humans, but experimental results show that human accuracy can be
equalled using computing methods. We report the image features that predict
both measured and perceived intelligence, and highlight misleading features
such as "smiling" and "wearing glasses" that are correlated with perceived but
not measured intelligence. Our results give insights into inaccurate
stereotyping from profile images and also have implications for privacy,
especially since in most social networks profile images are public by default.
Self-calibrating Neural Networks for Dimensionality Reduction
Authors: Yuansi Chen, Cengiz Pehlevan, Dmitri B. Chklovskii
Date: 2016-12-11 00:00:00
Tasks: Dimensionality Reduction

Recently, a novel family of biologically plausible online algorithms for
reducing the dimensionality of streaming data has been derived from the
similarity matching principle. In these algorithms, the number of output
dimensions can be determined adaptively by thresholding the singular values of
the input data matrix. However, setting such threshold requires knowing the
magnitude of the desired singular values in advance. Here we propose online
algorithms where the threshold is self-calibrating based on the singular values
computed from the existing observations. To derive these algorithms from the
similarity matching cost function we propose novel regularizers. As before,
these online algorithms can be implemented by Hebbian/anti-Hebbian neural
networks in which the learning rule depends on the chosen regularizer. We
demonstrate both mathematically and via simulation the effectiveness of these
online algorithms in various settings.
On Choosing Training and Testing Data for Supervised Algorithms in Ground Penetrating Radar Data for Buried Threat Detection
Authors: Daniël Reichman, Leslie M. Collins, Jordan M. Malof
Date: 2016-12-11 00:00:00
Tasks: GPR, Landmine

Ground penetrating radar (GPR) is one of the most popular and successful
sensing modalities that has been investigated for landmine and subsurface
threat detection. Many of the detection algorithms applied to this task are
supervised and therefore require labeled examples of target and non-target data
for training. Training data most often consists of 2-dimensional images (or
patches) of GPR data, from which features are extracted, and provided to the
classifier during training and testing. Identifying desirable training and
testing locations to extract patches, which we term "keypoints", is well
established in the literature. In contrast however, a large variety of
strategies have been proposed regarding keypoint utilization (e.g., how many of
the identified keypoints should be used at targets, or non-target, locations).
Given the variety keypoint utilization strategies that are available, it is
very unclear (i) which strategies are best, or (ii) whether the choice of
strategy has a large impact on classifier performance. We address these
questions by presenting a taxonomy of existing utilization strategies, and then
evaluating their effectiveness on a large dataset using many different
classifiers and features. We analyze the results and propose a new strategy,
called PatchSelect, which outperforms other strategies across all experiments.
A Multiplierless Pruned DCT-like Transformation for Image and Video Compression that Requires 10 Additions Only
Authors: V. A. Coutinho, R. J. Cintra, F. M. Bayer, S. Kulasekera, A. Madanayake
Date: 2014-02-24 00:00:00
Tasks: Video Compression

A multiplierless pruned approximate 8-point discrete cosine transform (DCT)
requiring only 10 additions is introduced. The proposed algorithm was assessed
in image and video compression, showing competitive performance with
state-of-the-art methods. Digital implementation in 45 nm CMOS technology up to
place-and-route level indicates clock speed of 288 MHz at a 1.1 V supply. The
8x8 block rate is 36 MHz.The DCT approximation was embedded into HEVC reference
software; resulting video frames, at up to 327 Hz for 8-bit RGB HEVC, presented
negligible image degradation.
Lock-Free Optimization for Non-Convex Problems
Authors: Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li
Date: 2016-12-11 00:00:00

Stochastic gradient descent~(SGD) and its variants have attracted much
attention in machine learning due to their efficiency and effectiveness for
optimization. To handle large-scale problems, researchers have recently
proposed several lock-free strategy based parallel SGD~(LF-PSGD) methods for
multi-core systems. However, existing works have only proved the convergence of
these LF-PSGD methods for convex problems. To the best of our knowledge, no
work has proved the convergence of the LF-PSGD methods for non-convex problems.
In this paper, we provide the theoretical proof about the convergence of two
representative LF-PSGD methods, Hogwild! and AsySVRG, for non-convex problems.
Empirical results also show that both Hogwild! and AsySVRG are convergent on
non-convex problems, which successfully verifies our theoretical results.
SCOPE: Scalable Composite Optimization for Learning on Spark
Authors: Shen-Yi Zhao, Ru Xiang, Ying-Hao Shi, Peng Gao, Wu-Jun Li
Date: 2016-01-30 00:00:00
Tasks: Stochastic Optimization

Many machine learning models, such as logistic regression~(LR) and support
vector machine~(SVM), can be formulated as composite optimization problems.
Recently, many distributed stochastic optimization~(DSO) methods have been
proposed to solve the large-scale composite optimization problems, which have
shown better performance than traditional batch methods. However, most of these
DSO methods are not scalable enough. In this paper, we propose a novel DSO
method, called \underline{s}calable \underline{c}omposite
\underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it
on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both
computation-efficient and communication-efficient. Theoretical analysis shows
that SCOPE is convergent with linear convergence rate when the objective
function is convex. Furthermore, empirical results on real datasets show that
SCOPE can outperform other state-of-the-art distributed learning methods on
Spark, including both batch learning methods and DSO methods.
Identity Matters in Deep Learning
Authors: Moritz Hardt, Tengyu Ma
Date: 2016-11-14 00:00:00
Tasks: Deep Learning

An emerging design principle in deep learning is that each layer of a deep
artificial neural network should be able to easily express the identity
transformation. This idea not only motivated various normalization techniques,
such as \emph{batch normalization}, but was also key to the immense success of
\emph{residual networks}.
  In this work, we put the principle of \emph{identity parameterization} on a
more solid theoretical footing alongside further empirical progress. We first
give a strikingly simple proof that arbitrarily deep linear residual networks
have no spurious local optima. The same result for linear feed-forward networks
in their standard parameterization is substantially more delicate. Second, we
show that residual networks with ReLu activations have universal finite-sample
expressivity in the sense that the network can represent any function of its
sample provided that the model has more parameters than the sample size.
  Directly inspired by our theory, we experiment with a radically simple
residual architecture consisting of only residual convolutional layers and ReLu
activations, but no batch normalization, dropout, or max pool. Our model
improves significantly on previous all-convolutional networks on the CIFAR10,
CIFAR100, and ImageNet classification benchmarks.
Adams Conditioning and Likelihood Ratio Transfer Mediated Inference
Authors: Jan A. Bergstra
Date: 2016-11-26 00:00:00
Tasks: Bayesian Inference

Bayesian inference as applied in a legal setting is about belief transfer and involves a plurality of agents and communication protocols. A forensic expert (FE) may communicate to a trier of fact (TOF) first its value of a certain likelihood ratio with respect to FE's belief state as represented by a probability function on FE's proposition space. Subsequently FE communicates its recently acquired confirmation that a certain evidence proposition is true. Then TOF performs likelihood ratio transfer mediated reasoning thereby revising their own belief state. The logical principles involved in likelihood transfer mediated reasoning are discussed in a setting where probabilistic arithmetic is done within a meadow, and with Adams conditioning placed in a central role.
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources
Authors: Jie Wang, Luyan Ji, Xiaomeng Huang, Haohuan Fu, Shiming Xu, Cong-Cong Li
Date: 2016-12-11 00:00:00
Tasks: Time Series Analysis

There is a trend to acquire high accuracy land-cover maps using multi-source
classification methods, most of which are based on data fusion, especially
pixel- or feature-level fusions. A probabilistic graphical model (PGM) approach
is proposed in this research for 30 m resolution land-cover mapping with
multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer
(MODIS) data. Independent classifiers were applied to two single-date Landsat 8
scenes and the MODIS time-series data, respectively, for probability
estimation. A PGM was created for each pixel in Landsat 8 data. Conditional
probability distributions were computed based on data quality and reliability
by using information selectively. Using the administrative territory of Beijing
City (Area-1) and a coastal region of Shandong province, China (Area-2) as
study areas, multiple land-cover maps were generated for comparison.
Quantitative results show the effectiveness of the proposed method. Overall
accuracies promoted from 74.0% (maps acquired from single-temporal Landsat
images) to 81.8% (output of the PGM) for Area-1. Improvements can also be seen
when using MODIS data and only a single-temporal Landsat image as input
(overall accuracy: 78.4% versus 74.0% for Area-1, and 86.8% versus 83.0% for
Area-2). Information from MODIS data did not help much when the PGM was applied
to cloud free regions of. One of the advantages of the proposed method is that
it can be applied where multi-temporal data cannot be simply stacked as a
multi-layered image.
A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection
Authors: Sunil Kumar, J. V. Desai, Shaktidev Mukherjee
Date: 2016-12-11 00:00:00
Tasks: Image Forensics, Keypoint Detection

Copy move forgery detection in digital images has become a very popular
research topic in the area of image forensics. Due to the availability of
sophisticated image editing tools and ever increasing hardware capabilities, it
has become an easy task to manipulate the digital images. Passive forgery
detection techniques are more relevant as they can be applied without the prior
information about the image in question. Block based techniques are used to
detect copy move forgery, but have limitations of large time complexity and
sensitivity against affine operations like rotation and scaling. Keypoint based
approaches are used to detect forgery in large images where the possibility of
significant post processing operations like rotation and scaling is more. A
hybrid approach is proposed using different methods for keypoint detection and
description. Speeded Up Robust Features (SURF) are used to detect the keypoints
in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features
are used to describe features at these keypoints. The proposed method has
performed better than the existing forgery detection method using SURF
significantly in terms of detection speed and is invariant to post processing
operations like rotation and scaling. The proposed method is also invariant to
other commonly applied post processing operations like adding Gaussian noise
and JPEG compression