pwc-archive/papers-with-abstracts huggingface dataset:
Aggressive actions and anger detection from multiple modalities using Kinect
Authors: Amol Patwardhan, Gerald Knapp
Date: 2016-07-05 00:00:00
Tasks: Action Detection

Prison facilities, mental correctional institutions, sports bars and places
of public protest are prone to sudden violence and conflicts. Surveillance
systems play an important role in mitigation of hostile behavior and
improvement of security by detecting such provocative and aggressive
activities. This research proposed using automatic aggressive behavior and
anger detection to improve the effectiveness of the surveillance systems. An
emotion and aggression aware component will make the surveillance system highly
responsive and capable of alerting the security guards in real time. This
research proposed facial expression, head, hand and body movement and speech
tracking for detecting anger and aggressive actions. Recognition was achieved
using support vector machines and rule based features. The multimodal affect
recognition precision rate for anger improved by 15.2% and recall rate improved
by 11.7% when behavioral rule based features were used in aggressive action
detection.
Application of Statistical Relational Learning to Hybrid Recommendation Systems
Authors: Shuo Yang, Mohammed Korayem, Khalifeh Aljadda, Trey Grainger, Sriraam Natarajan
Date: 2016-07-04 00:00:00
Tasks: Collaborative Filtering, Feature Engineering, Recommendation Systems, Relational Reasoning

Recommendation systems usually involve exploiting the relations among known
features and content that describe items (content-based filtering) or the
overlap of similar users who interacted with or rated the target item
(collaborative filtering). To combine these two filtering approaches, current
model-based hybrid recommendation systems typically require extensive feature
engineering to construct a user profile. Statistical Relational Learning (SRL)
provides a straightforward way to combine the two approaches. However, due to
the large scale of the data used in real world recommendation systems, little
research exists on applying SRL models to hybrid recommendation systems, and
essentially none of that research has been applied on real big-data-scale
systems. In this paper, we proposed a way to adapt the state-of-the-art in SRL
learning approaches to construct a real hybrid recommendation system.
Furthermore, in order to satisfy a common requirement in recommendation systems
(i.e. that false positives are more undesirable and therefore penalized more
harshly than false negatives), our approach can also allow tuning the trade-off
between the precision and recall of the system in a principled way. Our
experimental results demonstrate the efficiency of our proposed approach as
well as its improved performance on recommendation precision.
Generic Statistical Relational Entity Resolution in Knowledge Graphs
Authors: Jay Pujara, Lise Getoor
Date: 2016-07-04 00:00:00
Tasks: Entity Resolution, Knowledge Graphs

Entity resolution, the problem of identifying the underlying entity of
references found in data, has been researched for many decades in many
communities. A common theme in this research has been the importance of
incorporating relational features into the resolution process. Relational
entity resolution is particularly important in knowledge graphs (KGs), which
have a regular structure capturing entities and their interrelationships. We
identify three major problems in KG entity resolution: (1) intra-KG reference
ambiguity; (2) inter-KG reference ambiguity; and (3) ambiguity when extending
KGs with new facts. We implement a framework that generalizes across these
three settings and exploits this regular structure of KGs. Our framework has
many advantages over custom solutions widely deployed in industry, including
collective inference, scalability, and interpretability. We apply our framework
to two real-world KG entity resolution problems, ambiguity in NELL and merging
data from Freebase and MusicBrainz, demonstrating the importance of relational
features.
Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?
Authors: Eduardo Romera, Luis M. Bergasa, Roberto Arroyo
Date: 2016-07-04 00:00:00
Tasks: Autonomous Driving, Image Segmentation, Segmentation, Semantic Segmentation

Autonomous driving is a challenging topic that requires complex solutions in
perception tasks such as recognition of road, lanes, traffic signs or lights,
vehicles and pedestrians. Through years of research, computer vision has grown
capable of tackling these tasks with monocular detectors that can provide
remarkable detection rates with relatively low processing times. However, the
recent appearance of Convolutional Neural Networks (CNNs) has revolutionized
the computer vision field and has made possible approaches to perform full
pixel-wise semantic segmentation in times close to real time (even on hardware
that can be carried on a vehicle). In this paper, we propose to use full image
segmentation as an approach to simplify and unify most of the detection tasks
required in the perception module of an autonomous vehicle, analyzing major
concerns such as computation time and detection performance.
Deep Exploration via Bootstrapped DQN
Authors: Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy
Date: 2016-02-15 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Atari Games, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient exploration in complex environments remains a major challenge for
reinforcement learning. We propose bootstrapped DQN, a simple algorithm that
explores in a computationally and statistically efficient manner through use of
randomized value functions. Unlike dithering strategies such as epsilon-greedy
exploration, bootstrapped DQN carries out temporally-extended (or deep)
exploration; this can lead to exponentially faster learning. We demonstrate
these benefits in complex stochastic MDPs and in the large-scale Arcade
Learning Environment. Bootstrapped DQN substantially improves learning times
and performance across most Atari games.
Sensitivity analysis, multilinearity and beyond
Authors: Manuele Leonelli, Christiane Görgen, Jim Q. Smith
Date: 2015-12-07 00:00:00
Tasks: Sensitivity

Sensitivity methods for the analysis of the outputs of discrete Bayesian
networks have been extensively studied and implemented in different software
packages. These methods usually focus on the study of sensitivity functions and
on the impact of a parameter change to the Chan-Darwiche distance. Although not
fully recognized, the majority of these results heavily rely on the multilinear
structure of atomic probabilities in terms of the conditional probability
parameters associated with this type of network. By defining a statistical
model through the polynomial expression of its associated defining conditional
probabilities, we develop a unifying approach to sensitivity methods applicable
to a large suite of models including extensions of Bayesian networks, for
instance context-specific and dynamic ones, and chain event graphs. By then
focusing on models whose defining polynomial is multilinear, our algebraic
approach enables us to prove that the Chan-Darwiche distance is minimized for a
certain class of multi-parameter contemporaneous variations when parameters are
proportionally covaried.
Superintelligence cannot be contained: Lessons from Computability Theory
Authors: Manuel Alfonseca, Manuel Cebrian, Antonio Fernandez Anta, Lorenzo Coviello, Andres Abeliuk, Iyad Rahwan
Date: 2016-07-04 00:00:00

Superintelligence is a hypothetical agent that possesses intelligence far
surpassing that of the brightest and most gifted human minds. In light of
recent advances in machine intelligence, a number of scientists, philosophers
and technologists have revived the discussion about the potential catastrophic
risks entailed by such an entity. In this article, we trace the origins and
development of the neo-fear of superintelligence, and some of the major
proposals for its containment. We argue that such containment is, in principle,
impossible, due to fundamental limits inherent to computing itself. Assuming
that a superintelligence will contain a program that includes all the programs
that can be executed by a universal Turing machine on input potentially as
complex as the state of the world, strict containment requires simulations of
such a program, something theoretically (and practically) infeasible.
Encoding Cryptographic Functions to SAT Using Transalg System
Authors: Ilya Otpuschennikov, Alexander Semenov, Irina Gribanova, Oleg Zaikin, Stepan Kochemazov
Date: 2016-07-04 00:00:00
Tasks: Cryptanalysis, Distributed Computing

In this paper we propose the technology for constructing propositional
encodings of discrete functions. It is aimed at solving inversion problems of
considered functions using state-of-the-art SAT solvers. We implemented this
technology in the form of the software system called Transalg, and used it to
construct SAT encodings for a number of cryptanalysis problems. By applying SAT
solvers to these encodings we managed to invert several cryptographic
functions. In particular, we used the SAT encodings produced by Transalg to
construct the family of two-block MD5 collisions in which the first 10 bytes
are zeros. Also we used Transalg encoding for the widely known A5/1 keystream
generator to solve several dozen of its cryptanalysis instances in a
distributed computing environment. In the paper we compare in detail the
functionality of Transalg with that of similar software systems.
TensiStrength: Stress and relaxation magnitude detection for social media texts
Authors: Mike Thelwall
Date: 2016-07-01 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

Computer systems need to be able to react to stress in order to perform
optimally on some tasks. This article describes TensiStrength, a system to
detect the strength of stress and relaxation expressed in social media text
messages. TensiStrength uses a lexical approach and a set of rules to detect
direct and indirect expressions of stress or relaxation, particularly in the
context of transportation. It is slightly more effective than a comparable
sentiment analysis program, although their similar performances occur despite
differences on almost half of the tweets gathered. The effectiveness of
TensiStrength depends on the nature of the tweets classified, with tweets that
are rich in stress-related terms being particularly problematic. Although
generic machine learning methods can give better performance than TensiStrength
overall, they exploit topic-related terms in a way that may be undesirable in
practical applications and that may not work as well in more focused contexts.
In conclusion, TensiStrength and generic machine learning approaches work well
enough to be practical choices for intelligent applications that need to take
advantage of stress information, and the decision about which to use depends on
the nature of the texts analysed and the purpose of the task.
A Game-Theoretic Approach to Word Sense Disambiguation
Authors: Rocco Tripodi, Marcello Pelillo
Date: 2016-06-24 00:00:00
Proceeding: CL 2017 4
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation

This paper presents a new model for word sense disambiguation formulated in
terms of evolutionary game theory, where each word to be disambiguated is
represented as a node on a graph whose edges represent word relations and
senses are represented as classes. The words simultaneously update their class
membership preferences according to the senses that neighboring words are
likely to choose. We use distributional information to weigh the influence that
each word has on the decisions of the others and semantic similarity
information to measure the strength of compatibility among the choices. With
this information we can formulate the word sense disambiguation problem as a
constraint satisfaction problem and solve it using tools derived from game
theory, maintaining the textual coherence. The model is based on two ideas:
similar words should be assigned to similar classes and the meaning of a word
does not depend on all the words in a text but just on some of them. The paper
provides an in-depth motivation of the idea of modeling the word sense
disambiguation problem in terms of game theory, which is illustrated by an
example. The conclusion presents an extensive analysis on the combination of
similarity measures to use in the framework and a comparison with
state-of-the-art systems. The results show that our model outperforms
state-of-the-art algorithms and can be applied to different tasks and in
different scenarios.
Delta divergence: A novel decision cognizant measure of classifier incongruence
Authors: Josef Kittler, Cemre Zor
Date: 2016-04-15 00:00:00

Disagreement between two classifiers regarding the class membership of an
observation in pattern recognition can be indicative of an anomaly and its
nuance. As in general classifiers base their decision on class aposteriori
probabilities, the most natural approach to detecting classifier incongruence
is to use divergence. However, existing divergences are not particularly
suitable to gauge classifier incongruence. In this paper, we postulate the
properties that a divergence measure should satisfy and propose a novel
divergence measure, referred to as Delta divergence. In contrast to existing
measures, it is decision cognizant. The focus in Delta divergence on the
dominant hypotheses has a clutter reducing property, the significance of which
grows with increasing number of classes. The proposed measure satisfies other
important properties such as symmetry, and independence of classifier
confidence. The relationship of the proposed divergence to some baseline
measures is demonstrated experimentally, showing its superiority.
Modeling of Item-Difficulty for Ontology-based MCQs
Authors: Vinu E. V, Tahani Alsubait, P. Sreenivasa Kumar
Date: 2016-07-04 00:00:00
Tasks: Multiple-choice

Multiple choice questions (MCQs) that can be generated from a domain ontology
can significantly reduce human effort & time required for authoring &
administering assessments in an e-Learning environment. Even though here are
various methods for generating MCQs from ontologies, methods for determining
the difficulty-levels of such MCQs are less explored. In this paper, we study
various aspects and factors that are involved in determining the
difficulty-score of an MCQ, and propose an ontology-based model for the
prediction. This model characterizes the difficulty values associated with the
stem and choice set of the MCQs, and describes a measure which combines both
the scores. Further more, the notion of assigning difficultly-scores based on
the skill level of the test taker is utilized for predicating difficulty-score
of a stem. We studied the effectiveness of the predicted difficulty-scores with
the help of a psychometric model from the Item Response Theory, by involving
real-students and domain experts. Our results show that, the predicated
difficulty-levels of the MCQs are having high correlation with their actual
difficulty-levels.
Forest Floor Visualizations of Random Forests
Authors: Soeren H. Welling, Hanne H. F. Refsgaard, Per B. Brockhoff, Line H. Clemmensen
Date: 2016-05-30 00:00:00

We propose a novel methodology, forest floor, to visualize and interpret
random forest (RF) models. RF is a popular and useful tool for non-linear
multi-variate classification and regression, which yields a good trade-off
between robustness (low variance) and adaptiveness (low bias). Direct
interpretation of a RF model is difficult, as the explicit ensemble model of
hundreds of deep trees is complex. Nonetheless, it is possible to visualize a
RF model fit by its mapping from feature space to prediction space. Hereby the
user is first presented with the overall geometrical shape of the model
structure, and when needed one can zoom in on local details. Dimensional
reduction by projection is used to visualize high dimensional shapes. The
traditional method to visualize RF model structure, partial dependence plots,
achieve this by averaging multiple parallel projections. We suggest to first
use feature contributions, a method to decompose trees by splitting features,
and then subsequently perform projections. The advantages of forest floor over
partial dependence plots is that interactions are not masked by averaging. As a
consequence, it is possible to locate interactions, which are not visualized in
a given projection. Furthermore, we introduce: a goodness-of-visualization
measure, use of colour gradients to identify interactions and an out-of-bag
cross validated variant of feature contributions.
On the complexity of switching linear regression
Authors: Fabien Lauer
Date: 2015-10-23 00:00:00
Tasks: regression

This technical note extends recent results on the computational complexity of
globally minimizing the error of piecewise-affine models to the related problem
of minimizing the error of switching linear regression models. In particular,
we show that, on the one hand the problem is NP-hard, but on the other hand, it
admits a polynomial-time algorithm with respect to the number of data points
for any fixed data dimension and number of modes.
Understanding the Abstract Dialectical Framework (Preliminary Report)
Authors: Sylwia Polberg
Date: 2016-07-04 00:00:00
Tasks: Relation

Among the most general structures extending the framework by Dung are the
abstract dialectical frameworks (ADFs). They come equipped with various types
of semantics, with the most prominent - the labeling-based one - analyzed in
the context of computational complexity, signatures, instantiations and
software support. This makes the abstract dialectical frameworks valuable tools
for argumentation. However, there are fewer results available concerning the
relation between the ADFs and other argumentation frameworks. In this paper we
would like to address this issue by introducing a number of translations from
various formalisms into ADFs. The results of our study show the similarities
and differences between them, thus promoting the use and understanding of ADFs.
Moreover, our analysis also proves their capability to model many of the
existing frameworks, including those that go beyond the attack relation.
Finally, translations allow other structures to benefit from the research on
ADFs in general and from the existing software in particular.
Formal analysis of HTM Spatial Pooler performance under predefined operation conditions
Authors: M. Pietron, M. Wielgosz, K. Wiatr
Date: 2016-07-04 00:00:00

This paper introduces mathematical formalism for Spatial (SP) of Hierarchical
Temporal Memory (HTM) with a spacial consideration for its hardware
implementation. Performance of HTM network and its ability to learn and adjust
to a problem at hand is governed by a large set of parameters. Most of
parameters are codependent which makes creating efficient HTM-based solutions
challenging. It requires profound knowledge of the settings and their impact on
the performance of system. Consequently, this paper introduced a set of
formulas which are to facilitate the design process by enhancing tedious
trial-and-error method with a tool for choosing initial parameters which enable
quick learning convergence. This is especially important in hardware
implementations which are constrained by the limited resources of a platform.
The authors focused especially on a formalism of Spatial Pooler and derive at
the formulas for quality and convergence of the model. This may be considered
as recipes for designing efficient HTM models for given input patterns.
Learning Shapes by Convex Composition
Authors: Alireza Aghasi, Justin Romberg
Date: 2016-02-23 00:00:00

We present a mathematical and algorithmic scheme for learning the principal
geometric elements in an image or 3D object. We build on recent work that
convexifies the basic problem of finding a combination of a small number shapes
that overlap and occlude one another in such a way that they "match" a given
scene as closely as possible. This paper derives general sufficient conditions
under which this convex shape composition identifies a target composition. From
a computational standpoint, we present two different methods for solving the
associated optimization programs. The first method simply recasts the problem
as a linear program, while the second uses the alternating direction method of
multipliers with a series of easily computed proximal operators.
Multi-Objective Design of State Feedback Controllers Using Reinforced Quantum-Behaved Particle Swarm Optimization
Authors: Kaveh Hassani, Won-Sook Lee
Date: 2016-07-04 00:00:00

In this paper, a novel and generic multi-objective design paradigm is
proposed which utilizes quantum-behaved PSO(QPSO) for deciding the optimal
configuration of the LQR controller for a given problem considering a set of
competing objectives. There are three main contributions introduced in this
paper as follows. (1) The standard QPSO algorithm is reinforced with an
informed initialization scheme based on the simulated annealing algorithm and
Gaussian neighborhood selection mechanism. (2) It is also augmented with a
local search strategy which integrates the advantages of memetic algorithm into
conventional QPSO. (3) An aggregated dynamic weighting criterion is introduced
that dynamically combines the soft and hard constraints with control objectives
to provide the designer with a set of Pareto optimal solutions and lets her to
decide the target solution based on practical preferences. The proposed method
is compared against a gradient-based method, seven meta-heuristics, and the
trial-and-error method on two control benchmarks using sensitivity analysis and
full factorial parameter selection and the results are validated using
one-tailed T-test. The experimental results suggest that the proposed method
outperforms opponent methods in terms of controller effort, measures associated
with transient response and criteria related to steady-state.
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
Authors: Miles E. Lopes
Date: 2016-07-04 00:00:00
Proceeding: NeurIPS 2014
Tasks: regression

We study the residual bootstrap (RB) method in the context of
high-dimensional linear regression. Specifically, we analyze the distributional
approximation of linear contrasts $c^{\top} (\hat{\beta}_{\rho}-\beta)$, where
$\hat{\beta}_{\rho}$ is a ridge-regression estimator. When regression
coefficients are estimated via least squares, classical results show that RB
consistently approximates the laws of contrasts, provided that $p\ll n$, where
the design matrix is of size $n\times p$. Up to now, relatively little work has
considered how additional structure in the linear model may extend the validity
of RB to the setting where $p/n\asymp 1$. In this setting, we propose a version
of RB that resamples residuals obtained from ridge regression. Our main
structural assumption on the design matrix is that it is nearly low rank --- in
the sense that its singular values decay according to a power-law profile.
Under a few extra technical assumptions, we derive a simple criterion for
ensuring that RB consistently approximates the law of a given contrast. We then
specialize this result to study confidence intervals for mean response values
$X_i^{\top} \beta$, where $X_i^{\top}$ is the $i$th row of the design. More
precisely, we show that conditionally on a Gaussian design with near low-rank
structure, RB simultaneously approximates all of the laws
$X_i^{\top}(\hat{\beta}_{\rho}-\beta)$, $i=1,\dots,n$. This result is also
notable as it imposes no sparsity assumptions on $\beta$. Furthermore, since
our consistency results are formulated in terms of the Mallows (Kantorovich)
metric, the existence of a limiting distribution is not required.