pwc-archive/papers-with-abstracts huggingface dataset:
Joint Video and Text Parsing for Understanding Events and Answering Queries
Authors: Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe, Song-Chun Zhu
Date: 2013-08-29 00:00:00
Tasks: Semantic Parsing

We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results.
A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms
Authors: D. Thilagavathi, Antony Selvadoss Thanamani
Date: 2014-02-21 00:00:00
Tasks: CPU, Distributed Computing, Scheduling

Computational Grids are a new trend in distributed computing systems. They
allow the sharing of geographically distributed resources in an efficient way,
extending the boundaries of what we perceive as distributed computing. Various
sciences can benefit from the use of grids to solve CPU-intensive problems,
creating potential benefits to the entire society. Job scheduling is an
integrated part of parallel and distributed computing. It allows selecting
correct match of resource for a particular job and thus increases the job
throughput and utilization of resources. Job should be scheduled in an
automatic way to make the system more reliable, accessible and less sensitive
to subsystem failures. This paper provides a survey on various heuristic
algorithms, used for scheduling in grid.
Pareto-depth for Multiple-query Image Retrieval
Authors: Ko-Jen Hsiao, Jeff Calder, Alfred O. Hero III
Date: 2014-02-21 00:00:00
Tasks: Attribute, Content-Based Image Retrieval, Image Retrieval, Information Retrieval, Retrieval

Most content-based image retrieval systems consider either one single query,
or multiple queries that include the same object or represent the same semantic
information. In this paper we consider the content-based image retrieval
problem for multiple query images corresponding to different image semantics.
We propose a novel multiple-query information retrieval algorithm that combines
the Pareto front method (PFM) with efficient manifold ranking (EMR). We show
that our proposed algorithm outperforms state of the art multiple-query
retrieval algorithms on real-world image databases. We attribute this
performance improvement to concavity properties of the Pareto fronts, and prove
a theoretical result that characterizes the asymptotic concavity of the fronts.
Distribution-Independent Reliable Learning
Authors: Varun Kanade, Justin Thaler
Date: 2014-02-20 00:00:00
Tasks: Attribute, PAC learning

We study several questions in the reliable agnostic learning framework of
Kalai et al. (2009), which captures learning tasks in which one type of error
is costlier than others. A positive reliable classifier is one that makes no
false positive errors. The goal in the positive reliable agnostic framework is
to output a hypothesis with the following properties: (i) its false positive
error rate is at most $\epsilon$, (ii) its false negative error rate is at most
$\epsilon$ more than that of the best positive reliable classifier from the
class. A closely related notion is fully reliable agnostic learning, which
considers partial classifiers that are allowed to predict "unknown" on some
inputs. The best fully reliable partial classifier is one that makes no errors
and minimizes the probability of predicting "unknown", and the goal in fully
reliable learning is to output a hypothesis that is almost as good as the best
fully reliable partial classifier from a class.
  For distribution-independent learning, the best known algorithms for PAC
learning typically utilize polynomial threshold representations, while the
state of the art agnostic learning algorithms use point-wise polynomial
approximations. We show that one-sided polynomial approximations, an
intermediate notion between polynomial threshold representations and point-wise
polynomial approximations, suffice for learning in the reliable agnostic
settings. We then show that majorities can be fully reliably learned and
disjunctions of majorities can be positive reliably learned, through
constructions of appropriate one-sided polynomial approximations. Our fully
reliable algorithm for majorities provides the first evidence that fully
reliable learning may be strictly easier than agnostic learning. Our algorithms
also satisfy strong attribute-efficiency properties, and provide smooth
tradeoffs between sample complexity and running time.
Detecting Opinions in Tweets
Authors: Abdelmalek Amine, Reda Mohamed Hamou, Michel Simonet
Date: 2014-02-20 00:00:00

Given the incessant growth of documents describing the opinions of different
people circulating on the web, including Web 2.0 has made it possible to give
an opinion on any product in the net. In this paper, we examine the various
opinions expressed in the tweets and classify them positive, negative or
neutral by using the emoticons for the Bayesian method and adjectives and
adverbs for the Turney's method
Group-sparse Matrix Recovery
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2014-02-20 00:00:00
Tasks: Clustering, regression

We apply the OSCAR (octagonal selection and clustering algorithms for
regression) in recovering group-sparse matrices (two-dimensional---2D---arrays)
from compressive measurements. We propose a 2D version of OSCAR (2OSCAR)
consisting of the $\ell_1$ norm and the pair-wise $\ell_{\infty}$ norm, which
is convex but non-differentiable. We show that the proximity operator of 2OSCAR
can be computed based on that of OSCAR. The 2OSCAR problem can thus be
efficiently solved by state-of-the-art proximal splitting algorithms.
Experiments on group-sparse 2D array recovery show that 2OSCAR regularization
solved by the SpaRSA algorithm is the fastest choice, while the PADMM algorithm
(with debiasing) yields the most accurate results.
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2014-02-20 00:00:00
Tasks: Compressive Sensing

We propose a new method, {\it binary fused compressive sensing} (BFCS), to
recover sparse piece-wise smooth signals from 1-bit compressive measurements.
The proposed algorithm is a modification of the previous {\it binary iterative
hard thresholding} (BIHT) algorithm, where, in addition to the sparsity
constraint, the total-variation of the recovered signal is upper constrained.
As in BIHT, the data term of the objective function is an one-sided $\ell_1$
(or $\ell_2$) norm. Experiments on the recovery of sparse piece-wise smooth
signals show that the proposed algorithm is able to take advantage of the
piece-wise smoothness of the original signal, achieving more accurate recovery
than BIHT.
A novel sparsity and clustering regularization
Authors: Xiangrong Zeng, Mário A. T. Figueiredo
Date: 2013-10-18 00:00:00
Tasks: Clustering, regression

We propose a novel SPARsity and Clustering (SPARC) regularizer, which is a
modified version of the previous octagonal shrinkage and clustering algorithm
for regression (OSCAR), where, the proposed regularizer consists of a
$K$-sparse constraint and a pair-wise $\ell_{\infty}$ norm restricted on the
$K$ largest components in magnitude. The proposed regularizer is able to
separably enforce $K$-sparsity and encourage the non-zeros to be equal in
magnitude. Moreover, it can accurately group the features without shrinking
their magnitude. In fact, SPARC is closely related to OSCAR, so that the
proximity operator of the former can be efficiently computed based on that of
the latter, allowing using proximal splitting algorithms to solve problems with
SPARC regularization. Experiments on synthetic data and with benchmark breast
cancer data show that SPARC is a competitive group-sparsity inducing
regularizer for regression and classification.
Real-time Automatic Emotion Recognition from Body Gestures
Authors: Stefano Piana, Alessandra Staglianò, Francesca Odone, Alessandro Verri, Antonio Camurri
Date: 2014-02-20 00:00:00
Tasks: Emotion Recognition

Although psychological research indicates that bodily expressions convey important affective information, to date research in emotion recognition focused mainly on facial expression or voice analysis. In this paper we propose an approach to realtime automatic emotion recognition from body movements. A set of postural, kinematic, and geometrical features are extracted from sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed method has been assessed on data acquired through two different systems: a professionalgrade optical motion capture system, and Microsoft Kinect. The system has been assessed on a "six emotions" recognition problem, and using a leave-one-subject-out cross validation strategy, reached an overall recognition rate of 61.3% which is very close to the recognition rate of 61.9% obtained by human observers. To provide further testing of the system, two games were developed, where one or two users have to interact to understand and express emotions with their body.
Expressing social attitudes in virtual agents for social training games
Authors: Nicolas Sabouret, Hazaël Jones, Magalie Ochs, Mathieu Chollet, Catherine Pelachaud
Date: 2014-02-20 00:00:00

The use of virtual agents in social coaching has increased rapidly in the
last decade. In order to train the user in different situations than can occur
in real life, the virtual agent should be able to express different social
attitudes. In this paper, we propose a model of social attitudes that enables a
virtual agent to reason on the appropriate social attitude to express during
the interaction with a user given the course of the interaction, but also the
emotions, mood and personality of the agent. Moreover, the model enables the
virtual agent to display its social attitude through its non-verbal behaviour.
The proposed model has been developed in the context of job interview
simulation. The methodology used to develop such a model combined a theoretical
and an empirical approach. Indeed, the model is based both on the literature in
Human and Social Sciences on social attitudes but also on the analysis of an
audiovisual corpus of job interviews and on post-hoc interviews with the
recruiters on their expressed attitudes during the job interview.
A logical model of Theory of Mind for virtual agents in the context of job interview simulation
Authors: Marwen Belkaid, Nicolas Sabouret
Date: 2014-02-20 00:00:00

Job interview simulation with a virtual agents aims at improving people's
social skills and supporting professional inclusion. In such simulators, the
virtual agent must be capable of representing and reasoning about the user's
mental state based on social cues that inform the system about his/her affects
and social attitude. In this paper, we propose a formal model of Theory of Mind
(ToM) for virtual agent in the context of human-agent interaction that focuses
on the affective dimension. It relies on a hybrid ToM that combines the two
major paradigms of the domain. Our framework is based on modal logic and
inference rules about the mental states, emotions and social relations of both
actors. Finally, we present preliminary results regarding the impact of such a
model on natural interaction in the context of job interviews simulation.
Assessing the Reach and Impact of Game-Based Learning Approaches to Cultural Competency and Behavioural Change
Authors: Ian Dunwell, Panagiotis Petridis, Petros Lameras, Maurice Hendrix, Stella Doukianou, Mark Gaved
Date: 2014-02-20 00:00:00

As digital games continue to be explored as solutions to educational and
behavioural challenges, the need for evaluation methodologies which support
both the unique nature of the format and the need for comparison with other
approaches continues to increase. In this workshop paper, a range of challenges
are described related specifically to the case of cultural learning using
digital games, in terms of how it may best be assessed, understood, and
sustained through an iterative process supported by research. An evaluation
framework is proposed, identifying metrics for reach and impact and their
associated challenges, as well as presenting ethical considerations and the
means to utilize evaluation outcomes within an iterative cycle, and to provide
feedback to learners. Presenting as a case study a serious game from the Mobile
Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive
Learning Technologies and Social Networks (MASELTOV) project, the use of the
framework in the context of an integrative project is discussed, with emphasis
on the need to view game-based learning as a blended component of the cultural
learning process, rather than a standalone solution. The particular case of
mobile gaming is also considered within this case study, providing a platform
by which to deliver and update content in response to evaluation outcomes.
Discussion reflects upon the general challenges related to the assessment of
cultural learning, and behavioural change in more general terms, suggesting
future work should address the need to provide sustainable, research-driven
platforms for game-based learning content.
Using the Crowd to Generate Content for Scenario-Based Serious-Games
Authors: Sigal Sina, Sarit Kraus, Avi Rosenfeld
Date: 2014-02-20 00:00:00

In the last decade, scenario-based serious-games have become a main tool for
learning new skills and capabilities. An important factor in the development of
such systems is the overhead in time, cost and human resources to manually
create the content for these scenarios. We focus on how to create content for
scenarios in medical, military, commerce and gaming applications where
maintaining the integrity and coherence of the content is integral for the
system's success. To do so, we present an automatic method for generating
content about everyday activities through combining computer science techniques
with the crowd. We use the crowd in three basic ways: to capture a database of
scenarios of everyday activities, to generate a database of likely replacements
for specific events within that scenario, and to evaluate the resulting
scenarios. We found that the generated scenarios were rated as reliable and
consistent by the crowd when compared to the scenarios that were originally
captured. We also compared the generated scenarios to those created by
traditional planning techniques. We found that both methods were equally
effective in generated reliable and consistent scenarios, yet the main
advantages of our approach is that the content we generate is more varied and
much easier to create. We have begun integrating this approach within a
scenario-based training application for novice investigators within the law
enforcement departments to improve their questioning skills.
Friend Inspector: A Serious Game to Enhance Privacy Awareness in Social Networks
Authors: Alexandra Cetto, Michael Netter, Günther Pernul, Christian Richthammer, Moritz Riesner, Christian Roth, Johannes Sänger
Date: 2014-02-20 00:00:00

Currently, many users of Social Network Sites are insufficiently aware of who
can see their shared personal items. Nonetheless, most approaches focus on
enhancing privacy in Social Networks through improved privacy settings,
neglecting the fact that privacy awareness is a prerequisite for privacy
control. Social Network users first need to know about privacy issues before
being able to make adjustments. In this paper, we introduce Friend Inspector, a
serious game that allows its users to playfully increase their privacy
awareness on Facebook. Since its launch, Friend Inspector has attracted a
significant number of visitors, emphasising the need for better tools to
understand privacy settings on Social Networks.
An Algorithm for Training Polynomial Networks
Authors: Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Date: 2013-04-26 00:00:00

We consider deep neural networks, in which the output of each node is a
quadratic function of its inputs. Similar to other deep architectures, these
networks can compactly represent any function on a finite training set. The
main goal of this paper is the derivation of an efficient layer-by-layer
algorithm for training such networks, which we denote as the \emph{Basis
Learner}. The algorithm is a universal learner in the sense that the training
error is guaranteed to decrease at every iteration, and can eventually reach
zero under mild conditions. We present practical implementations of this
algorithm, as well as preliminary experimental results. We also compare our
deep architecture to other shallow architectures for learning polynomials, in
particular kernel learning.
Enhanced Secure Algorithm for Fingerprint Recognition
Authors: Amira Mohammad Abdel-Mawgoud Saleh
Date: 2014-02-20 00:00:00
Tasks: Binarization

Fingerprint recognition requires a minimal effort from the user, does not
capture other information than strictly necessary for the recognition process,
and provides relatively good performance. A critical step in fingerprint
identification system is thinning of the input fingerprint image. The
performance of a minutiae extraction algorithm relies heavily on the quality of
the thinning algorithm. So, a fast fingerprint thinning algorithm is proposed.
The algorithm works directly on the gray-scale image as binarization of
fingerprint causes many spurious minutiae and also removes many important
features. The performance of the thinning algorithm is evaluated and
experimental results show that the proposed thinning algorithm is both fast and
accurate. A new minutiae-based fingerprint matching technique is proposed. The
main idea is that each fingerprint is represented by a minutiae table of just
two columns in the database. The number of different minutiae types
(terminations and bifurcations) found in each track of a certain width around
the core point of the fingerprint is recorded in this table. Each row in the
table represents a certain track, in the first column, the number of
terminations in each track is recorded, in the second column, the number of
bifurcations in each track is recorded. The algorithm is rotation and
translation invariant, and needs less storage size. Experimental results show
that recognition accuracy is 98%, with Equal Error Rate (EER) of 2%. Finally,
the integrity of the data transmission via communication channels must be
secure all the way from the scanner to the application. After applying Gaussian
noise addition, and JPEG compression with high and moderate quality factors on
the watermarked fingerprint images, recognition accuracy decreases slightly to
reach 96%.
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
Authors: Vikash Mansinghka, Eric Jonas
Date: 2014-02-20 00:00:00
Tasks: Bayesian Inference

The brain interprets ambiguous sensory information faster and more reliably
than modern computers, using neurons that are slower and less reliable than
logic gates. But Bayesian inference, which underpins many computational models
of perception and cognition, appears computationally challenging even given
modern transistor speeds and energy budgets. The computational principles and
structures needed to narrow this gap are unknown. Here we show how to build
fast Bayesian computing machines using intentionally stochastic, digital parts,
narrowing this efficiency gap by multiple orders of magnitude. We find that by
connecting stochastic digital components according to simple mathematical
rules, one can build massively parallel, low precision circuits that solve
Bayesian inference problems and are compatible with the Poisson firing
statistics of cortical neurons. We evaluate circuits for depth and motion
perception, perceptual learning and causal reasoning, each performing inference
over 10,000+ latent variables in real time - a 1,000x speed advantage over
commodity microprocessors. These results suggest a new role for randomness in
the engineering and reverse-engineering of intelligent computation.
Survey on Sparse Coded Features for Content Based Face Image Retrieval
Authors: D. Johnvictor, G. Selvavinayagam
Date: 2014-02-20 00:00:00
Tasks: Content-Based Image Retrieval, Face Image Retrieval, Image Retrieval, Retrieval

Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo sharing sites are getting more popularity, large human face photos are available in database. Multiple types of facial features are used to represent discriminality on large scale human facial image database. Searching and mining of facial images are challenging problems and important research issues. Sparse representation on features provides significant improvement in indexing related images to query image.