pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
Authors: Binbin Lin, Qingyang Li, Qian Sun, Ming-Jun Lai, Ian Davidson, Wei Fan, Jieping Ye
Date: 2014-07-30 00:00:00

\textit{Drosophila melanogaster} has been established as a model organism for
investigating the fundamental principles of developmental gene interactions.
The gene expression patterns of \textit{Drosophila melanogaster} can be
documented as digital images, which are annotated with anatomical ontology
terms to facilitate pattern discovery and comparison. The automated annotation
of gene expression pattern images has received increasing attention due to the
recent expansion of the image database. The effectiveness of gene expression
pattern annotation relies on the quality of feature representation. Previous
studies have demonstrated that sparse coding is effective for extracting
features from gene expression images. However, solving sparse coding remains a
computationally challenging problem, especially when dealing with large-scale
data sets and learning large size dictionaries. In this paper, we propose a
novel algorithm to solve the sparse coding problem, called Stochastic
Coordinate Coding (SCC). The proposed algorithm alternatively updates the
sparse codes via just a few steps of coordinate descent and updates the
dictionary via second order stochastic gradient descent. The computational cost
is further reduced by focusing on the non-zero components of the sparse codes
and the corresponding columns of the dictionary only in the updating procedure.
Thus, the proposed algorithm significantly improves the efficiency and the
scalability, making sparse coding applicable for large-scale data sets and
large dictionary sizes. Our experiments on Drosophila gene expression data sets
demonstrate the efficiency and the effectiveness of the proposed algorithm.
Learning Multi-target Tracking with Quadratic Object Interactions
Authors: Shaofei Wang, Charless C. Fowlkes
Date: 2014-12-05 00:00:00
Tasks: Object, Structured Prediction

We describe a model for multi-target tracking based on associating
collections of candidate detections across frames of a video. In order to model
pairwise interactions between different tracks, such as suppression of
overlapping tracks and contextual cues about co-occurence of different objects,
we augment a standard min-cost flow objective with quadratic terms between
detection variables. We learn the parameters of this model using structured
prediction and a loss function which approximates the multi-target tracking
accuracy. We evaluate two different approaches to finding an optimal set of
tracks under model objective based on an LP relaxation and a novel greedy
extension to dynamic programming that handles pairwise interactions. We find
the greedy algorithm achieves equivalent performance to the LP relaxation while
being 2-7x faster than a commercial solver. The resulting model with learned
parameters outperforms existing methods across several categories on the KITTI
tracking benchmark.
Circumventing the Curse of Dimensionality in Prediction: Causal Rate-Distortion for Infinite-Order Markov Processes
Authors: Sarah Marzen, James P. Crutchfield
Date: 2014-12-09 00:00:00
Tasks: Clustering

Predictive rate-distortion analysis suffers from the curse of dimensionality:
clustering arbitrarily long pasts to retain information about arbitrarily long
futures requires resources that typically grow exponentially with length. The
challenge is compounded for infinite-order Markov processes, since conditioning
on finite sequences cannot capture all of their past dependencies. Spectral
arguments show that algorithms which cluster finite-length sequences fail
dramatically when the underlying process has long-range temporal correlations
and can fail even for processes generated by finite-memory hidden Markov
models. We circumvent the curse of dimensionality in rate-distortion analysis
of infinite-order processes by casting predictive rate-distortion objective
functions in terms of the forward- and reverse-time causal states of
computational mechanics. Examples demonstrate that the resulting causal
rate-distortion theory substantially improves current predictive
rate-distortion analyses.
Fast Learning of Relational Dependency Networks
Authors: Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick, Xiaoqian Yin, Yan Sun
Date: 2014-10-28 00:00:00

A Relational Dependency Network (RDN) is a directed graphical model widely
used for multi-relational data. These networks allow cyclic dependencies,
necessary to represent relational autocorrelations. We describe an approach for
learning both the RDN's structure and its parameters, given an input relational
database: First learn a Bayesian network (BN), then transform the Bayesian
network to an RDN. Thus fast Bayes net learning can provide fast RDN learning.
The BN-to-RDN transform comprises a simple, local adjustment of the Bayes net
structure and a closed-form transform of the Bayes net parameters. This method
can learn an RDN for a dataset with a million tuples in minutes. We empirically
compare our approach to state-of-the art RDN learning methods that use
functional gradient boosting, on five benchmark datasets. Learning RDNs via BNs
scales much better to large datasets than learning RDNs with boosting, and
provides competitive accuracy in predictions.
Plan or not: Remote Human-robot Teaming with Incomplete Task Information
Authors: Vignesh Narayanan, Yu Zhang, Nathaniel Mendoza, Subbarao Kambhampati
Date: 2014-12-09 00:00:00

Human-robot interaction can be divided into two categories based on the
physical distance between the human and robot: remote and proximal. In proximal
interaction, the human and robot often engage in close coordination; in remote
interaction, the human and robot are less coupled due to communication
constraints. As a result, providing automation for the robot in remote
interaction becomes more important. Thus far, human factor studies on
automation in remote human-robot interaction have been restricted to various
forms of supervision, in which the robot is essentially being used as a smart
mobile manipulation platform with sensing capabilities. In this paper, we
investigate the incorporation of general planning capability into the robot to
facilitate peer-to-peer human-robot teaming, in which the human and robot are
viewed as teammates that are physically separated. The human and robot share
the same global goal and collaborate to achieve it. Note that humans may feel
uncomfortable at such robot autonomy, which can potentially reduce teaming
performance. One important difference between peer-to-peer teaming and
supervised teaming is that an autonomous robot in peer-to-peer teaming can
achieve the goal alone when the task information is completely specified.
However, incompleteness often exists, which implies information asymmetry.
While information asymmetry can be desirable sometimes, it may also lead to the
robot choosing improper actions that negatively influence the teaming
performance. We aim to investigate the various trade-offs, e.g., mental
workload and situation awareness, between these two types of remote human-robot
teaming.
Zipf's Law and the Frequency of Characters or Words of Oracles
Authors: Xiuli Wang
Date: 2014-12-09 00:00:00

The article discusses the frequency of characters of Oracle,concluding that
the frequency and the rank of a word or character is fit to Zipf-Mandelboit Law
or Zipf's law with three parameters,and figuring out the parameters based on
the frequency,and pointing out that what some researchers of Oracle call the
assembling on the two ends is just a description by their impression about the
Oracle data.
What is a salient object? A dataset and a baseline model for salient object detection
Authors: Ali Borji
Date: 2014-12-08 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Superpixels

Salient object detection or salient region detection models, diverging from
fixation prediction models, have traditionally been dealing with locating and
segmenting the most salient object or region in a scene. While the notion of
most salient object is sensible when multiple objects exist in a scene, current
datasets for evaluation of saliency detection approaches often have scenes with
only one single object. We introduce three main contributions in this paper:
First, we take an indepth look at the problem of salient object detection by
studying the relationship between where people look in scenes and what they
choose as the most salient object when they are explicitly asked. Based on the
agreement between fixations and saliency judgments, we then suggest that the
most salient object is the one that attracts the highest fraction of fixations.
Second, we provide two new less biased benchmark datasets containing scenes
with multiple objects that challenge existing saliency models. Indeed, we
observed a severe drop in performance of 8 state-of-the-art models on our
datasets (40% to 70%). Third, we propose a very simple yet powerful model based
on superpixels to be used as a baseline for model evaluation and comparison.
While on par with the best models on MSRA-5K dataset, our model wins over other
models on our data highlighting a serious drawback of existing models, which is
convoluting the processes of locating the most salient object and its
segmentation. We also provide a review and statistical analysis of some labeled
scene datasets that can be used for evaluating salient object detection models.
We believe that our work can greatly help remedy the over-fitting of models to
existing biased datasets and opens new venues for future research in this
fast-evolving field.
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework
Authors: Ivan Titov, Ehsan Khoddam
Date: 2014-12-08 00:00:00
Proceeding: HLT 2015 5
Tasks: Semantic Role Labeling

We introduce a new approach to unsupervised estimation of feature-rich
semantic role labeling models. Our model consists of two components: (1) an
encoding component: a semantic role labeling model which predicts roles given a
rich set of syntactic and lexical features; (2) a reconstruction component: a
tensor factorization model which relies on roles to predict argument fillers.
When the components are estimated jointly to minimize errors in argument
reconstruction, the induced roles largely correspond to roles defined in
annotated resources. Our method performs on par with most accurate role
induction methods on English and German, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
languages.
Covariance Matrices for Mean Field Variational Bayes
Authors: Ryan Giordano, Tamara Broderick
Date: 2014-10-24 00:00:00

Mean Field Variational Bayes (MFVB) is a popular posterior approximation
method due to its fast runtime on large-scale data sets. However, it is well
known that a major failing of MFVB is its (sometimes severe) underestimates of
the uncertainty of model variables and lack of information about model variable
covariance. We develop a fast, general methodology for exponential families
that augments MFVB to deliver accurate uncertainty estimates for model
variables -- both for individual variables and coherently across variables.
MFVB for exponential families defines a fixed-point equation in the means of
the approximating posterior, and our approach yields a covariance estimate by
perturbing this fixed point. Inspired by linear response theory, we call our
method linear response variational Bayes (LRVB). We demonstrate the accuracy of
our method on simulated data sets.
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
Authors: Awni Y. Hannun, Andrew L. Maas, Daniel Jurafsky, Andrew Y. Ng
Date: 2014-08-12 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present a method to perform first-pass large vocabulary continuous speech
recognition using only a neural network and language model. Deep neural network
acoustic models are now commonplace in HMM-based speech recognition systems,
but building such systems is a complex, domain-specific task. Recent work
demonstrated the feasibility of discarding the HMM sequence modeling framework
by directly predicting transcript text from audio. This paper extends this
approach in two ways. First, we demonstrate that a straightforward recurrent
neural network architecture can achieve a high level of accuracy. Second, we
propose and evaluate a modified prefix-search decoding algorithm. This approach
to decoding enables first-pass speech recognition with a language model,
completely unaided by the cumbersome infrastructure of HMM-based systems.
Experiments on the Wall Street Journal corpus demonstrate fairly competitive
word error rates, and the importance of bi-directional network recurrence.
Image quality assessment measure based on natural image statistics in the Tetrolet domain
Authors: Abdelkaher Ait Abdelouahad, Mohammed El Hassouni, Hocine Cherifi, Driss Aboutajdine
Date: 2014-12-08 00:00:00
Tasks: Image Quality Assessment

This paper deals with a reduced reference (RR) image quality measure based on
natural image statistics modeling. For this purpose, Tetrolet transform is used
since it provides a convenient way to capture local geometric structures. This
transform is applied to both reference and distorted images. Then, Gaussian
Scale Mixture (GSM) is proposed to model subbands in order to take account
statistical dependencies between tetrolet coefficients. In order to quantify
the visual degradation, a measure based on Kullback Leibler Divergence (KLD) is
provided. The proposed measure was tested on the Cornell VCL A-57 dataset and
compared with other measures according to FR-TV1 VQEG framework.
A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic
Authors: Ali Aajli, Karim Afdel
Date: 2014-12-08 00:00:00

In recent years, adaptive learning systems rely increasingly on learning
hierarchy to customize the educational logic developed in their courses. Most
approaches do not consider that the relationships of prerequisites between the
skills are fuzzy relationships. In this article, we describe a new approach of
a practical application of fuzzy logic techniques to the construction of
learning hierarchies. For this, we use a learning hierarchy predefined by one
or more experts of a specific field. However, the relationships of
prerequisites between the skills in the learning hierarchy are not definitive
and they are fuzzy relationships. Indeed, we measure relevance degree of all
relationships existing in this learning hierarchy and we try to answer to the
following question: Is the relationships of prerequisites predefined in initial
learning hierarchy are correctly established or not?
A tool for implementation of a domain model based on fuzzy relationships
Authors: Ali Aajli, Karim Afdel
Date: 2014-12-08 00:00:00

The domain model is one of the important components used by adaptive learning
systems to automatically generate customized courses for the learners. In this
paper our contribution is to propose a new tool for implementation of a domain
model based on fuzzy relationships among concepts. This tool allows the experts
and teachers to find the best parameters in order to adapt the learners's
differences.
Real-Time System of Hand Detection And Gesture Recognition In Cyber Presence Interactive System For E-Learning
Authors: Bousaaid Mourad, Ayaou Tarik, Afdel Karim, Estraillier Pascal
Date: 2014-12-08 00:00:00
Tasks: Gesture Recognition, Hand Detection

The development of technologies of multimedia, linked to that of Internet and
democratization of high outflow, has made henceforth E-learning possible for
learners being in virtual classes and geographically distributed. The quality
and quantity of asynchronous and synchronous communications are the key
elements for E-learning success. It is important to have a propitious
supervision to reduce the feeling of isolation in E-learning. This feeling of
isolation is among the main causes of loss and high rates of stalling in
E-learning. The researches to be conducted in this domain aim to bring
solutions of convergence coming from real time image for the capture and
recognition of hand gestures. These gestures will be analyzed by the system and
transformed as indicator of participation. This latter is displayed in the
table of performance of the tutor as a curve according to the time. In case of
isolation of learner, the indicator of participation will become red and the
tutor will be informed of learners with difficulties to participate during
learning session.
When Computer Vision Gazes at Cognition
Authors: Tao Gao, Daniel Harari, Joshua Tenenbaum, Shimon Ullman
Date: 2014-12-08 00:00:00
Tasks: Task 2

Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.
Probabilistic low-rank matrix completion on finite alphabets
Authors: Jean Lafond, Olga Klopp, Eric Moulines, Jospeh Salmon
Date: 2014-12-08 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Dimensionality Reduction, Low-Rank Matrix Completion, Matrix Completion, Recommendation Systems

The task of reconstructing a matrix given a sample of observedentries is
known as the matrix completion problem. It arises ina wide range of problems,
including recommender systems, collaborativefiltering, dimensionality
reduction, image processing, quantum physics or multi-class classificationto
name a few. Most works have focused on recovering an unknown real-valued
low-rankmatrix from randomly sub-sampling its entries.Here, we investigate the
case where the observations take a finite number of values, corresponding for
examples to ratings in recommender systems or labels in multi-class
classification.We also consider a general sampling scheme (not necessarily
uniform) over the matrix entries.The performance of a nuclear-norm penalized
estimator is analyzed theoretically.More precisely, we derive bounds for the
Kullback-Leibler divergence between the true and estimated distributions.In
practice, we have also proposed an efficient algorithm based on lifted
coordinate gradient descent in order to tacklepotentially high dimensional
settings.
Low Complexity Regularization of Linear Inverse Problems
Authors: Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili
Date: 2014-07-07 00:00:00

Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of $\ell^2$-stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem.
Phishing Detection in IMs using Domain Ontology and CBA - An innovative Rule Generation Approach
Authors: Mohammad S. Qaseem, A. Govardhan
Date: 2014-12-08 00:00:00

User ignorance towards the use of communication services like Instant
Messengers, emails, websites, social networks etc. is becoming the biggest
advantage for phishers. It is required to create technical awareness in users
by educating them to create a phishing detection application which would
generate phishing alerts for the user so that phishing messages are not
ignored. The lack of basic security features to detect and prevent phishing has
had a profound effect on the IM clients, as they lose their faith in e-banking
and e-commerce transactions, which will have a disastrous impact on the
corporate and banking sectors and businesses which rely heavily on the
internet. Very little research contributions were available in for phishing
detection in Instant messengers. A context based, dynamic and intelligent
phishing detection methodology in IMs is proposed, to analyze and detect
phishing in Instant Messages with relevance to domain ontology (OBIE) and
utilizes the Classification based on Association (CBA) for generating phishing
rules and alerting the victims. A PDS Monitoring system algorithm is used to
identify the phishing activity during exchange of messages in IMs, with high
ratio of precision and recall. The results have shown improvement by the
increased percentage of precision and recall when compared to the existing
methods.