pwc-archive/papers-with-abstracts huggingface dataset:
Building a Pilot Software Quality-in-Use Benchmark Dataset
Authors: Issa Atoum, Chih How Bong, Narayanan Kulathuramaiyer
Date: 2015-09-18 00:00:00
Tasks: Sentence, Sentiment Analysis

Prepared domain specific datasets plays an important role to supervised
learning approaches. In this article a new sentence dataset for software
quality-in-use is proposed. Three experts were chosen to annotate the data
using a proposed annotation scheme. Then the data were reconciled in a (no
match eliminate) process to reduce bias. The Kappa, k statistics revealed an
acceptable level of agreement; moderate to substantial agreement between the
experts. The built data can be used to evaluate software quality-in-use models
in sentiment analysis models. Moreover, the annotation scheme can be used to
extend the current dataset.
Convolutional Color Constancy
Authors: Jonathan T. Barron
Date: 2015-07-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: Color Constancy, object-detection, Object Detection, Structured Prediction

Color constancy is the problem of inferring the color of the light that
illuminated a scene, usually so that the illumination color can be removed.
Because this problem is underconstrained, it is often solved by modeling the
statistical regularities of the colors of natural objects and illumination. In
contrast, in this paper we reformulate the problem of color constancy as a 2D
spatial localization task in a log-chrominance space, thereby allowing us to
apply techniques from object detection and structured prediction to the color
constancy problem. By directly learning how to discriminate between correctly
white-balanced images and poorly white-balanced images, our model is able to
improve performance on standard benchmarks by nearly 40%.
Energy saving in smart homes based on consumer behaviour: A case study
Authors: Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez
Date: 2015-09-18 00:00:00
Tasks: Recommendation Systems

This paper presents a case study of a recommender system that can be used to
save energy in smart homes without lowering the comfort of the inhabitants. We
present an algorithm that uses consumer behavior data only and uses machine
learning to suggest actions for inhabitants to reduce the energy consumption of
their homes. The system mines for frequent and periodic patterns in the event
data provided by the Digitalstrom home automation system. These patterns are
converted into association rules, prioritized and compared with the current
behavior of the inhabitants. If the system detects an opportunities to save
energy without decreasing the comfort level it sends a recommendation to the
residents.
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
Authors: Ming Xu, Jianping Wu, Yiman Du, Haohan Wang, Geqi Qi, Kezhen Hu, Yun-Peng Xiao
Date: 2014-07-09 00:00:00

A major problem in road network analysis is discovery of important
crossroads, which can provide useful information for transport planning.
However, none of existing approaches addresses the problem of identifying
network-wide important crossroads in real road network. In this paper, we
propose a novel data-driven based approach named CRRank to rank important
crossroads. Our key innovation is that we model the trip network reflecting
real travel demands with a tripartite graph, instead of solely analysis on the
topology of road network. To compute the importance scores of crossroads
accurately, we propose a HITS-like ranking algorithm, in which a procedure of
score propagation on our tripartite graph is performed. We conduct experiments
on CRRank using a real-world dataset of taxi trajectories. Experiments verify
the utility of CRRank.
Computational evolution of decision-making strategies
Authors: Peter Kvam, Joseph Cesario, Jory Schossau, Heather Eisthen, Arend Hintze
Date: 2015-09-18 00:00:00
Tasks: Decision Making

Most research on adaptive decision-making takes a strategy-first approach,
proposing a method of solving a problem and then examining whether it can be
implemented in the brain and in what environments it succeeds. We present a
method for studying strategy development based on computational evolution that
takes the opposite approach, allowing strategies to develop in response to the
decision-making environment via Darwinian evolution. We apply this approach to
a dynamic decision-making problem where artificial agents make decisions about
the source of incoming information. In doing so, we show that the complexity of
the brains and strategies of evolved agents are a function of the environment
in which they develop. More difficult environments lead to larger brains and
more information use, resulting in strategies resembling a sequential sampling
approach. Less difficult environments drive evolution toward smaller brains and
less information use, resulting in simpler heuristic-like strategies.
Visual Generalized Coordinates
Authors: M. Seetha Ramaiah, Amitabha Mukerjee, Arindam Chakraborty, Sadbodh Sharma
Date: 2015-09-18 00:00:00

An open problem in robotics is that of using vision to identify a robot's own
body and the world around it. Many models attempt to recover the traditional
C-space parameters. Instead, we propose an alternative C-space by deriving
generalized coordinates from $n$ images of the robot. We show that the space of
such images is bijective to the motion space, so these images lie on a manifold
$\mathcal{V}$ homeomorphic to the canonical C-space. We now approximate this
manifold as a set of $n$ neighbourhood tangent spaces that result in a graph,
which we call the Visual Roadmap (VRM). Given a new robot image, we perform
inverse kinematics visually by interpolating between nearby images in the image
space. Obstacles are projected onto the VRM in $O(n)$ time by superimposition
of images, leading to the identification of collision poses. The edges joining
the free nodes can now be checked with a visual local planner, and free-space
motions computed in $O(nlogn)$ time. This enables us to plan paths in the image
space for a robot manipulator with unknown link geometries, DOF, kinematics,
obstacles, and camera pose. We sketch the proofs for the main theoretical
ideas, identify the assumptions, and demonstrate the approach for both
articulated and mobile robots. We also investigate the feasibility of the
process by investigating various metrics and image sampling densities, and
demonstrate it on simulated and real robots.
A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones
Authors: Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson, Vitorino Ramos
Date: 2015-07-30 00:00:00

A model of an Ant System where ants are controlled by a spiking neural
circuit and a second order pheromone mechanism in a foraging task is presented.
A neural circuit is trained for individual ants and subsequently the ants are
exposed to a virtual environment where a swarm of ants performed a resource
foraging task. The model comprises an associative and unsupervised learning
strategy for the neural circuit of the ant. The neural circuit adapts to the
environment by means of classical conditioning. The initially unknown
environment includes different types of stimuli representing food and obstacles
which, when they come in direct contact with the ant, elicit a reflex response
in the motor neural system of the ant: moving towards or away from the source
of the stimulus. The ants are released on a landscape with multiple food
sources where one ant alone would have difficulty harvesting the landscape to
maximum efficiency. The introduction of a double pheromone mechanism yields
better results than traditional ant colony optimization strategies. Traditional
ant systems include mainly a positive reinforcement pheromone. This approach
uses a second pheromone that acts as a marker for forbidden paths (negative
feedback). This blockade is not permanent and is controlled by the evaporation
rate of the pheromones. The combined action of both pheromones acts as a
collective stigmergic memory of the swarm, which reduces the search space of
the problem. This paper explores how the adaptation and learning abilities
observed in biologically inspired cognitive architectures is synergistically
enhanced by swarm optimization strategies. The model portraits two forms of
artificial intelligent behaviour: at the individual level the spiking neural
network is the main controller and at the collective level the pheromone
distribution is a map towards the solution emerged by the colony.
Linearized Kernel Dictionary Learning
Authors: Alona Golts, Michael Elad
Date: 2015-09-18 00:00:00
Tasks: Dictionary Learning, General Classification

In this paper we present a new approach of incorporating kernels into
dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been
introduced recently, shows an improvement in classification performance, with
relation to its linear counterpart K-SVD. However, this algorithm requires the
storage and handling of a very large kernel matrix, which leads to high
computational cost, while also limiting its use to setups with small number of
training examples. We address these problems by combining two ideas: first we
approximate the kernel matrix using a cleverly sampled subset of its columns
using the Nystr\"{o}m method; secondly, as we wish to avoid using this matrix
altogether, we decompose it by SVD to form new "virtual samples," on which any
linear dictionary learning can be employed. Our method, termed "Linearized
Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a
pre-processing stage on top of any efficient off-the-shelf dictionary learning
scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our
method on several tasks of both supervised and unsupervised classification and
show the efficiency of the proposed scheme, its easy integration and
performance boosting properties.
Sports highlights generation based on acoustic events detection: A rugby case study
Authors: Anant Baijal, Jaeyoun Cho, Woojung Lee, Byeong-Seob Ko
Date: 2015-09-18 00:00:00
Tasks: General Classification

We approach the challenging problem of generating highlights from sports
broadcasts utilizing audio information only. A language-independent,
multi-stage classification approach is employed for detection of key acoustic
events which then act as a platform for summarization of highlight scenes.
Objective results and human experience indicate that our system is highly
efficient.
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination
Authors: Olga Kolchyna, Tharsis T. P. Souza, Philip Treleaven, Tomaso Aste
Date: 2015-07-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, feature selection, General Classification, Sentiment Analysis, Sentiment Classification, Task 2, Twitter Sentiment Analysis

This paper covers the two approaches for sentiment analysis: i) lexicon based
method; ii) machine learning method. We describe several techniques to
implement these approaches and discuss how they can be adopted for sentiment
classification of Twitter messages. We present a comparative study of different
lexicon combinations and show that enhancing sentiment lexicons with emoticons,
abbreviations and social-media slang expressions increases the accuracy of
lexicon-based classification for Twitter. We discuss the importance of feature
generation and feature selection processes for machine learning sentiment
classification. To quantify the performance of the main sentiment analysis
methods over Twitter we run these algorithms on a benchmark Twitter dataset
from the SemEval-2013 competition, task 2-B. The results show that machine
learning method based on SVM and Naive Bayes classifiers outperforms the
lexicon method. We present a new ensemble method that uses a lexicon based
sentiment score as input feature for the machine learning approach. The
combined method proved to produce more precise classifications. We also show
that employing a cost-sensitive classifier for highly unbalanced datasets
yields an improvement of sentiment classification performance up to 7%.
Color-Stripe Structured Light Robust to Surface Color and Discontinuity
Authors: Kwang Hee Lee, Changsoo Je, Sang Wook Lee
Date: 2015-09-18 00:00:00
Tasks: Object

Multiple color stripes have been employed for structured light-based rapid
range imaging to increase the number of uniquely identifiable stripes. The use
of multiple color stripes poses two problems: (1) object surface color may
disturb the stripe color and (2) the number of adjacent stripes required for
identifying a stripe may not be maintained near surface discontinuities such as
occluding boundaries. In this paper, we present methods to alleviate those
problems. Log-gradient filters are employed to reduce the influence of object
colors, and color stripes in two and three directions are used to increase the
chance of identifying correct stripes near surface discontinuities.
Experimental results demonstrate the effectiveness of our methods.
ORB-SLAM: a Versatile and Accurate Monocular SLAM System
Authors: Raul Mur-Artal, J. M. M. Montiel, Juan D. Tardos
Date: 2015-02-03 00:00:00
Tasks: Simultaneous Localization and Mapping

This paper presents ORB-SLAM, a feature-based monocular SLAM system that
operates in real time, in small and large, indoor and outdoor environments. The
system is robust to severe motion clutter, allows wide baseline loop closing
and relocalization, and includes full automatic initialization. Building on
excellent algorithms of recent years, we designed from scratch a novel system
that uses the same features for all SLAM tasks: tracking, mapping,
relocalization, and loop closing. A survival of the fittest strategy that
selects the points and keyframes of the reconstruction leads to excellent
robustness and generates a compact and trackable map that only grows if the
scene content changes, allowing lifelong operation. We present an exhaustive
evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves
unprecedented performance with respect to other state-of-the-art monocular SLAM
approaches. For the benefit of the community, we make the source code public.
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Authors: Kun Zhao, Azadeh Alavi, Arnold Wiliem, Brian C. Lovell
Date: 2015-09-18 00:00:00
Tasks: Clustering

Reformulating computer vision problems over Riemannian manifolds has
demonstrated superior performance in various computer vision applications. This
is because visual data often forms a special structure lying on a lower
dimensional space embedded in a higher dimensional space. However, since these
manifolds belong to non-Euclidean topological spaces, exploiting their
structures is computationally expensive, especially when one considers the
clustering analysis of massive amounts of data. To this end, we propose an
efficient framework to address the clustering problem on Riemannian manifolds.
This framework implements random projections for manifold points via kernel
space, which can preserve the geometric structure of the original space, but is
computationally efficient. Here, we introduce three methods that follow our
framework. We then validate our framework on several computer vision
applications by comparing against popular clustering methods on Riemannian
manifolds. Experimental results demonstrate that our framework maintains the
performance of the clustering whilst massively reducing computational
complexity by over two orders of magnitude in some cases.
Generative Image Modeling Using Spatial LSTMs
Authors: Lucas Theis, Matthias Bethge
Date: 2015-06-10 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Image Generation, Texture Synthesis

Modeling the distribution of natural images is challenging, partly because of
strong statistical dependencies which can extend over hundreds of pixels.
Recurrent neural networks have been successful in capturing long-range
dependencies in a number of problems but only recently have found their way
into generative image models. We here introduce a recurrent image model based
on multi-dimensional long short-term memory units which are particularly suited
for image modeling due to their spatial structure. Our model scales to images
of arbitrary size and its likelihood is computationally tractable. We find that
it outperforms the state of the art in quantitative comparisons on several
image datasets and produces promising results when used for texture synthesis
and inpainting.
Proceedings Thirteenth International Workshop on the ACL2 Theorem Prover and Its Applications
Authors: Matt Kaufmann, David L. Rager
Date: 2015-09-18 00:00:00

This volume contains the proceedings of the Thirteenth International Workshop
on the ACL2 Theorem Prover and Its Applications, ACL2 2015, a two-day workshop
held in Austin, Texas, USA, on October 1-2, 2015. ACL2 workshops occur at
approximately 18-month intervals and provide a major technical forum for
researchers to present and discuss improvements and extensions to the theorem
prover, comparisons of ACL2 with other systems, and applications of ACL2 in
formal verification.
  ACL2 is a state-of-the-art automated reasoning system that has been
successfully applied in academia, government, and industry for specification
and verification of computing systems and in teaching computer science courses.
In 2005, Boyer, Kaufmann, and Moore were awarded the 2005 ACM Software System
Award for their work on ACL2 and the other theorem provers in the Boyer-Moore
family.
An Experimental Survey on Correlation Filter-based Tracking
Authors: Zhe Chen, Zhibin Hong, DaCheng Tao
Date: 2015-09-18 00:00:00
Tasks: Object Tracking, Survey, Visual Object Tracking

Over these years, Correlation Filter-based Trackers (CFTs) have aroused
increasing interests in the field of visual object tracking, and have achieved
extremely compelling results in different competitions and benchmarks. In this
paper, our goal is to review the developments of CFTs with extensive
experimental results. 11 trackers are surveyed in our work, based on which a
general framework is summarized. Furthermore, we investigate different training
schemes for correlation filters, and also discuss various effective
improvements that have been made recently. Comprehensive experiments have been
conducted to evaluate the effectiveness and efficiency of the surveyed CFTs,
and comparisons have been made with other competing trackers. The experimental
results have shown that state-of-art performance, in terms of robustness, speed
and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF.
We find that further improvements for correlation filter-based tracking can be
made on estimating scales, applying part-based tracking strategy and
cooperating with long-term tracking methods.
A Light Sliding-Window Part-of-Speech Tagger for the Apertium Free/Open-Source Machine Translation Platform
Authors: Gang Chen, Mikel L. Forcada
Date: 2015-09-18 00:00:00
Tasks: Machine Translation, Translation

This paper describes a free/open-source implementation of the light
sliding-window (LSW) part-of-speech tagger for the Apertium free/open-source
machine translation platform. Firstly, the mechanism and training process of
the tagger are reviewed, and a new method for incorporating linguistic rules is
proposed. Secondly, experiments are conducted to compare the performances of
the tagger under different window settings, with or without Apertium-style
"forbid" rules, with or without Constraint Grammar, and also with respect to
the traditional HMM tagger in Apertium.
Accelerated Distance Computation with Encoding Tree for High Dimensional Data
Authors: Shicong Liu, Junru Shao, Hongtao Lu
Date: 2015-09-17 00:00:00
Tasks: Quantization, Vocal Bursts Intensity Prediction

We propose a novel distance to calculate distance between high dimensional
vector pairs, utilizing vector quantization generated encodings. Vector
quantization based methods are successful in handling large scale high
dimensional data. These methods compress vectors into short encodings, and
allow efficient distance computation between an uncompressed vector and
compressed dataset without decompressing explicitly. However for large
datasets, these distance computing methods perform excessive computations. We
avoid excessive computations by storing the encodings on an Encoding
Tree(E-Tree), interestingly the memory consumption is also lowered. We also
propose Encoding Forest(E-Forest) to further lower the computation cost. E-Tree
and E-Forest is compatible with various existing quantization-based methods. We
show by experiments our methods speed-up distance computing for high
dimensional data drastically, and various existing algorithms can benefit from
our methods.
A Ternary Non-Commutative Latent Factor Model for Scalable Three-Way Real Tensor Completion
Authors: Guy Baruch
Date: 2014-10-26 00:00:00
Tasks: Collaborative Filtering

Motivated by large-scale Collaborative-Filtering applications, we present a
Non-Commuting Latent Factor (NCLF) tensor-completion approach for modeling
three-way arrays, which is diagonal like the standard PARAFAC, but wherein
different terms distinguish different kinds of three-way relations of
co-clusters, as determined by permutations of latent factors. The first key
component of the algebraic representation is the usage of two non-commutative
real trilinear operations as the building blocks of the approximation. These
operations are the standard three dimensional triple-product and a trilinear
product on a two-dimensional real vector space, which is a representation of
the real Clifford Algebra Cl(1,1) (a certain Majorana spinor). Both operations
are purely ternary in that they cannot be decomposed into two group-operations
on the relevant spaces. The second key component of the method is combining
these operations using permutation-symmetry preserving linear combinations. We
apply the model to the MovieLens and Fannie Mae datasets, and find that it
outperforms the PARAFAC model. We propose some future directions, such as
unsupervised-learning.
Algorithmic statistics, prediction and machine learning
Authors: Alexey Milovanov
Date: 2015-09-17 00:00:00
Tasks: BIG-bench Machine Learning, Learning Theory, Prediction

Algorithmic statistics considers the following problem: given a binary string
$x$ (e.g., some experimental data), find a "good" explanation of this data. It
uses algorithmic information theory to define formally what is a good
explanation. In this paper we extend this framework in two directions.
  First, the explanations are not only interesting in themselves but also used
for prediction: we want to know what kind of data we may reasonably expect in
similar situations (repeating the same experiment). We show that some kind of
hierarchy can be constructed both in terms of algorithmic statistics and using
the notion of a priori probability, and these two approaches turn out to be
equivalent.
  Second, a more realistic approach that goes back to machine learning theory,
assumes that we have not a single data string $x$ but some set of "positive
examples" $x_1,\ldots,x_l$ that all belong to some unknown set $A$, a property
that we want to learn. We want this set $A$ to contain all positive examples
and to be as small and simple as possible. We show how algorithmic statistic
can be extended to cover this situation.