pwc-archive/papers-with-abstracts huggingface dataset:
Excisive Hierarchical Clustering Methods for Network Data
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
Date: 2016-07-21 00:00:00
Tasks: Clustering

We introduce two practical properties of hierarchical clustering methods for
(possibly asymmetric) network data: excisiveness and linear scale preservation.
The latter enforces imperviousness to change in units of measure whereas the
former ensures local consistency of the clustering outcome. Algorithmically,
excisiveness implies that we can reduce computational complexity by only
clustering a data subset of interest while theoretically guaranteeing that the
same hierarchical outcome would be observed when clustering the whole dataset.
Moreover, we introduce the concept of representability, i.e. a generative model
for describing clustering methods through the specification of their action on
a collection of networks. We further show that, within a rich set of admissible
methods, requiring representability is equivalent to requiring both
excisiveness and linear scale preservation. Leveraging this equivalence, we
show that all excisive and linear scale preserving methods can be factored into
two steps: a transformation of the weights in the input network followed by the
application of a canonical clustering method. Furthermore, their factorization
can be used to show stability of excisive and linear scale preserving methods
in the sense that a bounded perturbation in the input network entails a bounded
perturbation in the clustering output.
Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
Date: 2016-07-21 00:00:00
Tasks: Clustering

This paper characterizes hierarchical clustering methods that abide by two
previously introduced axioms -- thus, denominated admissible methods -- and
proposes tractable algorithms for their implementation. We leverage the fact
that, for asymmetric networks, every admissible method must be contained
between reciprocal and nonreciprocal clustering, and describe three families of
intermediate methods. Grafting methods exchange branches between dendrograms
generated by different admissible methods. The convex combination family
combines admissible methods through a convex operation in the space of
dendrograms, and thirdly, the semi-reciprocal family clusters nodes that are
related by strong cyclic influences in the network. Algorithms for the
computation of hierarchical clusters generated by reciprocal and nonreciprocal
clustering as well as the grafting, convex combination, and semi-reciprocal
families are derived using matrix operations in a dioid algebra. Finally, the
introduced clustering methods and algorithms are exemplified through their
application to a network describing the interrelation between sectors of the
United States (U.S.) economy.
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Authors: Margret Keuper, Siyu Tang, Yu Zhongjie, Bjoern Andres, Thomas Brox, Bernt Schiele
Date: 2016-07-21 00:00:00
Tasks: Motion Segmentation, object-detection, Object Detection, Segmentation

Recently, Minimum Cost Multicut Formulations have been proposed and proven to
be successful in both motion trajectory segmentation and multi-target tracking
scenarios. Both tasks benefit from decomposing a graphical model into an
optimal number of connected components based on attractive and repulsive
pairwise terms. The two tasks are formulated on different levels of granularity
and, accordingly, leverage mostly local information for motion segmentation and
mostly high-level information for multi-target tracking. In this paper we argue
that point trajectories and their local relationships can contribute to the
high-level task of multi-target tracking and also argue that high-level cues
from object detection and tracking are helpful to solve motion segmentation. We
propose a joint graphical model for point trajectories and object detections
whose Multicuts are solutions to motion segmentation {\it and} multi-target
tracking problems at once. Results on the FBMS59 motion segmentation benchmark
as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark
demonstrate the promise of this joint approach.
Opinion Mining in Online Reviews About Distance Education Programs
Authors: Janik Jaskolski, Fabian Siegberg, Thomas Tibroni, Philipp Cimiano, Roman Klinger
Date: 2016-07-21 00:00:00
Tasks: General Classification, Opinion Mining, text-classification, Text Classification

The popularity of distance education programs is increasing at a fast pace.
En par with this development, online communication in fora, social media and
reviewing platforms between students is increasing as well. Exploiting this
information to support fellow students or institutions requires to extract the
relevant opinions in order to automatically generate reports providing an
overview of pros and cons of different distance education programs. We report
on an experiment involving distance education experts with the goal to develop
a dataset of reviews annotated with relevant categories and aspects in each
category discussed in the specific review together with an indication of the
sentiment.
  Based on this experiment, we present an approach to extract general
categories and specific aspects under discussion in a review together with
their sentiment. We frame this task as a multi-label hierarchical text
classification problem and empirically investigate the performance of different
classification architectures to couple the prediction of a category with the
prediction of particular aspects in this category. We evaluate different
architectures and show that a hierarchical approach leads to superior results
in comparison to a flat model which makes decisions independently.
Hierarchical Clustering of Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
Date: 2016-07-21 00:00:00
Tasks: Clustering

This paper considers networks where relationships between nodes are
represented by directed dissimilarities. The goal is to study methods that,
based on the dissimilarity structure, output hierarchical clusters, i.e., a
family of nested partitions indexed by a connectivity parameter. Our
construction of hierarchical clustering methods is built around the concept of
admissible methods, which are those that abide by the axioms of value - nodes
in a network with two nodes are clustered together at the maximum of the two
dissimilarities between them - and transformation - when dissimilarities are
reduced, the network may become more clustered but not less. Two particular
methods, termed reciprocal and nonreciprocal clustering, are shown to provide
upper and lower bounds in the space of admissible methods. Furthermore,
alternative clustering methodologies and axioms are considered. In particular,
modifying the axiom of value such that clustering in two-node networks occurs
at the minimum of the two dissimilarities entails the existence of a unique
admissible clustering method.
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection
Authors: Arnaud Dapogny, Kévin Bailly, Séverine Dubuisson
Date: 2016-07-21 00:00:00
Tasks: Action Unit Detection, Descriptive, Facial Expression Recognition, Facial Expression Recognition (FER), Occlusion Handling

Fully-Automatic Facial Expression Recognition (FER) from still images is a
challenging task as it involves handling large interpersonal morphological
differences, and as partial occlusions can occasionally happen. Furthermore,
labelling expressions is a time-consuming process that is prone to
subjectivity, thus the variability may not be fully covered by the training
data. In this work, we propose to train Random Forests upon spatially defined
local subspaces of the face. The output local predictions form a categorical
expression-driven high-level representation that we call Local Expression
Predictions (LEPs). LEPs can be combined to describe categorical facial
expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by
confidence scores provided by an autoencoder network. Such network is trained
to locally capture the manifold of the non-occluded training data in a
hierarchical way. Extensive experiments show that the proposed LEP
representation yields high descriptive power for categorical expressions and AU
occurrence prediction, and leads to interesting perspectives towards the design
of occlusion-robust and confidence-aware FER systems.
Left/Right Hand Segmentation in Egocentric Videos
Authors: Alejandro Betancourt, Pietro Morerio, Emilia Barakova, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
Date: 2016-07-21 00:00:00
Tasks: Hand Segmentation, Segmentation, Superpixels

Wearable cameras allow people to record their daily activities from a
user-centered (First Person Vision) perspective. Due to their favorable
location, wearable cameras frequently capture the hands of the user, and may
thus represent a promising user-machine interaction tool for different
applications. Existent First Person Vision methods handle hand segmentation as
a background-foreground problem, ignoring two important facts: i) hands are not
a single "skin-like" moving element, but a pair of interacting cooperative
entities, ii) close hand interactions may lead to hand-to-hand occlusions and,
as a consequence, create a single hand-like segment. These facts complicate a
proper understanding of hand movements and interactions. Our approach extends
traditional background-foreground strategies, by including a
hand-identification step (left-right) based on a Maxwell distribution of angle
and position. Hand-to-hand occlusions are addressed by exploiting temporal
superpixels. The experimental results show that, in addition to a reliable
left/right hand-segmentation, our approach considerably improves the
traditional background-foreground hand-segmentation.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Authors: Shun Kataoka, Takuto Kobayashi, Muneki Yasuda, Kazuyuki Tanaka
Date: 2016-07-21 00:00:00
Tasks: Attribute, Clustering, Community Detection, Stochastic Block Model

We propose a new algorithm to detect the community structure in a network
that utilizes both the network structure and vertex attribute data. Suppose we
have the network structure together with the vertex attribute data, that is,
the information assigned to each vertex associated with the community to which
it belongs. The problem addressed this paper is the detection of the community
structure from the information of both the network structure and the vertex
attribute data. Our approach is based on the Bayesian approach that models the
posterior probability distribution of the community labels. The detection of
the community structure in our method is achieved by using belief propagation
and an EM algorithm. We numerically verified the performance of our method
using computer-generated networks and real-world networks.
Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests
Authors: Arnaud Dapogny, Kévin Bailly, Séverine Dubuisson
Date: 2016-07-21 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Head Pose Estimation, Pose Estimation

Automatic facial expression classification (FER) from videos is a critical
problem for the development of intelligent human-computer interaction systems.
Still, it is a challenging problem that involves capturing high-dimensional
spatio-temporal patterns describing the variation of one's appearance over
time. Such representation undergoes great variability of the facial morphology
and environmental factors as well as head pose variations. In this paper, we
use Conditional Random Forests to capture low-level expression transition
patterns. More specifically, heterogeneous derivative features (e.g. feature
point movements or texture variations) are evaluated upon pairs of images. When
testing on a video frame, pairs are created between this current frame and
previous ones and predictions for each previous frame are used to draw trees
from Pairwise Conditional Random Forests (PCRF) whose pairwise outputs are
averaged over time to produce robust estimates. Moreover, PCRF collections can
also be conditioned on head pose estimation for multi-view dynamic FER. As
such, our approach appears as a natural extension of Random Forests for
learning spatio-temporal patterns, potentially from multiple viewpoints.
Experiments on popular datasets show that our method leads to significant
improvements over standard Random Forests as well as state-of-the-art
approaches on several scenarios, including a novel multi-view video corpus
generated from a publicly available database.
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
Authors: Yu Li, ShaoDi You, Michael S. Brown, Robby T. Tan
Date: 2016-07-21 00:00:00
Tasks: Benchmarking, Survey

This paper provides a comprehensive survey of methods dealing with visibility
enhancement of images taken in hazy or foggy scenes. The survey begins with
discussing the optical models of atmospheric scattering media and image
formation. This is followed by a survey of existing methods, which are grouped
to multiple image methods, polarizing filters based methods, methods with known
depth, and single-image methods. We also provide a benchmark of a number of
well known single-image methods, based on a recent dataset provided by Fattal
and our newly generated scattering media dataset that contains ground truth
images for quantitative evaluation. To our knowledge, this is the first
benchmark using numerical metrics to evaluate dehazing techniques. This
benchmark allows us to objectively compare the results of existing methods and
to better identify the strengths and limitations of each method.
Detection of surface defects on ceramic tiles based on morphological techniques
Authors: Grasha Jacob, R. Shenbagavalli, S. Karthika
Date: 2016-07-21 00:00:00
Tasks: Boundary Detection

Ceramic tiles have become very popular and are used in the flooring of
offices and shopping malls. As testing the quality of tiles manually in a
highly polluted environment in the manufacturing industry is a labor-intensive
and time consuming process, analysis is carried out on the tile images. This
paper discusses an automated system to detect the defects on the surface of
ceramic tiles based on dilation, erosion, SMEE and boundary detection
techniques.
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss
Authors: Barbara Plank, Anders Søgaard, Yoav Goldberg
Date: 2016-04-19 00:00:00
Proceeding: ACL 2016 8
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Bidirectional long short-term memory (bi-LSTM) networks have recently proven
successful for various NLP sequence modeling tasks, but little is known about
their reliance to input representations, target languages, data set size, and
label noise. We address these issues and evaluate bi-LSTMs with word,
character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to
traditional POS taggers across languages and data sizes. We also present a
novel bi-LSTM model, which combines the POS tagging loss function with an
auxiliary loss function that accounts for rare words. The model obtains
state-of-the-art performance across 22 languages, and works especially well for
morphologically complex languages. Our analysis suggests that bi-LSTMs are less
sensitive to training data size and label corruptions (at small noise levels)
than previously assumed.
A Distributed Quaternion Kalman Filter With Applications to Fly-by-Wire Systems
Authors: Sayed Pouria Talebi
Date: 2016-05-15 00:00:00
Tasks: Management

The introduction of automated flight control and management systems have made
possible aircraft designs that sacrifice arodynamic stability in order to
incorporate stealth technology intro their shape, operate more efficiently, and
are highly maneuverable. Therefore, modern flight management systems are
reliant on multiple redundant sensors to monitor and control the rotations of
the aircraft. To this end, a novel distributed quaternion Kalman filtering
algorithm is developed for tracking the rotation and orientation of an aircraft
in the three-dimensional space. The algorithm is developed to distribute
computation among the sensors in a manner that forces them to consent to a
unique solution while being robust to sensor and link failure, a desirable
characteristic for flight management systems. In addition, the underlying
quaternion-valued state space model allows to avoid problems associated with
gimbal lock. The performance of the developed algorithm is verified through
simulations.
A Comprehensive Survey on Cross-modal Retrieval
Authors: Kaiye Wang, Qiyue Yin, Wei Wang, Shu Wu, Liang Wang
Date: 2016-07-21 00:00:00
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval, Survey

In recent years, cross-modal retrieval has drawn much attention due to the
rapid growth of multimodal data. It takes one type of data as the query to
retrieve relevant data of another type. For example, a user can use a text to
retrieve relevant pictures or videos. Since the query and its retrieved results
can be of different modalities, how to measure the content similarity between
different modalities of data remains a challenge. Various methods have been
proposed to deal with such a problem. In this paper, we first review a number
of representative methods for cross-modal retrieval and classify them into two
main groups: 1) real-valued representation learning, and 2) binary
representation learning. Real-valued representation learning methods aim to
learn real-valued common representations for different modalities of data. To
speed up the cross-modal retrieval, a number of binary representation learning
methods are proposed to map different modalities of data into a common Hamming
space. Then, we introduce several multimodal datasets in the community, and
show the experimental results on two commonly used multimodal datasets. The
comparison reveals the characteristic of different kinds of cross-modal
retrieval methods, which is expected to benefit both practical applications and
future research. Finally, we discuss open problems and future research
directions.
Exploring phrase-compositionality in skip-gram models
Authors: Xiaochang Peng, Daniel Gildea
Date: 2016-07-21 00:00:00
Tasks: Dependency Parsing

In this paper, we introduce a variation of the skip-gram model which jointly
learns distributed word vector representations and their way of composing to
form phrase embeddings. In particular, we propose a learning procedure that
incorporates a phrase-compositionality function which can capture how we want
to compose phrases vectors from their component word vectors. Our experiments
show improvement in word and phrase similarity tasks as well as syntactic tasks
like dependency parsing using the proposed joint models.
I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification
Authors: Ran Dubin, Amit Dvir, Ofir Pele, Ofer Hadar
Date: 2016-02-01 00:00:00
Tasks: General Classification

Desktops and laptops can be maliciously exploited to violate privacy. There
are two main types of attack scenarios: active and passive. In this paper, we
consider the passive scenario where the adversary does not interact actively
with the device, but he is able to eavesdrop on the network traffic of the
device from the network side. Most of the Internet traffic is encrypted and
thus passive attacks are challenging. Previous research has shown that
information can be extracted from encrypted multimedia streams. This includes
video title classification of non HTTP adaptive streams (non-HAS). This paper
presents an algorithm for encrypted HTTP adaptive video streaming title
classification. We show that an external attacker can identify the video title
from video HTTP adaptive streams (HAS) sites such as YouTube. To the best of
our knowledge, this is the first work that shows this. We provide a large data
set of 10000 YouTube video streams of 100 popular video titles (each title
downloaded 100 times) as examples for this task. The dataset was collected
under real-world network conditions. We present several machine algorithms for
the task and run a through set of experiments, which shows that our
classification accuracy is more than 95%. We also show that our algorithms are
able to classify video titles that are not in the training set as unknown and
some of the algorithms are also able to eliminate false prediction of video
titles and instead report unknown. Finally, we evaluate our algorithms
robustness to delays and packet losses at test time and show that a solution
that uses SVM is the most robust against these changes given enough training
data. We provide the dataset and the crawler for future research.
Greedy bi-criteria approximations for $k$-medians and $k$-means
Authors: Daniel Hsu, Matus Telgarsky
Date: 2016-07-21 00:00:00
Tasks: Clustering

This paper investigates the following natural greedy procedure for clustering
in the bi-criterion setting: iteratively grow a set of centers, in each round
adding the center from a candidate set that maximally decreases clustering
cost. In the case of $k$-medians and $k$-means, the key results are as follows.
  $\bullet$ When the method considers all data points as candidate centers,
then selecting $\mathcal{O}(k\log(1/\varepsilon))$ centers achieves cost at
most $2+\varepsilon$ times the optimal cost with $k$ centers.
  $\bullet$ Alternatively, the same guarantees hold if each round samples
$\mathcal{O}(k/\varepsilon^5)$ candidate centers proportionally to their
cluster cost (as with $\texttt{kmeans++}$, but holding centers fixed).
  $\bullet$ In the case of $k$-means, considering an augmented set of
$n^{\lceil1/\varepsilon\rceil}$ candidate centers gives $1+\varepsilon$
approximation with $\mathcal{O}(k\log(1/\varepsilon))$ centers, the entire
algorithm taking
$\mathcal{O}(dk\log(1/\varepsilon)n^{1+\lceil1/\varepsilon\rceil})$ time, where
$n$ is the number of data points in $\mathbb{R}^d$.
  $\bullet$ In the case of Euclidean $k$-medians, generating a candidate set
via $n^{\mathcal{O}(1/\varepsilon^2)}$ executions of stochastic gradient
descent with adaptively determined constraint sets will once again give
approximation $1+\varepsilon$ with $\mathcal{O}(k\log(1/\varepsilon))$ centers
in $dk\log(1/\varepsilon)n^{\mathcal{O}(1/\varepsilon^2)}$ time.
  Ancillary results include: guarantees for cluster costs based on powers of
metrics; a brief, favorable empirical evaluation against $\texttt{kmeans++}$;
data-dependent bounds allowing $1+\varepsilon$ in the first two bullets above,
for example with $k$-medians over finite metric spaces.