pwc-archive/papers-with-abstracts huggingface dataset:
AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents
Authors: Hrishikesh Ganu, Mithun Ghosh, Shashi Roshan
Date: 2019-02-24 00:00:00

In this short paper, we present early insights from a Decision Support System
for Customer Support Agents (CSAs) serving customers of a leading accounting
software. The system is under development and is designed to provide
suggestions to CSAs to make them more productive. A unique aspect of the
solution is the use of bandit algorithms to create a tractable
human-in-the-loop system that can learn from CSAs in an online fashion. In
addition to discussing the ML aspects, we also bring out important insights we
gleaned from early feedback from CSAs. These insights motivate our future work
and also might be of wider interest to ML practitioners.
Nowcasting Recessions using the SVM Machine Learning Algorithm
Authors: Alexander James, Yaser S. Abu-Mostafa, Xiao Qiao
Date: 2019-02-17 00:00:00
Tasks: BIG-bench Machine Learning

We introduce a novel application of Support Vector Machines (SVM), an important Machine Learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, "forecasting" a condition about the present time because the full information about it is not available until later, is key for recessions, which are only determined months after the fact. We show that SVM has excellent predictive performance for this task, and we provide implementation details to facilitate its use in similar problems in economics and finance.
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles
Authors: Thiago Freitas dos Santos, Paulo E. Santos, Leonardo A. Ferreira, Reinaldo A. C. Bianchi, Pedro Cabalar
Date: 2019-02-16 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Spatial puzzles composed of rigid objects, flexible strings and holes offer
interesting domains for reasoning about spatial entities that are common in the
human daily-life's activities. The goal of this work is to investigate the
automated solution of this kind of puzzles adapting an algorithm that combines
Answer Set Programming (ASP) with Markov Decision Process (MDP), algorithm
oASP(MDP), to use heuristics accelerating the learning process. ASP is applied
to represent the domain as an MDP, while a Reinforcement Learning algorithm
(Q-Learning) is used to find the optimal policies. In this work, the heuristics
were obtained from the solution of relaxed versions of the puzzles. Experiments
were performed on deterministic, non-deterministic and non-stationary versions
of the puzzles. Results show that the proposed approach can accelerate the
learning process, presenting an advantage when compared to the non-heuristic
versions of oASP(MDP) and Q-Learning.
The Ethics of AI Ethics -- An Evaluation of Guidelines
Authors: Thilo Hagendorff
Date: 2019-02-28 00:00:00
Tasks: Ethics

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.
Automatic cough detection based on airflow signals for portable spirometry system
Authors: Mateusz Soliński, Michał Łepek, Łukasz Kołtowski
Date: 2019-02-26 00:00:00
Tasks: General Classification, Specificity

We give a short introduction to cough detection efforts that were undertaken during the last decade and we describe the solution for automatic cough detection developed for the AioCare portable spirometry system. In contrast to more popular analysis of sound and audio recordings, we fully based our approach on airflow signals only. As the system is intended to be used in a large variety of environments and different patients, we trained and validated the algorithm using AioCare-collected data and the large database of spirometry curves from the NHANES database by the American National Center for Health Statistics. We trained different classifiers, such as logistic regression, feed-forward artificial neural network, support vector machine, and random forest to choose the one with the best performance. The ANN solution was selected as the final classifier. The classification results on the test set (AioCare data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) and 0.88 (F1 score). The classification methodology developed in this study is robust for detecting cough events during spirometry measurements. As far as we know, the solution presented in this work is the first fully reproducible description of the automatic cough detection algorithm based totally on airflow signals and the first cough detection implemented in a commercial spirometry system that is to be published.
How Machine (Deep) Learning Helps Us Understand Human Learning: the Value of Big Ideas
Authors: Marc Maliar
Date: 2019-02-16 00:00:00
Tasks: Handwritten Digit Recognition

I use simulation of two multilayer neural networks to gain intuition into the
determinants of human learning. The first network, the teacher, is trained to
achieve a high accuracy in handwritten digit recognition. The second network,
the student, learns to reproduce the output of the first network. I show that
learning from the teacher is more effective than learning from the data under
the appropriate degree of regularization. Regularization allows the teacher to
distinguish the trends and to deliver "big ideas" to the student. I also model
other learning situations such as expert and novice teachers, high- and
low-ability students and biased learning experience due to, e.g., poverty and
trauma. The results from computer simulation accord remarkably well with
finding of the modern psychological literature. The code is written in MATLAB
and will be publicly available from the author's web page.
Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines
Authors: Jinyu Yang, Bo Zhang
Date: 2019-02-26 00:00:00

This study provides a systematic review of the recent advances in designing
the intelligent tutoring robot (ITR), and summarises the status quo of applying
artificial intelligence (AI) techniques. We first analyse the environment of
the ITR and propose a relationship model for describing interactions of ITR
with the students, the social milieu and the curriculum. Then, we transform the
relationship model into the perception-planning-action model for exploring what
AI techniques are suitable to be applied in the ITR. This article provides
insights on promoting human-robot teaching-learning process and AI-assisted
educational techniques, illustrating the design guidelines and future research
perspectives in intelligent tutoring robots.
The Homunculus Brain and Categorical Logic
Authors: Michael Heller
Date: 2019-02-28 00:00:00

The interaction between syntax (formal language) and its semantics (meanings of language) is one which has been well studied in categorical logic. The results of this particular study are employed to understand how the brain is able to create meanings. To emphasize the toy character of the proposed model, we prefer to speak of the homunculus brain rather than the brain per se. The homunculus brain consists of neurons, each of which is modeled by a category, and axons between neurons, which are modeled by functors between the corresponding neuron-categories. Each neuron (category) has its own program enabling its working, i.e. a theory of this neuron. In analogy to what is known from categorical logic, we postulate the existence of a pair of adjoint functors, called Lang and Syn, from a category, now called BRAIN, of categories, to a category, now called MIND, of theories. Our homunculus is a kind of ``mathematical robot'', the neuronal architecture of which is not important. Its only aim is to provide us with the opportunity to study how such a simple brain-like structure could ``create meanings'' and perform abstraction operations out of its purely syntactic program. The pair of adjoint functors Lang and Syn model the mutual dependencies between the syntactical structure of a given theory of MIND and the internal logic of its semantics given by a category of BRAIN. In this way, a formal language (syntax) and its meanings (semantics) are interwoven with each other in a manner corresponding to the adjointness of the functors Lang and Syn. Higher cognitive functions of abstraction and realization of concepts are also modelled by a corresponding pair of adjoint functors. The categories BRAIN and MIND interact with each other with their entire structures and, at the same time, these very structures are shaped by this interaction.
Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
Authors: Krzysztof Czarnecki, Rick Salay
Date: 2019-03-03 00:00:00
Tasks: Autonomous Vehicles, BIG-bench Machine Learning, Position

Perception is a safety-critical function of autonomous vehicles and machine
learning (ML) plays a key role in its implementation. This position paper
identifies (1) perceptual uncertainty as a performance measure used to define
safety requirements and (2) its influence factors when using supervised ML.
This work is a first step towards a framework for measuring and controling the
effects of these factors and supplying evidence to support claims about
perceptual uncertainty.
Egocentric Bias and Doubt in Cognitive Agents
Authors: Nanda Kishore Sreenivas, Shrisha Rao
Date: 2019-03-01 00:00:00

Modeling social interactions based on individual behavior has always been an
area of interest, but prior literature generally presumes rational behavior.
Thus, such models may miss out on capturing the effects of biases humans are
susceptible to. This work presents a method to model egocentric bias, the
real-life tendency to emphasize one's own opinion heavily when presented with
multiple opinions. We use a symmetric distribution centered at an agent's own
opinion, as opposed to the Bounded Confidence (BC) model used in prior work. We
consider a game of iterated interactions where an agent cooperates based on its
opinion about an opponent. Our model also includes the concept of domain-based
self-doubt, which varies as the interaction succeeds or not. An increase in
doubt makes an agent reduce its egocentricity in subsequent interactions, thus
enabling the agent to learn reactively. The agent system is modeled with
factions not having a single leader, to overcome some of the issues associated
with leader-follower factions. We find that agents belonging to factions
perform better than individual agents. We observe that an intermediate level of
egocentricity helps the agent perform at its best, which concurs with
conventional wisdom that neither overconfidence nor low self-esteem brings
benefits.
Learning $\textit{Ex Nihilo}$
Authors: Selmer Bringsjord, Naveen Sundar Govindarajulu
Date: 2019-03-04 00:00:00

This paper introduces, philosophically and to a degree formally, the novel
concept of learning $\textit{ex nihilo}$, intended (obviously) to be analogous
to the concept of creation $\textit{ex nihilo}$. Learning $\textit{ex nihilo}$
is an agent's learning "from nothing," by the suitable employment of schemata
for deductive and inductive reasoning. This reasoning must be in
machine-verifiable accord with a formal proof/argument theory in a
$\textit{cognitive calculus}$ (i.e., roughly, an intensional higher-order
multi-operator quantified logic), and this reasoning is applied to percepts
received by the agent, in the context of both some prior knowledge, and some
prior and current interests. Learning $\textit{ex nihilo}$ is a challenge to
contemporary forms of ML, indeed a severe one, but the challenge is offered in
the spirt of seeking to stimulate attempts, on the part of non-logicist ML
researchers and engineers, to collaborate with those in possession of
learning-$\textit{ex nihilo}$ frameworks, and eventually attempts to integrate
directly with such frameworks at the implementation level. Such integration
will require, among other things, the symbiotic interoperation of
state-of-the-art automated reasoners and high-expressivity planners, with
statistical/connectionist ML technology.
A study of problems with multiple interdependent components - Part I
Authors: Mohamed El Yafrani
Date: 2019-03-01 00:00:00
Tasks: Management

Recognising that real-world optimisation problems have multiple
interdependent components can be quite easy. However, providing a generic and
formal model for dependencies between components can be a tricky task. In fact,
a PMIC can be considered simply as a single optimisation problem and the
dependencies between components could be investigated by studying the
decomposability of the problem and the correlations between the sub-problems.
In this work, we attempt to define PMICs by reasoning from a reverse
perspective. Instead of considering a decomposable problem, we model multiple
problems (the components) and define how these components could be connected.
In this document, we introduce notions related to problems with mutliple
interndependent components. We start by introducing realistic examples from
logistics and supply chain management to illustrate the composite nature and
dependencies in these problems. Afterwards, we provide our attempt to formalise
and classify dependency in multi-component problems.
Challenges for an Ontology of Artificial Intelligence
Authors: Scott H. Hawley
Date: 2019-02-25 00:00:00
Tasks: Philosophy

Of primary importance in formulating a response to the increasing prevalence
and power of artificial intelligence (AI) applications in society are questions
of ontology. Questions such as: What "are" these systems? How are they to be
regarded? How does an algorithm come to be regarded as an agent? We discuss
three factors which hinder discussion and obscure attempts to form a clear
ontology of AI: (1) the various and evolving definitions of AI, (2) the
tendency for pre-existing technologies to be assimilated and regarded as
"normal," and (3) the tendency of human beings to anthropomorphize. This list
is not intended as exhaustive, nor is it seen to preclude entirely a clear
ontology, however, these challenges are a necessary set of topics for
consideration. Each of these factors is seen to present a 'moving target' for
discussion, which poses a challenge for both technical specialists and
non-practitioners of AI systems development (e.g., philosophers and
theologians) to speak meaningfully given that the corpus of AI structures and
capabilities evolves at a rapid pace. Finally, we present avenues for moving
forward, including opportunities for collaborative synthesis for scholars in
philosophy and science.
The meta-problem and the transfer of knowledge between theories of consciousness: a software engineer's take
Authors: Marcel Kvassay
Date: 2019-02-18 00:00:00

This contribution examines two radically different explanations of our
phenomenal intuitions, one reductive and one strongly non-reductive, and
identifies two germane ideas that could benefit many other theories of
consciousness. Firstly, the ability of sophisticated agent architectures with a
purely physical implementation to support certain functional forms of qualia or
proto-qualia appears to entail the possibility of machine consciousness with
qualia, not only for reductive theories but also for the nonreductive ones that
regard consciousness as ubiquitous in Nature. Secondly, analysis of
introspective psychological material seems to hint that, under the threshold of
our ordinary waking awareness, there exist further 'submerged' or 'subliminal'
layers of consciousness which constitute a hidden foundation and support and
another source of our phenomenal intuitions. These 'submerged' layers might
help explain certain puzzling phenomena concerning subliminal perception, such
as the apparently 'unconscious' multisensory integration and learning of
subliminal stimuli.
Genetic Algorithm for a class of Knapsack Problems
Authors: Shalin Shah
Date: 2019-02-15 00:00:00

The 0/1 knapsack problem is weakly NP-hard in that there exist
pseudo-polynomial time algorithms based on dynamic programming that can solve
it exactly. There are also the core branch and bound algorithms that can solve
large randomly generated instances in a very short amount of time. However, as
the correlation between the variables is increased, the difficulty of the
problem increases. Recently a new class of knapsack problems was introduced by
D. Pisinger called the spanner knapsack instances. These instances are
unsolvable by the core branch and bound instances; and as the size of the
coefficients and the capacity constraint increase, the spanner instances are
unsolvable even by dynamic programming based algorithms. In this paper, a
genetic algorithm is presented for spanner knapsack instances. Results show
that the algorithm is capable of delivering optimum solutions within a
reasonable amount of computational duration.
GPU Kernels for Block-Sparse Weights
Authors: Scott Gray, Alec Radford and Diederik P. Kingma
Date: 2017-12-01 00:00:00
Proceeding: OpenAi 2017 12
Tasks: GPU, Sentiment Analysis

We’re releasing highly optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. The kernels allow for efficient evaluation and differentiation of linear layers, including convolutional layers, with flexibly configurable block-sparsity patterns in the weight matrix. We find that depending on the sparsity, these kernels can run orders of magnitude faster than the best available alternatives such as cuBLAS. Using the kernels we improve upon the state-of-the-art in text sentiment analysis and generative modeling of text and images. By releasing our kernels in the open we aim to spur further
advancement in model and algorithm design.
Natural Questions: a Benchmark for Question Answering Research
Authors: Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov
Date: 2019-06-01 00:00:00
Proceeding: Transactions of the Association of Computational Linguistics 2019 6
Tasks: Natural Questions, Question Answering

We present the Natural Questions corpus, a question answering dataset. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations, 7,830 examples with 5-way annotations for development data, and a further 7,842 examples 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.
Feature selection in omics prediction problems using cat scores and false nondiscovery rate control
Authors: Miika Ahdesmäki, Korbinian Strimmer
Date: 2009-03-11 00:00:00
Tasks: feature selection

We revisit the problem of feature selection in linear discriminant analysis
(LDA), that is, when features are correlated. First, we introduce a pooled
centroids formulation of the multiclass LDA predictor function, in which the
relative weights of Mahalanobis-transformed predictors are given by
correlation-adjusted $t$-scores (cat scores). Second, for feature selection we
propose thresholding cat scores by controlling false nondiscovery rates (FNDR).
Third, training of the classifier is based on James--Stein shrinkage estimates
of correlations and variances, where regularization parameters are chosen
analytically without resampling. Overall, this results in an effective and
computationally inexpensive framework for high-dimensional prediction with
natural feature selection. The proposed shrinkage discriminant procedures are
implemented in the R package ``sda'' available from the R repository CRAN.
Simultaneous Recognition of Horizontal and Vertical Text in Natural Images
Authors: Chankyu Choi, Youngmin Yoon, Junsu Lee, Jun-Seok Kim
Date: 2018-12-06 00:00:00
Tasks: Scene Text Recognition

Recent state-of-the-art scene text recognition methods have primarily focused
on horizontal text in images. However, in several Asian countries, including
China, large amounts of text in signs, books, and TV commercials are vertically
directed. Because the horizontal and vertical texts exhibit different
characteristics, developing an algorithm that can simultaneously recognize both
types of text in real environments is necessary. To address this problem, we
adopted the direction encoding mask (DEM) and selective attention network (SAN)
methods based on supervised learning. DEM contains directional information to
compensate in cases that lack text direction; therefore, our network is trained
using this information to handle the vertical text. The SAN method is designed
to work individually for both types of text. To train the network to recognize
both types of text and to evaluate the effectiveness of the designed model, we
prepared a new synthetic vertical text dataset and collected an actual vertical
text dataset (VTD142) from the Web. Using these datasets, we proved that our
proposed model can accurately recognize both vertical and horizontal text and
can achieve state-of-the-art results in experiments using benchmark datasets,
including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is
relatively simple as compared to its predecessors, it maintains the accuracy
and is trained in an end-to-end manner.
Meta-song evaluation for chord recognition
Authors: Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez, Tijl De Bie
Date: 2011-09-02 00:00:00
Proceeding: TBD 2011 9
Tasks: Chord Recognition

We present a new approach to evaluate chord recognition systems on songs which do not have full annotations. The principle is to use online chord databases to generate high accurate "pseudo annotations" for these songs and compute "pseudo accuracies" of test systems. Statistical models that model the relationship between "pseudo accuracy" and real performance are then applied to estimate test systems' performance. The approach goes beyond the existing evaluation metrics, allowing us to carry out extensive analysis on chord recognition systems, such as their generalizations to different genres. In the experiments we applied this method to evaluate three state-of-the-art chord recognition systems, of which the results verified its reliability.