pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models
Authors: Christian Gruhl, Bernhard Sick
Date: 2016-05-18 00:00:00
Tasks: Intrusion Detection, Novelty Detection

In this article, we propose CANDIES (Combined Approach for Novelty Detection
in Intelligent Embedded Systems), a new approach to novelty detection in
technical systems. We assume that in a technical system several processes
interact. If we observe these processes with sensors, we are able to model the
observations (samples) with a probabilistic model, where, in an ideal case, the
components of the parametric mixture density model we use, correspond to the
processes in the real world. Eventually, at run-time, novel processes emerge in
the technical systems such as in the case of an unpredictable failure. As a
consequence, new kinds of samples are observed that require an adaptation of
the model. CANDIES relies on mixtures of Gaussians which can be used for
classification purposes, too. New processes may emerge in regions of the
models' input spaces where few samples were observed before (low-density
regions) or in regions where already many samples were available (high-density
regions). The latter case is more difficult, but most existing solutions focus
on the former. Novelty detection in low- and high-density regions requires
different detection strategies. With CANDIES, we introduce a new technique to
detect novel processes in high-density regions by means of a fast online
goodness-of-fit test. For detection in low-density regions we combine this
approach with a 2SND (Two-Stage-Novelty-Detector) which we presented in
preliminary work. The properties of CANDIES are evaluated using artificial data
and benchmark data from the field of intrusion detection in computer networks,
where the task is to detect new kinds of attacks.
Variational Gaussian Copula Inference
Authors: Shaobo Han, Xuejun Liao, David B. Dunson, Lawrence Carin
Date: 2015-06-19 00:00:00

We utilize copulas to constitute a unified framework for constructing and
optimizing variational proposals in hierarchical Bayesian models. For models
with continuous and non-Gaussian hidden variables, we propose a semiparametric
and automated variational Gaussian copula approach, in which the parametric
Gaussian copula family is able to preserve multivariate posterior dependence,
and the nonparametric transformations based on Bernstein polynomials provide
ample flexibility in characterizing the univariate marginal posteriors.
Improving Weakly-Supervised Object Localization By Micro-Annotation
Authors: Alexander Kolesnikov, Christoph H. Lampert
Date: 2016-05-18 00:00:00
Tasks: Object, Object Localization, Semantic Segmentation, Weakly-Supervised Object Localization

Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on
tracks. We propose a method to overcome these failures by adding a very small
amount of model-specific additional annotation. The main idea is to cluster a
deep network's mid-level representations and assign object or distractor labels
to each cluster. Experiments show substantially improved localization results
on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL
VOC2012 dataset for semantic segmentation.
A new kernel-based approach for overparameterized Hammerstein system identification
Authors: Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson
Date: 2015-04-30 00:00:00

In this paper we propose a new identification scheme for Hammerstein systems,
which are dynamic systems consisting of a static nonlinearity and a linear
time-invariant dynamic system in cascade. We assume that the nonlinear function
can be described as a linear combination of $p$ basis functions. We reconstruct
the $p$ coefficients of the nonlinearity together with the first $n$ samples of
the impulse response of the linear system by estimating an $np$-dimensional
overparameterized vector, which contains all the combinations of the unknown
variables. To avoid high variance in these estimates, we adopt a regularized
kernel-based approach and, in particular, we introduce a new kernel tailored
for Hammerstein system identification. We show that the resulting scheme
provides an estimate of the overparameterized vector that can be uniquely
decomposed as the combination of an impulse response and $p$ coefficients of
the static nonlinearity. We also show, through several numerical experiments,
that the proposed method compares very favorably with two standard methods for
Hammerstein system identification.
Image segmentation with superpixel-based covariance descriptors in low-rank representation
Authors: Xianbin Gu, Jeremiah D. Deng, Martin K. Purvis
Date: 2016-05-18 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

This paper investigates the problem of image segmentation using superpixels.
We propose two approaches to enhance the discriminative ability of the
superpixel's covariance descriptors. In the first one, we employ the
Log-Euclidean distance as the metric on the covariance manifolds, and then use
the RBF kernel to measure the similarities between covariance descriptors. The
second method is focused on extracting the subspace structure of the set of
covariance descriptors by extending a low rank representation algorithm on to
the covariance manifolds. Experiments are carried out with the Berkly
Segmentation Dataset, and compared with the state-of-the-art segmentation
algorithms, both methods are competitive.
Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery
Authors: Alina Marcu, Marius Leordeanu
Date: 2016-05-18 00:00:00
Tasks: Object Recognition, Road Segmentation, Visual Reasoning

Visual context is important in object recognition and it is still an open
problem in computer vision. Along with the advent of deep convolutional neural
networks (CNN), using contextual information with such systems starts to
receive attention in the literature. At the same time, aerial imagery is
gaining momentum. While advances in deep learning make good progress in aerial
image analysis, this problem still poses many great challenges. Aerial images
are often taken under poor lighting conditions and contain low resolution
objects, many times occluded by trees or taller buildings. In this domain, in
particular, visual context could be of great help, but there are still very few
papers that consider context in aerial image understanding. Here we introduce
context as a complementary way of recognizing objects. We propose a dual-stream
deep neural network model that processes information along two independent
pathways, one for local and another for global visual reasoning. The two are
later combined in the final layers of processing. Our model learns to combine
local object appearance as well as information from the larger scene at the
same time and in a complementary way, such that together they form a powerful
classifier. We test our dual-stream network on the task of segmentation of
buildings and roads in aerial images and obtain state-of-the-art results on the
Massachusetts Buildings Dataset. We also introduce two new datasets, for
buildings and road segmentation, respectively, and study the relative
importance of local appearance vs. the larger scene, as well as their
performance in combination. While our local-global model could also be useful
in general recognition tasks, we clearly demonstrate the effectiveness of
visual context in conjunction with deep nets for aerial image understanding.
The Bees Algorithm for the Vehicle Routing Problem
Authors: Aish Fenton
Date: 2016-05-18 00:00:00

In this thesis we present a new algorithm for the Vehicle Routing Problem
called the Enhanced Bees Algorithm. It is adapted from a fairly recent
algorithm, the Bees Algorithm, which was developed for continuous optimisation
problems. We show that the results obtained by the Enhanced Bees Algorithm are
competitive with the best meta-heuristics available for the Vehicle Routing
Problem (within 0.5% of the optimal solution for common benchmark problems). We
show that the algorithm has good runtime performance, producing results within
2% of the optimal solution within 60 seconds, making it suitable for use within
real world dispatch scenarios.
Beyond Caption To Narrative: Video Captioning With Multiple Sentences
Authors: Andrew Shin, Katsunori Ohnishi, Tatsuya Harada
Date: 2016-05-18 00:00:00
Tasks: Action Localization, Image Captioning, Video Captioning

Recent advances in image captioning task have led to increasing interests in
video captioning task. However, most works on video captioning are focused on
generating single input of aggregated features, which hardly deviates from
image captioning process and does not fully take advantage of dynamic contents
present in videos. We attempt to generate video captions that convey richer
contents by temporally segmenting the video with action localization,
generating multiple captions from multiple frames, and connecting them with
natural language processing techniques, in order to generate a story-like
caption. We show that our proposed method can generate captions that are richer
in contents and can compete with state-of-the-art method without explicitly
using video-level features as input.
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data
Authors: Teng Long, Ryan Lowe, Jackie Chi Kit Cheung, Doina Precup
Date: 2016-05-18 00:00:00
Proceeding: ACL 2016 8
Tasks: Entity Embeddings

Recent work in learning vector-space embeddings for multi-relational data has
focused on combining relational information derived from knowledge bases with
distributional information derived from large text corpora. We propose a simple
approach that leverages the descriptions of entities or phrases available in
lexical resources, in conjunction with distributional semantics, in order to
derive a better initialization for training relational models. Applying this
initialization to the TransE model results in significant new state-of-the-art
performances on the WordNet dataset, decreasing the mean rank from the previous
best of 212 to 51. It also results in faster convergence of the entity
representations. We find that there is a trade-off between improving the mean
rank and the hits@10 with this approach. This illustrates that much remains to
be understood regarding performance improvements in relational models.
Relative distance features for gait recognition with Kinect
Authors: Ke Yang, Yong Dou, Shaohe Lv, Fei Zhang, Qi Lv
Date: 2016-05-18 00:00:00
Tasks: Gait Recognition

Gait and static body measurement are important biometric technologies for
passive human recognition. Many previous works argue that recognition
performance based completely on the gait feature is limited. The reason for
this limited performance remains unclear. This study focuses on human
recognition with gait feature obtained by Kinect and shows that gait feature
can effectively distinguish from different human beings through a novel
representation -- relative distance-based gait features. Experimental results
show that the recognition accuracy with relative distance features reaches up
to 85%, which is comparable with that of anthropometric features. The
combination of relative distance features and anthropometric features can
provide an accuracy of more than 95%. Results indicate that the relative
distance feature is quite effective and worthy of further study in more general
scenarios (e.g., without Kinect).
Learning a low-rank shared dictionary for object classification
Authors: Tiep H. Vu, Vishal Monga
Date: 2016-01-31 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, Object

Despite the fact that different objects possess distinct class-specific
features, they also usually share common patterns. Inspired by this
observation, we propose a novel method to explicitly and simultaneously learn a
set of common patterns as well as class-specific features for classification.
Our dictionary learning framework is hence characterized by both a shared
dictionary and particular (class-specific) dictionaries. For the shared
dictionary, we enforce a low-rank constraint, i.e. claim that its spanning
subspace should have low dimension and the coefficients corresponding to this
dictionary should be similar. For the particular dictionaries, we impose on
them the well-known constraints stated in the Fisher discrimination dictionary
learning (FDDL). Further, we propose a new fast and accurate algorithm to solve
the sparse coding problems in the learning step, accelerating its convergence.
The said algorithm could also be applied to FDDL and its extensions.
Experimental results on widely used image databases establish the advantages of
our method over state-of-the-art dictionary learning methods.
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
Authors: Minh Dao, Xiang Xiang, Bulent Ayhan, Chiman Kwan, Trac. D. Tran
Date: 2016-05-01 00:00:00

In this paper, we propose a burnscar detection model for hyperspectral
imaging (HSI) data. The proposed model contains two-processing steps in which
the first step separate and then suppress the cloud information presenting in
the data set using an RPCA algorithm and the second step detect the burnscar
area in the low-rank component output of the first step. Experiments are
conducted on the public MODIS dataset available at NASA official website.
Learning Deep Representations of Fine-grained Visual Descriptions
Authors: Scott Reed, Zeynep Akata, Bernt Schiele, Honglak Lee
Date: 2016-05-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, Image Retrieval, Retrieval, zero-shot-classification, Zero-Shot Learning

State-of-the-art methods for zero-shot visual recognition formulate learning
as a joint embedding problem of images and side information. In these
formulations the current best complement to visual features are attributes:
manually encoded vectors describing shared characteristics among categories.
Despite good performance, attributes have limitations: (1) finer-grained
recognition requires commensurately more attributes, and (2) attributes do not
provide a natural language interface. We propose to overcome these limitations
by training neural language models from scratch; i.e. without pre-training and
only consuming words and characters. Our proposed models train end-to-end to
align with the fine-grained and category-specific content of images. Natural
language provides a flexible and compact way of encoding only the salient
visual aspects for distinguishing categories. By training on raw text, our
model can do inference on raw text as well, providing humans a familiar mode
both for annotation and retrieval. Our model achieves strong performance on
zero-shot text-based image retrieval and significantly outperforms the
attribute-based state-of-the-art for zero-shot classification on the Caltech
UCSD Birds 200-2011 dataset.
Yelp Dataset Challenge: Review Rating Prediction
Authors: Nabiha Asghar
Date: 2016-05-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Multi-class Classification, Prediction

Review websites, such as TripAdvisor and Yelp, allow users to post online
reviews for various businesses, products and services, and have been recently
shown to have a significant influence on consumer shopping behaviour. An online
review typically consists of free-form text and a star rating out of 5. The
problem of predicting a user's star rating for a product, given the user's text
review for that product, is called Review Rating Prediction and has lately
become a popular, albeit hard, problem in machine learning. In this paper, we
treat Review Rating Prediction as a multi-class classification problem, and
build sixteen different prediction models by combining four feature extraction
methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic
Indexing, with four machine learning algorithms, (i) logistic regression, (ii)
Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector
Classification. We analyse the performance of each of these sixteen models to
come up with the best model for predicting the ratings from reviews. We use the
dataset provided by Yelp for training and testing the models.
Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation
Authors: Mehran Javanmardi, Mehdi Sajjadi, Ting Liu, Tolga Tasdizen
Date: 2016-05-04 00:00:00
Tasks: Semantic Segmentation

We introduce a novel unsupervised loss function for learning semantic
segmentation with deep convolutional neural nets (ConvNet) when densely labeled
training images are not available. More specifically, the proposed loss
function penalizes the L1-norm of the gradient of the label probability vector
image , i.e. total variation, produced by the ConvNet. This can be seen as a
regularization term that promotes piecewise smoothness of the label probability
vector image produced by the ConvNet during learning. The unsupervised loss
function is combined with a supervised loss in a semi-supervised setting to
learn ConvNets that can achieve high semantic segmentation accuracy even when
only a tiny percentage of the pixels in the training images are labeled. We
demonstrate significant improvements over the purely supervised setting in the
Weizmann horse, Stanford background and Sift Flow datasets. Furthermore, we
show that using the proposed piecewise smoothness constraint in the learning
phase significantly outperforms post-processing results from a purely
supervised approach with Markov Random Fields (MRF). Finally, we note that the
framework we introduce is general and can be used to learn to label other types
of structures such as curvilinear structures by modifying the unsupervised loss
function accordingly.
Orthogonal symmetric non-negative matrix factorization under the stochastic block model
Authors: Subhadeep Paul, Yuguo Chen
Date: 2016-05-17 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

We present a method based on the orthogonal symmetric non-negative matrix
tri-factorization of the normalized Laplacian matrix for community detection in
complex networks. While the exact factorization of a given order may not exist
and is NP hard to compute, we obtain an approximate factorization by solving an
optimization problem. We establish the connection of the factors obtained
through the factorization to a non-negative basis of an invariant subspace of
the estimated matrix, drawing parallel with the spectral clustering. Using such
factorization for clustering in networks is motivated by analyzing a
block-diagonal Laplacian matrix with the blocks representing the connected
components of a graph. The method is shown to be consistent for community
detection in graphs generated from the stochastic block model and the degree
corrected stochastic block model. Simulation results and real data analysis
show the effectiveness of these methods under a wide variety of situations,
including sparse and highly heterogeneous graphs where the usual spectral
clustering is known to fail. Our method also performs better than the state of
the art in popular benchmark network datasets, e.g., the political web blogs
and the karate club data.
Fast and Accurate Performance Analysis of LTE Radio Access Networks
Authors: Anand Padmanabha Iyer, Ion Stoica, Mosharaf Chowdhury, Li Erran Li
Date: 2016-05-16 00:00:00
Tasks: Feature Engineering, Multi-Task Learning

An increasing amount of analytics is performed on data that is procured in a
real-time fashion to make real-time decisions. Such tasks include simple
reporting on streams to sophisticated model building. However, the practicality
of such analyses are impeded in several domains because they are faced with a
fundamental trade-off between data collection latency and analysis accuracy.
  In this paper, we study this trade-off in the context of a specific domain,
Cellular Radio Access Networks (RAN). Our choice of this domain is influenced
by its commonalities with several other domains that produce real-time data,
our access to a large live dataset, and their real-time nature and
dimensionality which makes it a natural fit for a popular analysis technique,
machine learning (ML). We find that the latency accuracy trade-off can be
resolved using two broad, general techniques: intelligent data grouping and
task formulations that leverage domain characteristics. Based on this, we
present CellScope, a system that addresses this challenge by applying a domain
specific formulation and application of Multi-task Learning (MTL) to RAN
performance analysis. It achieves this goal using three techniques: feature
engineering to transform raw data into effective features, a PCA inspired
similarity metric to group data from geographically nearby base stations
sharing performance commonalities, and a hybrid online-offline model for
efficient model updates. Our evaluation of CellScope shows that its accuracy
improvements over direct application of ML range from 2.5x to 4.4x while
reducing the model update overhead by up to 4.8x. We have also used CellScope
to analyze a live LTE consisting of over 2 million subscribers for a period of
over 10 months, where it uncovered several problems and insights, some of them
previously unknown.
Combat Models for RTS Games
Authors: Alberto Uriarte, Santiago Ontañón
Date: 2016-05-17 00:00:00
Tasks: Starcraft

Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require
access to a forward model (or "simulator") of the game at hand. However, in
some games such forward model is not readily available. This paper presents
three forward models for two-player attrition games, which we call "combat
models", and show how they can be used to simulate combat in RTS games. We also
show how these combat models can be learned from replay data. We use StarCraft
as our application domain. We report experiments comparing our combat models
predicting a combat output and their impact when used for tactical decisions
during a real game.
Fuzzy Sets Across the Natural Language Generation Pipeline
Authors: A. Ramos-Soto, A. Bugarín, S. Barro
Date: 2016-05-17 00:00:00
Tasks: Text Generation

We explore the implications of using fuzzy techniques (mainly those commonly
used in the linguistic description/summarization of data discipline) from a
natural language generation perspective. For this, we provide an extensive
discussion of some general convergence points and an exploration of the
relationship between the different tasks involved in the standard NLG system
pipeline architecture and the most common fuzzy approaches used in linguistic
summarization/description of data, such as fuzzy quantified statements,
evaluation criteria or aggregation operators. Each individual discussion is
illustrated with a related use case. Recent work made in the context of
cross-fertilization of both research fields is also referenced. This paper
encompasses general ideas that emerged as part of the PhD thesis "Application
of fuzzy sets in data-to-text systems". It does not present a specific
application or a formal approach, but rather discusses current high-level
issues and potential usages of fuzzy sets (focused on linguistic summarization
of data) in natural language generation.
Dataflow matrix machines as programmable, dynamically expandable, self-referential generalized recurrent neural networks
Authors: Michael Bukatin, Steve Matthews, Andrey Radul
Date: 2016-05-17 00:00:00

Dataflow matrix machines are a powerful generalization of recurrent neural
networks. They work with multiple types of linear streams and multiple types of
neurons, including higher-order neurons which dynamically update the matrix
describing weights and topology of the network in question while the network is
running. It seems that the power of dataflow matrix machines is sufficient for
them to be a convenient general purpose programming platform. This paper
explores a number of useful programming idioms and constructions arising in
this context.