pwc-archive/papers-with-abstracts huggingface dataset:
Combining Answer Set Programming and POMDPs for Knowledge Representation and Reasoning on Mobile Robots
Authors: Shiqi Zhang, Mohan Sridharan
Date: 2013-07-29 00:00:00

For widespread deployment in domains characterized by partial observability,
non-deterministic actions and unforeseen changes, robots need to adapt sensing,
processing and interaction with humans to the tasks at hand. While robots
typically cannot process all sensor inputs or operate without substantial
domain knowledge, it is a challenge to provide accurate domain knowledge and
humans may not have the time and expertise to provide elaborate and accurate
feedback. The architecture described in this paper combines declarative
programming and probabilistic reasoning to address these challenges, enabling
robots to: (a) represent and reason with incomplete domain knowledge, resolving
ambiguities and revising existing knowledge using sensor inputs and minimal
human feedback; and (b) probabilistically model the uncertainty in sensor input
processing and navigation. Specifically, Answer Set Programming (ASP), a
declarative programming paradigm, is combined with hierarchical partially
observable Markov decision processes (POMDPs), using domain knowledge to revise
probabilistic beliefs, and using positive and negative observations for early
termination of tasks that can no longer be pursued. All algorithms are
evaluated in simulation and on mobile robots locating target objects in indoor
domains.
Tight Lower Bounds for Homology Inference
Authors: Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
Date: 2013-07-29 00:00:00
Tasks: LEMMA

The homology groups of a manifold are important topological invariants that
provide an algebraic summary of the manifold. These groups contain rich
topological information, for instance, about the connected components, holes,
tunnels and sometimes the dimension of the manifold. In earlier work, we have
considered the statistical problem of estimating the homology of a manifold
from noiseless samples and from noisy samples under several different noise
models. We derived upper and lower bounds on the minimax risk for this problem.
In this note we revisit the noiseless case. In previous work we used Le Cam's
lemma to establish a lower bound that differed from the upper bound of Niyogi,
Smale and Weinberger by a polynomial factor in the condition number.
  In this note we use a different construction based on the direct analysis of
the likelihood ratio test to show that the upper bound of Niyogi, Smale and
Weinberger is in fact tight, thus establishing rate optimal asymptotic minimax
bounds for the problem. The techniques we use here extend in a straightforward
way to the noisy settings considered in our earlier work.
ReAct! An Interactive Tool for Hybrid Planning in Robotics
Authors: Zeynep Dogmus, Esra Erdem, Volkan Patoglu
Date: 2013-07-29 00:00:00

We present ReAct!, an interactive tool for high-level reasoning for cognitive
robotic applications. ReAct! enables robotic researchers to describe robots'
actions and change in dynamic domains, without having to know about the
syntactic and semantic details of the underlying formalism in advance, and
solve planning problems using state-of-the-art automated reasoners, without
having to learn about their input/output language or usage. In particular,
ReAct! can be used to represent sophisticated dynamic domains that feature
concurrency, indirect effects of actions, and state/transition constraints. It
allows for embedding externally defined calculations (e.g., checking for
collision-free continuous trajectories) into representations of hybrid domains
that require a tight integration of (discrete) high-level reasoning with
(continuous) geometric reasoning. ReAct! also enables users to solve planning
problems that involve complex goals. Such variety of utilities are useful for
robotic researchers to work on interesting and challenging domains, ranging
from service robotics to cognitive factories. ReAct! provides sample
formalizations of some action domains (e.g., multi-agent path planning, Tower
of Hanoi), as well as dynamic simulations of plans computed by a
state-of-the-art automated reasoner (e.g., a SAT solver or an ASP solver).
Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle
Authors: R. Subash Chandra Boss, K. Thangavel, D. Arul Pon Daniel
Date: 2013-07-29 00:00:00

Currently Mammography is a most effective imaging modality used by
radiologists for the screening of breast cancer. Finding an accurate, robust
and efficient breast region segmentation technique still remains a challenging
problem in digital mammography. Extraction of the breast profile region and the
removal of pectoral muscle are essential pre-processing steps in Computer Aided
Diagnosis (CAD) system for the diagnosis of breast cancer. Primarily it allows
the search for abnormalities to be limited to the region of the breast tissue
without undue influence from the background of the mammogram. The presence of
pectoral muscle in mammograms biases detection procedures, which recommends
removing the pectoral muscle during mammogram image pre-processing. The
presence of pectoral muscle in mammograms may disturb or influence the
detection of breast cancer as the pectoral muscle and mammographic parenchymas
appear similar. The goal of breast region extraction is reducing the image size
without losing anatomic information, it improve the accuracy of the overall CAD
system. The main objective of this study is to propose an automated method to
identify the pectoral muscle in Medio-Lateral Oblique (MLO) view mammograms. In
this paper, we proposed histogram based 8-neighborhood connected component
labelling method for breast region extraction and removal of pectoral muscle.
The proposed method is evaluated by using the mean values of accuracy and
error. The comparative analysis shows that the proposed method identifies the
breast region more accurately.
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report
Authors: Damien Jade Duff, Esra Erdem, Volkan Patoglu
Date: 2013-07-29 00:00:00
Tasks: 3D Object Recognition, Object Recognition

We investigate different approaches to integrating object recognition and
planning in a tabletop manipulation domain with the set of objects used in the
2012 RoboCup@Work competition. Results of our preliminary experiments show
that, with some approaches, close integration of perception and planning
improves the quality of plans, as well as the computation times of feasible
plans.
Levels of Integration between Low-Level Reasoning and Task Planning
Authors: Esra Erdem, Volkan Patoglu, Peter Schüller
Date: 2013-07-29 00:00:00
Tasks: Computational Efficiency, Task Planning

We provide a systematic analysis of levels of integration between discrete
high-level reasoning and continuous low-level reasoning to address hybrid
planning problems in robotics. We identify four distinct strategies for such an
integration: (i) low-level checks are done for all possible cases in advance
and then this information is used during plan generation, (ii) low-level checks
are done exactly when they are needed during the search for a plan, (iii) first
all plans are computed and then infeasible ones are filtered, and (iv) by means
of replanning, after finding a plan, low-level checks identify whether it is
infeasible or not; if it is infeasible, a new plan is computed considering the
results of previous low- level checks. We perform experiments on hybrid
planning problems in robotic manipulation and legged locomotion domains
considering these four methods of integration, as well as some of their
combinations. We analyze the usefulness of levels of integration in these
domains, both from the point of view of computational efficiency (in time and
space) and from the point of view of plan quality relative to its feasibility.
We discuss advantages and disadvantages of each strategy in the light of
experimental results and provide some guidelines on choosing proper strategies
for a given domain.
ParceLiNGAM: A causal ordering method robust against latent confounders
Authors: Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen, Takashi Washio
Date: 2013-03-29 00:00:00

We consider learning a causal ordering of variables in a linear non-Gaussian
acyclic model called LiNGAM. Several existing methods have been shown to
consistently estimate a causal ordering assuming that all the model assumptions
are correct. But, the estimation results could be distorted if some assumptions
actually are violated. In this paper, we propose a new algorithm for learning
causal orders that is robust against one typical violation of the model
assumptions: latent confounders. The key idea is to detect latent confounders
by testing independence between estimated external influences and find subsets
(parcels) that include variables that are not affected by latent confounders.
We demonstrate the effectiveness of our method using artificial data and
simulated brain imaging data.
Borel Isomorphic Dimensionality Reduction of Data and Supervised Learning
Authors: Stan Hatko
Date: 2013-07-29 00:00:00
Tasks: Dimensionality Reduction

In this project we further investigate the idea of reducing the
dimensionality of datasets using a Borel isomorphism with the purpose of
subsequently applying supervised learning algorithms, as originally suggested
by my supervisor V. Pestov (in 2011 Dagstuhl preprint). Any consistent learning
algorithm, for example kNN, retains universal consistency after a Borel
isomorphism is applied. A series of concrete examples of Borel isomorphisms
that reduce the number of dimensions in a dataset is provided, based on
multiplying the data by orthogonal matrices before the dimensionality reducing
Borel isomorphism is applied. We test the accuracy of the resulting classifier
in a lower dimensional space with various data sets. Working with a phoneme
voice recognition dataset, of dimension 256 with 5 classes (phonemes), we show
that a Borel isomorphic reduction to dimension 16 leads to a minimal drop in
accuracy. In conclusion, we discuss further prospects of the method.
A new approach in dynamic traveling salesman problem: a hybrid of ant colony optimization and descending gradient
Authors: Farhad Soleimanian Gharehchopogh, Isa Maleki, Seyyed Reza Khaze
Date: 2013-07-29 00:00:00
Tasks: Traveling Salesman Problem

Nowadays swarm intelligence-based algorithms are being used widely to
optimize the dynamic traveling salesman problem (DTSP). In this paper, we have
used mixed method of Ant Colony Optimization (AOC)and gradient descent to
optimize DTSP which differs with ACO algorithm in evaporation rate and
innovative data. This approach prevents premature convergence and scape from
local optimum spots and also makes it possible to find better solutions for
algorithm. In this paper, we are going to offer gradient descent and ACO
algorithm which in comparison to some former methods it shows that algorithm
has significantly improved routes optimization.
Data mining application for cyber space users tendency in blog writing: a case study
Authors: Farhad Soleimanian Gharehchopogh, Seyyed Reza Khaze
Date: 2013-07-29 00:00:00

Blogs are the recent emerging media which relies on information technology
and technological advance. Since the mass media in some less-developed and
developing countries are in government service and their policies are developed
based on governmental interests, so blogs are provided for ideas and exchanging
opinions. In this paper, we highlighted performed simulations from obtained
information from 100 users and bloggers in Kohkiloye and Boyer Ahmad Province
and using Weka 3.6 tool and c4.5 algorithm by applying decision tree with more
than %82 precision for getting future tendency anticipation of users to
blogging and using in strategically areas.
Participation anticipating in elections using data mining methods
Authors: Amin Babazadeh Sangar, Seyyed Reza Khaze, Laya Ebrahimi
Date: 2013-07-29 00:00:00
Tasks: General Classification

Anticipating the political behavior of people will be considerable help for
election candidates to assess the possibility of their success and to be
acknowledged about the public motivations to select them. In this paper, we
provide a general schematic of the architecture of participation anticipating
system in presidential election by using KNN, Classification Tree and Na\"ive
Bayes and tools orange based on crisp which had hopeful output. To test and
assess the proposed model, we begin to use the case study by selecting 100
qualified persons who attend in 11th presidential election of Islamic republic
of Iran and anticipate their participation in Kohkiloye & Boyerahmad. We
indicate that KNN can perform anticipation and classification processes with
high accuracy in compared with two other algorithms to anticipate
participation.
Accelerated Time-of-Flight Mass Spectrometry
Authors: Morteza Ibrahimi, Andrea Montanari, George S Moore
Date: 2012-12-18 00:00:00

We study a simple modification to the conventional time of flight mass
spectrometry (TOFMS) where a \emph{variable} and (pseudo)-\emph{random} pulsing
rate is used which allows for traces from different pulses to overlap. This
modification requires little alteration to the currently employed hardware.
However, it requires a reconstruction method to recover the spectrum from
highly aliased traces. We propose and demonstrate an efficient algorithm that
can process massive TOFMS data using computational resources that can be
considered modest with today's standards. This approach can be used to improve
duty cycle, speed, and mass resolving power of TOFMS at the same time. We
expect this to extend the applicability of TOFMS to new domains.
Reasoning for Moving Blocks Problem: Formal Representation and Implementation
Authors: P. A. Wałȩga
Date: 2013-07-28 00:00:00

The combined approach of the Qualitative Reasoning and Probabilistic
Functions for the knowledge representation is proposed. The method aims at
represent uncertain, qualitative knowledge that is essential for the moving
blocks task's execution. The attempt to formalize the commonsense knowledge is
performed with the Situation Calculus language for reasoning and robot's
beliefs representation. The method is implemented in the Prolog programming
language and tested for a specific simulated scenario. In most cases the
implementation enables us to solve a given task, i.e., move blocks to desired
positions. The example of robot's reasoning and main parts of the implemented
program's code are presented.
ROSoClingo: A ROS package for ASP-based robot control
Authors: Benjamin Andres, Philipp Obermeier, Orkunt Sabuncu, Torsten Schaub, David Rajaratnam
Date: 2013-07-28 00:00:00

Knowledge representation and reasoning capacities are vital to cognitive
robotics because they provide higher level cognitive functions for reasoning
about actions, environments, goals, perception, etc. Although Answer Set
Programming (ASP) is well suited for modelling such functions, there was so far
no seamless way to use ASP in a robotic environment. We address this
shortcoming and show how a recently developed reactive ASP system can be
harnessed to provide appropriate reasoning capacities within a robotic system.
To be more precise, we furnish a package integrating the reactive ASP solver
oClingo with the popular open-source robotic middleware ROS. The resulting
system, ROSoClingo, provides a generic way by which an ASP program can be used
to control the behaviour of a robot and to respond to the results of the
robot's actions.
Learning Frames from Text with an Unsupervised Latent Variable Model
Authors: Brendan O'Connor
Date: 2013-07-28 00:00:00

We develop a probabilistic latent-variable model to discover semantic
frames---types of events and their participants---from corpora. We present a
Dirichlet-multinomial model in which frames are latent categories that explain
the linking of verb-subject-object triples, given document-level sparsity. We
analyze what the model learns, and compare it to FrameNet, noting it learns
some novel and interesting frames. This document also contains a discussion of
inference issues, including concentration parameter learning; and a small-scale
error analysis of syntactic parsing accuracy.
Learning to Understand by Evolving Theories
Authors: Martin E. Mueller, Madhura D. Thosar
Date: 2013-07-27 00:00:00

In this paper, we describe an approach that enables an autonomous system to
infer the semantics of a command (i.e. a symbol sequence representing an
action) in terms of the relations between changes in the observations and the
action instances. We present a method of how to induce a theory (i.e. a
semantic description) of the meaning of a command in terms of a minimal set of
background knowledge. The only thing we have is a sequence of observations from
which we extract what kinds of effects were caused by performing the command.
This way, we yield a description of the semantics of the action and, hence, a
definition.
Counterfactual Reasoning and Learning Systems
Authors: Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
Date: 2012-09-11 00:00:00
Tasks: Causal Inference, counterfactual, Counterfactual Reasoning

This work shows how to leverage causal inference to understand the behavior
of complex learning systems interacting with their environment and predict the
consequences of changes to the system. Such predictions allow both humans and
algorithms to select changes that improve both the short-term and long-term
performance of such systems. This work is illustrated by experiments carried
out on the ad placement system associated with the Bing search engine.
A Review of Machine Learning based Anomaly Detection Techniques
Authors: Harjinder Kaur, Gurpreet Singh, Jaspreet Minhas
Date: 2013-07-27 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Intrusion Detection

Intrusion detection is so much popular since the last two decades where
intrusion is attempted to break into or misuse the system. It is mainly of two
types based on the intrusions, first is Misuse or signature based detection and
the other is Anomaly detection. In this paper Machine learning based methods
which are one of the types of Anomaly detection techniques is discussed.
Self-Learning for Player Localization in Sports Video
Authors: Kenji Okuma, David G. Lowe, James J. Little
Date: 2013-07-27 00:00:00
Tasks: Self-Learning

This paper introduces a novel self-learning framework that automates the
label acquisition process for improving models for detecting players in
broadcast footage of sports games. Unlike most previous self-learning
approaches for improving appearance-based object detectors from videos, we
allow an unknown, unconstrained number of target objects in a more generalized
video sequence with non-static camera views. Our self-learning approach uses a
latent SVM learning algorithm and deformable part models to represent the shape
and colour information of players, constraining their motions, and learns the
colour of the playing field by a gentle Adaboost algorithm. We combine those
image cues and discover additional labels automatically from unlabelled data.
In our experiments, our approach exploits both labelled and unlabelled data in
sparsely labelled videos of sports games, providing a mean performance
improvement of over 20% in the average precision for detecting sports players
and improved tracking, when videos contain very few labelled images.
MixedGrad: An O(1/T) Convergence Rate Algorithm for Stochastic Smooth Optimization
Authors: Mehrdad Mahdavi, Rong Jin
Date: 2013-07-26 00:00:00
Tasks: Stochastic Optimization

It is well known that the optimal convergence rate for stochastic
optimization of smooth functions is $O(1/\sqrt{T})$, which is same as
stochastic optimization of Lipschitz continuous convex functions. This is in
contrast to optimizing smooth functions using full gradients, which yields a
convergence rate of $O(1/T^2)$. In this work, we consider a new setup for
optimizing smooth functions, termed as {\bf Mixed Optimization}, which allows
to access both a stochastic oracle and a full gradient oracle. Our goal is to
significantly improve the convergence rate of stochastic optimization of smooth
functions by having an additional small number of accesses to the full gradient
oracle. We show that, with an $O(\ln T)$ calls to the full gradient oracle and
an $O(T)$ calls to the stochastic oracle, the proposed mixed optimization
algorithm is able to achieve an optimization error of $O(1/T)$.
An Architecture for Autonomously Controlling Robot with Embodiment in Real World
Authors: Megumi Fujita, Yuki Goto, Naoyuki Nide, Ken Satoh, Hiroshi Hosobe
Date: 2013-07-26 00:00:00

In the real world, robots with embodiment face various issues such as dynamic
continuous changes of the environment and input/output disturbances. The key to
solving these issues can be found in daily life; people `do actions associated
with sensing' and `dynamically change their plans when necessary'. We propose
the use of a new concept, enabling robots to do these two things, for
autonomously controlling mobile robots. We implemented our concept to make two
experiments under static/dynamic environments. The results of these experiments
show that our idea provides a way to adapt to dynamic changes of the
environment in the real world.
Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology
Authors: Piyush Ahuja
Date: 2013-07-26 00:00:00

Artificial Intelligence began as a field probing some of the most fundamental
questions of science - the nature of intelligence and the design of intelligent
artifacts. But it has grown into a discipline that is deeply entwined with
commerce and society. Today's AI technology, such as expert systems and
intelligent assistants, pose some difficult questions of risk, trust and
accountability. In this paper, we present these concerns, examining them in the
context of historical developments that have shaped the nature and direction of
AI research. We also suggest the exploration and further development of two
paradigms, human intelligence-machine cooperation, and a sociological view of
intelligence, which might help address some of these concerns.