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Network modeling is a key enabler to achieve efficient network operation in
future self-driving Software-Defined Networks. However, we still lack
functional network models able to produce accurate predictions of Key
Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In
this paper we propose RouteNet, a novel network model based on Graph Neural
Network (GNN) that is able to understand the complex relationship between
topology, routing, and input traffic to produce accurate estimates of the
per-source/destination per-packet delay distribution and loss. RouteNet
leverages the ability of GNNs to learn and model graph-structured information
and as a result, our model is able to generalize over arbitrary topologies,
routing schemes and traffic intensity. In our evaluation, we show that RouteNet
is able to predict accurately the delay distribution (mean delay and jitter)
and loss even in topologies, routing and traffic unseen in the training (worst
case MRE=15.4%). Also, we present several use cases where we leverage the KPI
predictions of our GNN model to achieve efficient routing optimization and
network planning. | [
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] |
Healthcare data suffers from both noise and lack of ground truth. The cost of
data increases as it is cleaned and annotated in healthcare. Unlike other data
sets, medical data annotation, which is critical to accurate ground truth,
requires medical domain expertise for a better patient outcome. In this work,
we report on the use of reinforcement learning to mimic the decision making
process of annotators for medical events, to automate annotation and labelling.
The reinforcement agent learns to annotate alarm data based on annotations done
by an expert. Our method shows promising results on medical alarm data sets. We
trained DQN and A2C agents using the data from monitoring devices annotated by
an expert. Initial results from these RL agents learning the expert annotation
behavior are promising. The A2C agent performs better in terms of learning the
sparse events in a given state, thereby choosing more right actions compared to
DQN agent. To the best of our knowledge, this is the first reinforcement
learning application for the automation of medical events annotation, which has
far-reaching practical use. | [
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] |
In machine learning Trojan attacks, an adversary trains a corrupted model
that obtains good performance on normal data but behaves maliciously on data
samples with certain trigger patterns. Several approaches have been proposed to
detect such attacks, but they make undesirable assumptions about the attack
strategies or require direct access to the trained models, which restricts
their utility in practice.
This paper addresses these challenges by introducing a Meta Neural Trojan
Detection (MNTD) pipeline that does not make assumptions on the attack
strategies and only needs black-box access to models. The strategy is to train
a meta-classifier that predicts whether a given target model is Trojaned. To
train the meta-model without knowledge of the attack strategy, we introduce a
technique called jumbo learning that samples a set of Trojaned models following
a general distribution. We then dynamically optimize a query set together with
the meta-classifier to distinguish between Trojaned and benign models.
We evaluate MNTD with experiments on vision, speech, tabular data and natural
language text datasets, and against different Trojan attacks such as data
poisoning attack, model manipulation attack, and latent attack. We show that
MNTD achieves 97% detection AUC score and significantly outperforms existing
detection approaches. In addition, MNTD generalizes well and achieves high
detection performance against unforeseen attacks. We also propose a robust MNTD
pipeline which achieves 90% detection AUC even when the attacker aims to evade
the detection with full knowledge of the system. | [
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] |
It is expected that many human drivers will still prefer to drive themselves
even if the self-driving technologies are ready. Therefore, human-driven
vehicles and autonomous vehicles (AVs) will coexist in a mixed traffic for a
long time. To enable AVs to safely and efficiently maneuver in this mixed
traffic, it is critical that the AVs can understand how humans cope with risks
and make driving-related decisions. On the other hand, the driving environment
is highly dynamic and ever-changing, and it is thus difficult to enumerate all
the scenarios and hard-code the controllers. To face up these challenges, in
this work, we incorporate a human decision-making model in reinforcement
learning to control AVs for safe and efficient operations. Specifically, we
adapt regret theory to describe a human driver's lane-changing behavior, and
fit the personalized models to individual drivers for predicting their
lane-changing decisions. The predicted decisions are incorporated in the safety
constraints for reinforcement learning in training and in implementation. We
then use an extended version of double deep Q-network (DDQN) to train our AV
controller within the safety set. By doing so, the amount of collisions in
training is reduced to zero, while the training accuracy is not impinged. | [
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] |
Machine learning promises to revolutionize clinical decision making and
diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms
by determining the diseases \emph{causing} them. However, existing diagnostic
algorithms are purely associative, identifying diseases that are strongly
correlated with a patients symptoms and medical history. We show that this
inability to disentangle correlation from causation can result in sub-optimal
or dangerous diagnoses. To overcome this, we reformulate diagnosis as a
counterfactual inference task and derive new counterfactual diagnostic
algorithms. We show that this approach is closer to the diagnostic reasoning of
clinicians and significantly improves the accuracy and safety of the resulting
diagnoses. We compare our counterfactual algorithm to the standard Bayesian
diagnostic algorithm and a cohort of 44 doctors using a test set of clinical
vignettes. While the Bayesian algorithm achieves an accuracy comparable to the
average doctor, placing in the top 48% of doctors in our cohort, our
counterfactual algorithm places in the top 25% of doctors, achieving expert
clinical accuracy. This improvement is achieved simply by changing how we query
our model, without requiring any additional model improvements. Our results
show that counterfactual reasoning is a vital missing ingredient for applying
machine learning to medical diagnosis. | [
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] |
Do large datasets provide value to psychologists? Without a systematic
methodology for working with such datasets, there is a valid concern that
analyses will produce noise artifacts rather than true effects. In this paper,
we offer a way to enable researchers to systematically build models and
identify novel phenomena in large datasets. One traditional approach is to
analyze the residuals of models---the biggest errors they make in predicting
the data---to discover what might be missing from those models. However, once a
dataset is sufficiently large, machine learning algorithms approximate the true
underlying function better than the data, suggesting instead that the
predictions of these data-driven models should be used to guide model-building.
We call this approach "Scientific Regret Minimization" (SRM) as it focuses on
minimizing errors for cases that we know should have been predictable. We
demonstrate this methodology on a subset of the Moral Machine dataset, a public
collection of roughly forty million moral decisions. Using SRM, we found that
incorporating a set of deontological principles that capture dimensions along
which groups of agents can vary (e.g. sex and age) improves a computational
model of human moral judgment. Furthermore, we were able to identify and
independently validate three interesting moral phenomena: criminal
dehumanization, age of responsibility, and asymmetric notions of
responsibility. | [
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] |
Today high-performance computing (HPC) platforms are still dominated by batch
jobs. Accordingly, effective batch job scheduling is crucial to obtain high
system efficiency. Existing HPC batch job schedulers typically leverage
heuristic priority functions to prioritize and schedule jobs. But, once
configured and deployed by the experts, such priority functions can hardly
adapt to the changes of job loads, optimization goals, or system settings,
potentially leading to degraded system efficiency when changes occur. To
address this fundamental issue, we present RLScheduler, an automated HPC batch
job scheduler built on reinforcement learning. RLScheduler relies on minimal
manual interventions or expert knowledge, but can learn high-quality scheduling
policies via its own continuous 'trial and error'. We introduce a new
kernel-based neural network structure and trajectory filtering mechanism in
RLScheduler to improve and stabilize the learning process. Through extensive
evaluations, we confirm that RLScheduler can learn high-quality scheduling
policies towards various workloads and various optimization goals with
relatively low computation cost. Moreover, we show that the learned models
perform stably even when applied to unseen workloads, making them practical for
production use. | [
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] |
Deductive formalisms have been strongly developed in recent years; among
them, Answer Set Programming (ASP) gained some momentum, and has been lately
fruitfully employed in many real-world scenarios. Nonetheless, in spite of a
large number of success stories in relevant application areas, and even in
industrial contexts, deductive reasoning cannot be considered the ultimate,
comprehensive solution to AI; indeed, in several contexts, other approaches
result to be more useful. Typical Bioinformatics tasks, for instance
classification, are currently carried out mostly by Machine Learning (ML) based
solutions. In this paper, we focus on the relatively new problem of analyzing
the evolution of neurological disorders. In this context, ML approaches already
demonstrated to be a viable solution for classification tasks; here, we show
how ASP can play a relevant role in the brain evolution simulation task. In
particular, we propose a general and extensible framework to support physicians
and researchers at understanding the complex mechanisms underlying neurological
disorders. The framework relies on a combined use of ML and ASP, and is general
enough to be applied in several other application scenarios, which are outlined
in the paper. | [
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] |
Self-play methods based on regret minimization have become the state of the
art for computing Nash equilibria in large two-players zero-sum extensive-form
games. These methods fundamentally rely on the hierarchical structure of the
players' sequential strategy spaces to construct a regret minimizer that
recursively minimizes regret at each decision point in the game tree. In this
paper, we introduce the first efficient regret minimization algorithm for
computing extensive-form correlated equilibria in large two-player general-sum
games with no chance moves. Designing such an algorithm is significantly more
challenging than designing one for the Nash equilibrium counterpart, as the
constraints that define the space of correlation plans lack the hierarchical
structure and might even form cycles. We show that some of the constraints are
redundant and can be excluded from consideration, and present an efficient
algorithm that generates the space of extensive-form correlation plans
incrementally from the remaining constraints. This structural decomposition is
achieved via a special convexity-preserving operation that we coin scaled
extension. We show that a regret minimizer can be designed for a scaled
extension of any two convex sets, and that from the decomposition we then
obtain a global regret minimizer. Our algorithm produces feasible iterates.
Experiments show that it significantly outperforms prior approaches and for
larger problems it is the only viable option. | [
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] |
In the past decade, the field of quantum machine learning has drawn
significant attention due to the prospect of bringing genuine computational
advantages to now widespread algorithmic methods. However, not all domains of
machine learning have benefited equally from quantum enhancements. Notably,
deep learning and reinforcement learning, despite their tremendous success in
the classical domain, both individually and combined, remain relatively
unaddressed by the quantum community. Arguably, one reason behind this is the
systematic use in these domains of models and methods without prominent
computational bottlenecks, leaving little room for quantum improvements. In
this work, we study the state-of-the-art neural-network approaches for
reinforcement learning with quantum enhancements in mind. We demonstrate the
substantial learning advantage that models with a sampling bottleneck can
provide over conventional neural network architectures in complex learning
environments. These so-called energy-based models, like deep energy-based
reinforcement learning, and deep projective simulation that we also introduce
in this work, effectively allow to trade off learning performance for
efficiency of computation. To alleviate the additional computational costs, we
propose to leverage future and near-term quantum algorithms, resulting in
overall more advantageous learning algorithms. This is achieved using
cutting-edge and new quantum computing machinery to speed-up classical sampling
methods and by employing generalized models to gain an additional quantum
advantage. | [
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] |
Deep Neural Network-based systems are now the state-of-the-art in many
robotics tasks, but their application in safety-critical domains remains
dangerous without formal guarantees on network robustness. Small perturbations
to sensor inputs (from noise or adversarial examples) are often enough to
change network-based decisions, which was already shown to cause an autonomous
vehicle to swerve into oncoming traffic. In light of these dangers, numerous
algorithms have been developed as defensive mechanisms from these adversarial
inputs, some of which provide formal robustness guarantees or certificates.
This work leverages research on certified adversarial robustness to develop an
online certified defense for deep reinforcement learning algorithms. The
proposed defense computes guaranteed lower bounds on state-action values during
execution to identify and choose the optimal action under a worst-case
deviation in input space due to possible adversaries or noise. The approach is
demonstrated on a Deep Q-Network policy and is shown to increase robustness to
noise and adversaries in pedestrian collision avoidance scenarios and a classic
control task. | [
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] |
The social implications of algorithmic decision-making in sensitive contexts
have generated lively debates among multiple stakeholders, such as moral and
political philosophers, computer scientists, and the public. Yet, the lack of a
common language and a conceptual framework for an appropriate bridging of the
moral, technical, and political aspects of the debate prevents the discussion
to be as effective as it can be. Social scientists and psychologists are
contributing to this debate by gathering a wealth of empirical data, yet a
philosophical analysis of the social implications of algorithmic
decision-making remains comparatively impoverished. In attempting to address
this lacuna, this paper argues that a hierarchy of different types of
explanations for why and how an algorithmic decision outcome is achieved can
establish the relevant connection between the moral and technical aspects of
algorithmic decision-making. In particular, I offer a multi-faceted conceptual
framework for the explanations and the interpretations of algorithmic
decisions, and I claim that this framework can lay the groundwork for a focused
discussion among multiple stakeholders about the social implications of
algorithmic decision-making, as well as AI governance and ethics more
generally. | [
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] |
Machine Learning models, extensively used for various multimedia
applications, are offered to users as a blackbox service on the Cloud on a
pay-per-query basis. Such blackbox models are commercially valuable to
adversaries, making them vulnerable to extraction attacks to reverse engineer
the proprietary model thereby violating the model privacy and Intellectual
Property. Here, the adversary first extracts the model architecture or
hyperparameters through side channel leakage, followed by stealing the
functionality of the target model by training the reconstructed architecture on
a synthetic dataset. While the attacks proposed in literature are empirical,
there is a need for a theoretical framework to measure the information leaked
under such extraction attacks. To this extent, in this work, we propose a novel
probabilistic framework, Airavata, to estimate the information leakage in such
model extraction attacks. This framework captures the fact that extracting the
exact target model is difficult due to experimental uncertainty while inferring
model hyperparameters and stochastic nature of training to steal the target
model functionality. Specifically, we use Bayesian Networks to capture
uncertainty in estimating the target model under various extraction attacks
based on the subjective notion of probability. We validate the proposed
framework under different adversary assumptions commonly adopted in literature
to reason about the attack efficacy. This provides a practical tool to infer
actionable details about extracting blackbox models and help identify the best
attack combination which maximises the knowledge extracted (or information
leaked) from the target model. | [
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] |
The cornerstone of computational drug design is the calculation of binding
affinity between two biological counterparts, especially a chemical compound,
i.e., a ligand, and a protein. Predicting the strength of protein-ligand
binding with reasonable accuracy is critical for drug discovery. In this paper,
we propose a data-driven framework named DeepAtom to accurately predict the
protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN)
architecture, DeepAtom could automatically extract binding related atomic
interaction patterns from the voxelized complex structure. Compared with the
other CNN based approaches, our light-weight model design effectively improves
the model representational capacity, even with the limited available training
data. With validation experiments on the PDBbind v.2016 benchmark and the
independent Astex Diverse Set, we demonstrate that the less feature engineering
dependent DeepAtom approach consistently outperforms the other state-of-the-art
scoring methods. We also compile and propose a new benchmark dataset to further
improve the model performances. With the new dataset as training input,
DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016
core set. The promising results demonstrate that DeepAtom models can be
potentially adopted in computational drug development protocols such as
molecular docking and virtual screening. | [
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A brain-computer interface (BCI) system usually needs a long calibration
session for each new subject/task to adjust its parameters, which impedes its
transition from the laboratory to real-world applications. Domain adaptation,
which leverages labeled data from auxiliary subjects/tasks (source domains),
has demonstrated its effectiveness in reducing such calibration effort.
Currently, most domain adaptation approaches require the source domains to have
the same feature space and label space as the target domain, which limits their
applications, as the auxiliary data may have different feature spaces and/or
different label spaces. This paper considers different set domain adaptation
for BCIs, i.e., the source and target domains have different label spaces. We
introduce a practical setting of different label sets for BCIs, and propose a
novel label alignment (LA) approach to align the source label space with the
target label space. It has three desirable properties: 1) LA only needs as few
as one labeled sample from each class of the target subject; 2) LA can be used
as a preprocessing step before different feature extraction and classification
algorithms; and, 3) LA can be integrated with other domain adaptation
approaches to achieve even better performance. Experiments on two motor imagery
datasets demonstrated the effectiveness of LA. | [
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] |
We developed an explainable artificial intelligence (AI) early warning score
(xAI-EWS) system for early detection of acute critical illness. While
maintaining a high predictive performance, our system explains to the clinician
on which relevant electronic health records (EHRs) data the prediction is
grounded. Acute critical illness is often preceded by deterioration of
routinely measured clinical parameters, e.g., blood pressure and heart rate.
Early clinical prediction is typically based on manually calculated screening
metrics that simply weigh these parameters, such as Early Warning Scores (EWS).
The predictive performance of EWSs yields a tradeoff between sensitivity and
specificity that can lead to negative outcomes for the patient. Previous work
on EHR-trained AI systems offers promising results with high levels of
predictive performance in relation to the early, real-time prediction of acute
critical illness. However, without insight into the complex decisions by such
system, clinical translation is hindered. In this letter, we present our
xAI-EWS system, which potentiates clinical translation by accompanying a
prediction with information on the EHR data explaining it. | [
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] |
Function approximation is a powerful approach for structuring large decision
problems that has facilitated great achievements in the areas of reinforcement
learning and game playing. Regression counterfactual regret minimization (RCFR)
is a simple algorithm for approximately solving imperfect information games
with normalized rectified linear unit (ReLU) parameterized policies. In
contrast, the more conventional softmax parameterization is standard in the
field of reinforcement learning and yields a regret bound with a better
dependence on the number of actions. We derive approximation error-aware regret
bounds for $(\Phi, f)$-regret matching, which applies to a general class of
link functions and regret objectives. These bounds recover a tighter bound for
RCFR and provide a theoretical justification for RCFR implementations with
alternative policy parameterizations ($f$-RCFR), including softmax. We provide
exploitability bounds for $f$-RCFR with the polynomial and exponential link
functions in zero-sum imperfect information games and examine empirically how
the link function interacts with the severity of the approximation. We find
that the previously studied ReLU parameterization performs better when the
approximation error is small while the softmax parameterization can perform
better when the approximation error is large. | [
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] |
General-purpose planning algorithms for automated driving combine mission,
behavior, and local motion planning. Such planning algorithms map features of
the environment and driving kinematics into complex reward functions. To
achieve this, planning experts often rely on linear reward functions. The
specification and tuning of these reward functions is a tedious process and
requires significant experience. Moreover, a manually designed linear reward
function does not generalize across different driving situations. In this work,
we propose a deep learning approach based on inverse reinforcement learning
that generates situation-dependent reward functions. Our neural network
provides a mapping between features and actions of sampled driving policies of
a model-predictive control-based planner and predicts reward functions for
upcoming planning cycles. In our evaluation, we compare the driving style of
reward functions predicted by our deep network against clustered and linear
reward functions. Our proposed deep learning approach outperforms clustered
linear reward functions and is at par with linear reward functions with
a-priori knowledge about the situation. | [
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] |
How can we make software analytics simpler and faster? One method is to match
the complexity of analysis to the intrinsic complexity of the data being
explored. For example, hyperparameter optimizers find the control settings for
data miners that improve the predictions generated via software analytics.
Sometimes, very fast hyperparameter optimization can be achieved by
"DODGE-ing"; i.e. simply steering way from settings that lead to similar
conclusions. But when is it wise to use that simple approach and when must we
use more complex (and much slower) optimizers?} To answer this, we applied
hyperparameter optimization to 120 SE data sets that explored bad smell
detection, predicting Github issue close time, bug report analysis, defect
prediction, and dozens of other non-SE problems. We find that the simple DODGE
works best for data sets with low "intrinsic dimensionality" (u ~ 3) and very
poorly for higher-dimensional data (u > 8). Nearly all the SE data seen here
was intrinsically low-dimensional, indicating that DODGE is applicable for many
SE analytics tasks. | [
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] |
In this paper, we introduce the increasing belief criterion in association
rule mining. The criterion uses a recursive application of Bayes' theorem to
compute a rule's belief. Extracted rules are required to have their belief
increase with their last observation. We extend the taxonomy of association
rule mining algorithms with a new branch for Bayesian rule mining~(BRM), which
uses increasing belief as the rule selection criterion. In contrast, the
well-established frequent association rule mining~(FRM) branch relies on the
minimum-support concept to extract rules. We derive properties of the
increasing belief criterion, such as the increasing belief boundary,
no-prior-worries, and conjunctive premises. Subsequently, we implement a BRM
algorithm using the increasing belief criterion, and illustrate its
functionality in three experiments: (1)~a proof-of-concept to illustrate BRM
properties, (2)~an analysis relating socioeconomic information and chemical
exposure data, and (3)~mining behaviour routines in patients undergoing
neurological rehabilitation. We illustrate how BRM is capable of extracting
rare rules and does not suffer from support dilution. Furthermore, we show that
BRM focuses on the individual event generating processes, while FRM focuses on
their commonalities. We consider BRM's increasing belief as an alternative
criterion to thresholds on rule support, as often applied in FRM, to determine
rule usefulness. | [
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] |
Time perception is the phenomenological experience of time by an individual.
In this paper, we study how to replicate neural mechanisms involved in time
perception, allowing robots to take a step towards temporal cognition. Our
framework follows a twofold biologically inspired approach. The first step
consists of estimating the passage of time from sensor measurements, since
environmental stimuli influence the perception of time. Sensor data is modeled
as Gaussian processes that represent the second-order statistics of the natural
environment. The estimated elapsed time between two events is computed from the
maximum likelihood estimate of the joint distribution of the data collected
between them. Moreover, exactly how time is encoded in the brain remains
unknown, but there is strong evidence of the involvement of dopaminergic
neurons in timing mechanisms. Since their phasic activity has a similar
behavior to the reward prediction error of temporal-difference learning models,
the latter are used to replicate this behavior. The second step of this
approach consists therefore of applying the agent's estimate of the elapsed
time in a reinforcement learning problem, where a feature representation called
Microstimuli is used. We validate our framework by applying it to an experiment
that was originally conducted with mice, and conclude that a robot using this
framework is able to reproduce the timing mechanisms of the animal's brain. | [
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] |
Although numerous ethics courses are available, with many focusing
specifically on technology and computer ethics, pedagogical approaches employed
in these courses rely exclusively on texts rather than on software development
or data analysis. Technical students often consider these courses unimportant
and a distraction from the "real" material. To develop instructional materials
and methodologies that are thoughtful and engaging, we must strive for balance:
between texts and coding, between critique and solution, and between
cutting-edge research and practical applicability. Finding such balance is
particularly difficult in the nascent field of responsible data science (RDS),
where we are only starting to understand how to interface between the
intrinsically different methodologies of engineering and social sciences. In
this paper we recount a recent experience in developing and teaching an RDS
course to graduate and advanced undergraduate students in data science. We then
dive into an area that is critically important to RDS -- transparency and
interpretability of machine-assisted decision-making, and tie this area to the
needs of emerging RDS curricula. Recounting our own experience, and leveraging
literature on pedagogical methods in data science and beyond, we propose the
notion of an "object-to-interpret-with". We link this notion to "nutritional
labels" -- a family of interpretability tools that are gaining popularity in
RDS research and practice. With this work we aim to contribute to the nascent
area of RDS education, and to inspire others in the community to come together
to develop a deeper theoretical understanding of the pedagogical needs of RDS,
and contribute concrete educational materials and methodologies that others can
use. All course materials are publicly available at
https://dataresponsibly.github.io/courses. | [
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] |
Learning robotic manipulation tasks using reinforcement learning with sparse
rewards is currently impractical due to the outrageous data requirements. Many
practical tasks require manipulation of multiple objects, and the complexity of
such tasks increases with the number of objects. Learning from a curriculum of
increasingly complex tasks appears to be a natural solution, but unfortunately,
does not work for many scenarios. We hypothesize that the inability of the
state-of-the-art algorithms to effectively utilize a task curriculum stems from
the absence of inductive biases for transferring knowledge from simpler to
complex tasks. We show that graph-based relational architectures overcome this
limitation and enable learning of complex tasks when provided with a simple
curriculum of tasks with increasing numbers of objects. We demonstrate the
utility of our framework on a simulated block stacking task. Starting from
scratch, our agent learns to stack six blocks into a tower. Despite using
step-wise sparse rewards, our method is orders of magnitude more data-efficient
and outperforms the existing state-of-the-art method that utilizes human
demonstrations. Furthermore, the learned policy exhibits zero-shot
generalization, successfully stacking blocks into taller towers and previously
unseen configurations such as pyramids, without any further training. | [
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To make progress in science, we often build abstract representations of
physical systems that meaningfully encode information about the systems. The
representations learnt by most current machine learning techniques reflect
statistical structure present in the training data; however, these methods do
not allow us to specify explicit and operationally meaningful requirements on
the representation. Here, we present a neural network architecture based on the
notion that agents dealing with different aspects of a physical system should
be able to communicate relevant information as efficiently as possible to one
another. This produces representations that separate different parameters which
are useful for making statements about the physical system in different
experimental settings. We present examples involving both classical and quantum
physics. For instance, our architecture finds a compact representation of an
arbitrary two-qubit system that separates local parameters from parameters
describing quantum correlations. We further show that this method can be
combined with reinforcement learning to enable representation learning within
interactive scenarios where agents need to explore experimental settings to
identify relevant variables. | [
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] |
Autonomous underwater vehicle (AUV) plays an increasingly important role in
ocean exploration. Existing AUVs are usually not fully autonomous and generally
limited to pre-planning or pre-programming tasks. Reinforcement learning (RL)
and deep reinforcement learning have been introduced into the AUV design and
research to improve its autonomy. However, these methods are still difficult to
apply directly to the actual AUV system because of the sparse rewards and low
learning efficiency. In this paper, we proposed a deep interactive
reinforcement learning method for path following of AUV by combining the
advantages of deep reinforcement learning and interactive RL. In addition,
since the human trainer cannot provide human rewards for AUV when it is running
in the ocean and AUV needs to adapt to a changing environment, we further
propose a deep reinforcement learning method that learns from both human
rewards and environmental rewards at the same time. We test our methods in two
path following tasks---straight line and sinusoids curve following of AUV by
simulating in the Gazebo platform. Our experimental results show that with our
proposed deep interactive RL method, AUV can converge faster than a DQN learner
from only environmental reward. Moreover, AUV learning with our deep RL from
both human and environmental rewards can also achieve a similar or even better
performance than that with the deep interactive RL method and can adapt to the
actual environment by further learning from environmental rewards. | [
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] |
Estimating temporal patterns in travel times along road segments in urban
settings is of central importance to traffic engineers and city planners. In
this work, we propose a methodology to leverage coarse-grained and aggregated
travel time data to estimate the street-level travel times of a given
metropolitan area. Our main focus is to estimate travel times along the
arterial road segments where relevant data are often unavailable. The central
idea of our approach is to leverage easy-to-obtain, aggregated data sets with
broad spatial coverage, such as the data published by Uber Movement, as the
fabric over which other expensive, fine-grained datasets, such as loop counter
and probe data, can be overlaid. Our proposed methodology uses a graph
representation of the road network and combines several techniques such as
graph-based routing, trip sampling, graph sparsification, and least-squares
optimization to estimate the street-level travel times. Using sampled trips and
weighted shortest-path routing, we iteratively solve constrained least-squares
problems to obtain the travel time estimates. We demonstrate our method on the
Los Angeles metropolitan-area street network, where aggregated travel time data
is available for trips between traffic analysis zones. Additionally, we present
techniques to scale our approach via a novel graph pseudo-sparsification
technique. | [
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Digital agriculture has the promise to transform agricultural throughput. It
can do this by applying data science and engineering for mapping input factors
to crop throughput, while bounding the available resources. In addition, as the
data volumes and varieties increase with the increase in sensor deployment in
agricultural fields, data engineering techniques will also be instrumental in
collection of distributed data as well as distributed processing of the data.
These have to be done such that the latency requirements of the end users and
applications are satisfied. Understanding how farm technology and big data can
improve farm productivity can significantly increase the world's food
production by 2050 in the face of constrained arable land and with the water
levels receding. While much has been written about digital agriculture's
potential, little is known about the economic costs and benefits of these
emergent systems. In particular, the on-farm decision making processes, both in
terms of adoption and optimal implementation, have not been adequately
addressed. For example, if some algorithm needs data from multiple data owners
to be pooled together, that raises the question of data ownership. This paper
is the first one to bring together the important questions that will guide the
end-to-end pipeline for the evolution of a new generation of digital
agricultural solutions, driving the next revolution in agriculture and
sustainability under one umbrella. | [
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] |
Explainable recommendation is far from being well solved partly due to three
challenges. The first is the personalization of preference learning, which
requires that different items/users have different contributions to the
learning of user preference or item quality. The second one is dynamic
explanation, which is crucial for the timeliness of recommendation
explanations. The last one is the granularity of explanations. In practice,
aspect-level explanations are more persuasive than item-level or user-level
ones. In this paper, to address these challenges simultaneously, we propose a
novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable
recommendations, which can make recommendations with dynamic aspect-level
explanations. The main idea of HDE is to learn the dynamic embeddings of users
and items for rating prediction and the dynamic latent aspect
preference/quality vectors for the generation of aspect-level explanations,
through fusion of the dynamic implicit feedbacks extracted from reviews and the
attentive user-item interactions. Particularly, as the aspect
preference/quality of users/items is learned automatically, HDE is able to
capture the impact of aspects that are not mentioned in reviews of a user or an
item. The extensive experiments conducted on real datasets verify the
recommending performance and explainability of HDE. The source code of our work
is available at \url{https://github.com/lola63/HDE-Python} | [
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] |
As deep neural networks (DNNs) get adopted in an ever-increasing number of
applications, explainability has emerged as a crucial desideratum for these
models. In many real-world tasks, one of the principal reasons for requiring
explainability is to in turn assess prediction robustness, where predictions
(i.e., class labels) that do not conform to their respective explanations
(e.g., presence or absence of a concept in the input) are deemed to be
unreliable. However, most, if not all, prior methods for checking
explanation-conformity (e.g., LIME, TCAV, saliency maps) require significant
manual intervention, which hinders their large-scale deployability. In this
paper, we propose a robustness-assessment framework, at the core of which is
the idea of using machine-checkable concepts. Our framework defines a large
number of concepts that the DNN explanations could be based on and performs the
explanation-conformity check at test time to assess prediction robustness. Both
steps are executed in an automated manner without requiring any human
intervention and are easily scaled to datasets with a very large number of
classes. Experiments on real-world datasets and human surveys show that our
framework is able to enhance prediction robustness significantly: the
predictions marked to be robust by our framework have significantly higher
accuracy and are more robust to adversarial perturbations. | [
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Determination of inspection and maintenance policies for minimizing long-term
risks and costs in deteriorating engineering environments constitutes a complex
optimization problem. Major computational challenges include the (i) curse of
dimensionality, due to exponential scaling of state/action set cardinalities
with the number of components; (ii) curse of history, related to exponentially
growing decision-trees with the number of decision-steps; (iii) presence of
state uncertainties, induced by inherent environment stochasticity and
variability of inspection/monitoring measurements; (iv) presence of
constraints, pertaining to stochastic long-term limitations, due to resource
scarcity and other infeasible/undesirable system responses. In this work, these
challenges are addressed within a joint framework of constrained Partially
Observable Markov Decision Processes (POMDP) and multi-agent Deep Reinforcement
Learning (DRL). POMDPs optimally tackle (ii)-(iii), combining stochastic
dynamic programming with Bayesian inference principles. Multi-agent DRL
addresses (i), through deep function parametrizations and decentralized control
assumptions. Challenge (iv) is herein handled through proper state augmentation
and Lagrangian relaxation, with emphasis on life-cycle risk-based constraints
and budget limitations. The underlying algorithmic steps are provided, and the
proposed framework is found to outperform well-established policy baselines and
facilitate adept prescription of inspection and intervention actions, in cases
where decisions must be made in the most resource- and risk-aware manner. | [
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In continuing tasks, average-reward reinforcement learning may be a more
appropriate problem formulation than the more common discounted reward
formulation. As usual, learning an optimal policy in this setting typically
requires a large amount of training experiences. Reward shaping is a common
approach for incorporating domain knowledge into reinforcement learning in
order to speed up convergence to an optimal policy. However, to the best of our
knowledge, the theoretical properties of reward shaping have thus far only been
established in the discounted setting. This paper presents the first reward
shaping framework for average-reward learning and proves that, under standard
assumptions, the optimal policy under the original reward function can be
recovered. In order to avoid the need for manual construction of the shaping
function, we introduce a method for utilizing domain knowledge expressed as a
temporal logic formula. The formula is automatically translated to a shaping
function that provides additional reward throughout the learning process. We
evaluate the proposed method on three continuing tasks. In all cases, shaping
speeds up the average-reward learning rate without any reduction in the
performance of the learned policy compared to relevant baselines. | [
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With the increasing adoption of Deep Learning (DL) for critical tasks, such
as autonomous driving, the evaluation of the quality of systems that rely on DL
has become crucial. Once trained, DL systems produce an output for any
arbitrary numeric vector provided as input, regardless of whether it is within
or outside the validity domain of the system under test. Hence, the quality of
such systems is determined by the intersection between their validity domain
and the regions where their outputs exhibit a misbehaviour. In this paper, we
introduce the notion of frontier of behaviours, i.e., the inputs at which the
DL system starts to misbehave. If the frontier of misbehaviours is outside the
validity domain of the system, the quality check is passed. Otherwise, the
inputs at the intersection represent quality deficiencies of the system. We
developed DeepJanus, a search-based tool that generates frontier inputs for DL
systems. The experimental results obtained for the lane keeping component of a
self-driving car show that the frontier of a well trained system contains
almost exclusively unrealistic roads that violate the best practices of civil
engineering, while the frontier of a poorly trained one includes many valid
inputs that point to serious deficiencies of the system. | [
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This paper explores the important role of critical science, and in particular
of post-colonial and decolonial theories, in understanding and shaping the
ongoing advances in artificial intelligence. Artificial Intelligence (AI) is
viewed as amongst the technological advances that will reshape modern societies
and their relations. Whilst the design and deployment of systems that
continually adapt holds the promise of far-reaching positive change, they
simultaneously pose significant risks, especially to already vulnerable
peoples. Values and power are central to this discussion. Decolonial theories
use historical hindsight to explain patterns of power that shape our
intellectual, political, economic, and social world. By embedding a decolonial
critical approach within its technical practice, AI communities can develop
foresight and tactics that can better align research and technology development
with established ethical principles, centring vulnerable peoples who continue
to bear the brunt of negative impacts of innovation and scientific progress. We
highlight problematic applications that are instances of coloniality, and using
a decolonial lens, submit three tactics that can form a decolonial field of
artificial intelligence: creating a critical technical practice of AI, seeking
reverse tutelage and reverse pedagogies, and the renewal of affective and
political communities. The years ahead will usher in a wave of new scientific
breakthroughs and technologies driven by AI research, making it incumbent upon
AI communities to strengthen the social contract through ethical foresight and
the multiplicity of intellectual perspectives available to us; ultimately
supporting future technologies that enable greater well-being, with the goal of
beneficence and justice for all. | [
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] |
Law enforcement must prioritize sources of unfairness before mitigating their
underlying unfairness, considering that they have limited resources. Unlike
previous works that only make cautionary claims of discrimination and de-biases
data after its generation, this paper attempts to prioritize unfair sources
before mitigating their unfairness in the real-world. We assume that a causal
bayesian network, representative of the data generation procedure, along with
the sensitive nodes, that result in unfairness, are given. We quantify Edge
Flow, which is the belief flowing along an edge by attenuating the indirect
path influences, and use it to quantify Edge Unfairness. We prove that
cumulative unfairness is non-existent in any decision, like judicial bail,
towards any sensitive groups, like race, when the edge unfairness is absent,
given an error-free linear model of conditional probability. We then measure
the potential to mitigate the cumulative unfairness when edge unfairness is
decreased. Based on these measures, we propose an unfair edge prioritization
algorithm that prioritizes the unfair edges and a discrimination removal
procedure that de-biases the generated data distribution. The experimental
section validates the specifications used for quantifying the above measures. | [
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] |
A number of applications involve sequential arrival of users, and require
showing each user an ordering of items. A prime example (which forms the focus
of this paper) is the bidding process in conference peer review where reviewers
enter the system sequentially, each reviewer needs to be shown the list of
submitted papers, and the reviewer then "bids" to review some papers. The order
of the papers shown has a significant impact on the bids due to primacy
effects. In deciding on the ordering of papers to show, there are two competing
goals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfying
reviewers by showing them relevant items. In this paper, we begin by developing
a framework to study this problem in a principled manner. We present an
algorithm called SUPER*, inspired by the A* algorithm, for this goal.
Theoretically, we show a local optimality guarantee of our algorithm and prove
that popular baselines are considerably suboptimal. Moreover, under a community
model for the similarities, we prove that SUPER* is near-optimal whereas the
popular baselines are considerably suboptimal. In experiments on real data from
ICLR 2018 and synthetic data, we find that SUPER* considerably outperforms
baselines deployed in existing systems, consistently reducing the number of
papers with fewer than requisite bids by 50-75% or more, and is also robust to
various real world complexities. | [
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] |
Public transportation plays a critical role in people's daily life. It has
been proven that public transportation is more environmentally sustainable,
efficient, and economical than any other forms of travel. However, due to the
increasing expansion of transportation networks and more complex travel
situations, people are having difficulties in efficiently finding the most
preferred route from one place to another through public transportation
systems. To this end, in this paper, we present Polestar, a data-driven engine
for intelligent and efficient public transportation routing. Specifically, we
first propose a novel Public Transportation Graph (PTG) to model public
transportation system in terms of various travel costs, such as time or
distance. Then, we introduce a general route search algorithm coupled with an
efficient station binding method for efficient route candidate generation.
After that, we propose a two-pass route candidate ranking module to capture
user preferences under dynamic travel situations. Finally, experiments on two
real-world data sets demonstrate the advantages of Polestar in terms of both
efficiency and effectiveness. Indeed, in early 2019, Polestar has been deployed
on Baidu Maps, one of the world's largest map services. To date, Polestar is
servicing over 330 cities, answers over a hundred millions of queries each day,
and achieves substantial improvement of user click ratio. | [
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] |
The field of cybersecurity has mostly been a cat-and-mouse game with the
discovery of new attacks leading the way. To take away an attacker's advantage
of reconnaissance, researchers have proposed proactive defense methods such as
Moving Target Defense (MTD). To find good movement strategies, researchers have
modeled MTD as leader-follower games between the defender and a
cyber-adversary. We argue that existing models are inadequate in sequential
settings when there is incomplete information about a rational adversary and
yield sub-optimal movement strategies. Further, while there exists an array of
work on learning defense policies in sequential settings for cyber-security,
they are either unpopular due to scalability issues arising out of incomplete
information or tend to ignore the strategic nature of the adversary simplifying
the scenario to use single-agent reinforcement learning techniques. To address
these concerns, we propose (1) a unifying game-theoretic model, called the
Bayesian Stackelberg Markov Games (BSMGs), that can model uncertainty over
attacker types and the nuances of an MTD system and (2) a Bayesian Strong
Stackelberg Q-learning (BSS-Q) approach that can, via interaction, learn the
optimal movement policy for BSMGs within a reasonable time. We situate BSMGs in
the landscape of incomplete-information Markov games and characterize the
notion of Strong Stackelberg Equilibrium (SSE) in them. We show that our
learning approach converges to an SSE of a BSMG and then highlight that the
learned movement policy (1) improves the state-of-the-art in MTD for
web-application security and (2) converges to an optimal policy in MTD domains
with incomplete information about adversaries even when prior information about
rewards and transitions is absent. | [
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] |
Deep Reinforcement Learning (DRL) experiments are commonly performed in
simulated environments due to the tremendous training sample demands from deep
neural networks. In contrast, model-based Bayesian Learning allows a robot to
learn good policies within a few trials in the real world. Although it takes
fewer iterations, Bayesian methods pay a relatively higher computational cost
per trial, and the advantage of such methods is strongly tied to dimensionality
and noise. In here, we compare a Deep Bayesian Learning algorithm with a
model-free DRL algorithm while analyzing our results collected from both
simulations and real-world experiments. While considering Sim and Real
learning, our experiments show that the sample-efficient Deep Bayesian RL
performance is better than DRL even when computation time (as opposed to number
of iterations) is taken in consideration. Additionally, the difference in
computation time between Deep Bayesian RL performed in simulation and in
experiments point to a viable path to traverse the reality gap. We also show
that a mix between Sim and Real does not outperform a purely Real approach,
pointing to the possibility that reality can provide the best prior knowledge
to a Bayesian Learning. Roboticists design and build robots every day, and our
results show that a higher learning efficiency in the real-world will shorten
the time between design and deployment by skipping simulations. | [
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How do cognitive agents decide what is the relevant information to learn and
how goals are selected to gain this knowledge? Cognitive agents need to be
motivated to perform any action. We discuss that emotions arise when
differences between expected and actual rates of progress towards a goal are
experienced. Therefore, the tracking of prediction error dynamics has a tight
relationship with emotions. Here, we suggest that the tracking of prediction
error dynamics allows an artificial agent to be intrinsically motivated to seek
new experiences but constrained to those that generate reducible prediction
error.We present an intrinsic motivation architecture that generates behaviors
towards self-generated and dynamic goals and that regulates goal selection and
the balance between exploitation and exploration through multi-level monitoring
of prediction error dynamics. This new architecture modulates exploration noise
and leverages computational resources according to the dynamics of the overall
performance of the learning system. Additionally, it establishes a possible
solution to the temporal dynamics of goal selection. The results of the
experiments presented here suggest that this architecture outperforms intrinsic
motivation approaches where exploratory noise and goals are fixed and a greedy
strategy is applied. | [
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] |
In robotics, methods and softwares usually require optimizations of
hyperparameters in order to be efficient for specific tasks, for instance
industrial bin-picking from homogeneous heaps of different objects. We present
a developmental framework based on long-term memory and reasoning modules
(Bayesian Optimisation, visual similarity and parameters bounds reduction)
allowing a robot to use meta-learning mechanism increasing the efficiency of
such continuous and constrained parameters optimizations. The new optimization,
viewed as a learning for the robot, can take advantage of past experiences
(stored in the episodic and procedural memories) to shrink the search space by
using reduced parameters bounds computed from the best optimizations realized
by the robot with similar tasks of the new one (e.g. bin-picking from an
homogenous heap of a similar object, based on visual similarity of objects
stored in the semantic memory). As example, we have confronted the system to
the constrained optimizations of 9 continuous hyperparameters for a
professional software (Kamido) in industrial robotic arm bin-picking tasks, a
step that is needed each time to handle correctly new object. We used a
simulator to create bin-picking tasks for 8 different objects (7 in simulation
and one with real setup, without and with meta-learning with experiences coming
from other similar objects) achieving goods results despite a very small
optimization budget, with a better performance reached when meta-learning is
used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations
for each optimization) for every object tested (p-value=0.036). | [
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] |
Anomalies are occurrences in a dataset that are in some way unusual and do
not fit the general patterns. The concept of the anomaly is typically
ill-defined and perceived as vague and domain-dependent. Moreover, despite some
250 years of publications on the topic, no comprehensive and concrete overviews
of the different types of anomalies have hitherto been published. By means of
an extensive literature review this study therefore offers the first
theoretically principled and domain-independent typology of data anomalies and
presents a full overview of anomaly types and subtypes. To concretely define
the concept of the anomaly and its different manifestations, the typology
employs five dimensions: data type, cardinality of relationship, anomaly level,
data structure, and data distribution. These fundamental and data-centric
dimensions naturally yield 3 broad groups, 9 basic types, and 63 subtypes of
anomalies. The typology facilitates the evaluation of the functional
capabilities of anomaly detection algorithms, contributes to explainable data
science, and provides insights into relevant topics such as local versus global
anomalies. | [
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] |
Artificial intelligence (AI) has huge potential to improve the health and
well-being of people, but adoption in clinical practice is still limited. Lack
of transparency is identified as one of the main barriers to implementation, as
clinicians should be confident the AI system can be trusted. Explainable AI has
the potential to overcome this issue and can be a step towards trustworthy AI.
In this paper we review the recent literature to provide guidance to
researchers and practitioners on the design of explainable AI systems for the
health-care domain and contribute to formalization of the field of explainable
AI. We argue the reason to demand explainability determines what should be
explained as this determines the relative importance of the properties of
explainability (i.e. interpretability and fidelity). Based on this, we propose
a framework to guide the choice between classes of explainable AI methods
(explainable modelling versus post-hoc explanation; model-based,
attribution-based, or example-based explanations; global and local
explanations). Furthermore, we find that quantitative evaluation metrics, which
are important for objective standardized evaluation, are still lacking for some
properties (e.g. clarity) and types of explanations (e.g. example-based
methods). We conclude that explainable modelling can contribute to trustworthy
AI, but the benefits of explainability still need to be proven in practice and
complementary measures might be needed to create trustworthy AI in health care
(e.g. reporting data quality, performing extensive (external) validation, and
regulation). | [
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] |
Medical diagnoses can shape and change the life of a person drastically.
Therefore, it is always best advised to collect as much evidence as possible to
be certain about the diagnosis. Unfortunately, in the case of the Brugada
Syndrome (BrS), a rare and inherited heart disease, only one diagnostic
criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In
the following treatise, we question whether the investigation of ECG strips by
the means of machine learning methods improves the detection of BrS positive
cases and hence, the diagnostic process. We propose a pipeline that reads in
scanned images of ECGs, and transforms the encaptured signals to digital
time-voltage data after several processing steps. Then, we present a long
short-term memory (LSTM) classifier that is built based on the previously
extracted data and that makes the diagnosis. The proposed pipeline
distinguishes between three major types of ECG images and recreates each
recorded lead signal. Features and quality are retained during the digitization
of the data, albeit some encountered issues are not fully removed (Part I).
Nevertheless, the results of the aforesaid program are suitable for further
investigation of the ECG by a computational method such as the proposed
classifier which proves the concept and could be the architectural basis for
future research (Part II). This thesis is divided into two parts as they are
part of the same process but conceptually different. It is hoped that this work
builds a new foundation for computational investigations in the case of the BrS
and its diagnosis. | [
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] |
Calcium imaging has become a powerful and popular technique to monitor the
activity of large populations of neurons in vivo. However, for ethical
considerations and despite recent technical developments, recordings are still
constrained to a limited number of trials and animals. This limits the amount
of data available from individual experiments and hinders the development of
analysis techniques and models for more realistic sizes of neuronal
populations. The ability to artificially synthesize realistic neuronal calcium
signals could greatly alleviate this problem by scaling up the number of
trials. Here, we propose a Generative Adversarial Network (GAN) model to
generate realistic calcium signals as seen in neuronal somata with calcium
imaging. To this end, we propose CalciumGAN, a model based on the WaveGAN
architecture and train it on calcium fluorescent signals with the Wasserstein
distance. We test the model on artificial data with known ground-truth and show
that the distribution of the generated signals closely resembles the underlying
data distribution. Then, we train the model on real calcium traces recorded
from the primary visual cortex of behaving mice and confirm that the
deconvolved spike trains match the statistics of the recorded data. Together,
these results demonstrate that our model can successfully generate realistic
calcium traces, thereby providing the means to augment existing datasets of
neuronal activity for enhanced data exploration and modelling. | [
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] |
We propose Corder, a self-supervised contrastive learning framework for
source code model. Corder is designed to alleviate the need of labeled data for
code retrieval and code summarization tasks. The pre-trained model of Corder
can be used in two ways: (1) it can produce vector representation of code which
can be applied to code retrieval tasks that do not have labeled data; (2) it
can be used in a fine-tuning process for tasks that might still require label
data such as code summarization. The key innovation is that we train the source
code model by asking it to recognize similar and dissimilar code snippets
through a contrastive learning objective. To do so, we use a set of
semantic-preserving transformation operators to generate code snippets that are
syntactically diverse but semantically equivalent. Through extensive
experiments, we have shown that the code models pretrained by Corder
substantially outperform the other baselines for code-to-code retrieval,
text-to-code retrieval, and code-to-text summarization tasks. | [
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] |
Intra-cardiac Echocardiography (ICE) is a powerful imaging modality for
guiding electrophysiology and structural heart interventions. ICE provides
real-time observation of anatomy, catheters, and emergent complications.
However, this increased reliance on intraprocedural imaging creates a high
cognitive demand on physicians who can often serve as interventionalist and
imager. We present a robotic manipulator for ICE catheters to assist physicians
with imaging and serve as a platform for developing processes for procedural
automation. Herein, we introduce two application modules towards these goals:
(1) a view recovery process that allows physicians to save views during
intervention and automatically return with the push of a button and (2) a
data-driven approach to compensate kinematic model errors that result from
non-linear behaviors in catheter bending, providing more precise control of the
catheter tip. View recovery is validated by repeated catheter positioning in
cardiac phantom and animal experiments with position- and image-based analysis.
We present a simplified calibration approach for error compensation and verify
with complex rotation of the catheter in benchtop and phantom experiments under
varying realistic curvature conditions. Results support that a robotic
manipulator for ICE can provide an efficient and reproducible tool, potentially
reducing execution time and promoting greater utilization of ICE imaging. | [
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] |
Cardiovascular Disease (CVD) is an important cause of disability and death
among individuals with Diabetes Mellitus (DM). International clinical
guidelines for the management of Type 2 DM (T2DM) are founded on primary and
secondary prevention and favor the evaluation of CVD related risk factors
towards appropriate treatment initiation. CVD risk prediction models can
provide valuable tools for optimizing the frequency of medical visits and
performing timely preventive and therapeutic interventions against CVD events.
The integration of explainability modalities in these models can enhance human
understanding on the reasoning process, maximize transparency and embellish
trust towards the models' adoption in clinical practice. The aim of the present
study is to develop and evaluate an explainable personalized risk prediction
model for the fatal or non-fatal CVD incidence in T2DM individuals. An
explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the
Tree SHAP (SHapley Additive exPlanations) method is deployed for the
calculation of the 5-year CVD risk and the generation of individual
explanations on the model's decisions. Data from the 5-year follow up of 560
patients with T2DM are used for development and evaluation purposes. The
obtained results (AUC = 71.13%) indicate the potential of the proposed approach
to handle the unbalanced nature of the used dataset, while providing clinically
meaningful insights about the ensemble model's decision process. | [
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] |
There is increased interest in assisting non-expert audiences to effectively
interact with machine learning (ML) tools and understand the complex output
such systems produce. Here, we describe user experiments designed to study how
individual skills and personality traits predict interpretability,
explainability, and knowledge discovery from ML generated model output. Our
work relies on Fuzzy Trace Theory, a leading theory of how humans process
numerical stimuli, to examine how different end users will interpret the output
they receive while interacting with the ML system. While our sample was small,
we found that interpretability -- being able to make sense of system output --
and explainability -- understanding how that output was generated -- were
distinct aspects of user experience. Additionally, subjects were more able to
interpret model output if they possessed individual traits that promote
metacognitive monitoring and editing, associated with more detailed, verbatim,
processing of ML output. Finally, subjects who are more familiar with ML
systems felt better supported by them and more able to discover new patterns in
data; however, this did not necessarily translate to meaningful insights. Our
work motivates the design of systems that explicitly take users' mental
representations into account during the design process to more effectively
support end user requirements. | [
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] |
Timeout is a short interruption during games used to communicate a change in
strategy, to give the players a rest or to stop a negative flow in the game.
Whatever the reason, coaches expect an improvement in their team's performance
after a timeout. But how effective are these timeouts in doing so? The simple
average of the differences between the scores before and after the timeouts has
been used as evidence that there is an effect and that it is substantial. We
claim that these statistical averages are not proper evidence and a more sound
approach is needed. We applied a formal causal framework using a large dataset
of official NBA play-by-play tables and drew our assumptions about the data
generation process in a causal graph. Using different matching techniques to
estimate the causal effect of timeouts, we concluded that timeouts have no
effect on teams' performances. Actually, since most timeouts are called when
the opposing team is scoring more frequently, the moments that follow resemble
an improvement in the team's performance but are just the natural game tendency
to return to its average state. This is another example of what statisticians
call the regression to the mean phenomenon. | [
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] |
Many statistical learning models hold an assumption that the training data
and the future unlabeled data are drawn from the same distribution. However,
this assumption is difficult to fulfill in real-world scenarios and creates
barriers in reusing existing labels from similar application domains. Transfer
Learning is intended to relax this assumption by modeling relationships between
domains, and is often applied in deep learning applications to reduce the
demand for labeled data and training time. Despite recent advances in exploring
deep learning models with visual analytics tools, little work has explored the
issue of explaining and diagnosing the knowledge transfer process between deep
learning models. In this paper, we present a visual analytics framework for the
multi-level exploration of the transfer learning processes when training deep
neural networks. Our framework establishes a multi-aspect design to explain how
the learned knowledge from the existing model is transferred into the new
learning task when training deep neural networks. Based on a comprehensive
requirement and task analysis, we employ descriptive visualization with
performance measures and detailed inspections of model behaviors from the
statistical, instance, feature, and model structure levels. We demonstrate our
framework through two case studies on image classification by fine-tuning
AlexNets to illustrate how analysts can utilize our framework. | [
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] |
Accurate brain tumour segmentation is a crucial step towards improving
disease diagnosis and proper treatment planning. In this paper, we propose a
deep-learning based method to segment a brain tumour into its subregions: whole
tumour, tumour core and enhancing tumour. The proposed architecture is a 3D
convolutional neural network based on a variant of the U-Net architecture of
Ronneberger et al. [17] with three main modifications: (i) a heavy encoder,
light decoder structure using residual blocks (ii) employment of dense blocks
instead of skip connections, and (iii) utilization of self-ensembling in the
decoder part of the network. The network was trained and tested using two
different approaches: a multitask framework to segment all tumour subregions at
the same time and a three-stage cascaded framework to segment one sub-region at
a time. An ensemble of the results from both frameworks was also computed. To
address the class imbalance issue, appropriate patch extraction was employed in
a pre-processing step. The connected component analysis was utilized in the
post-processing step to reduce false positive predictions. Experimental results
on the BraTS20 validation dataset demonstrates that the proposed model achieved
average Dice Scores of 0.90, 0.82, and 0.78 for whole tumour, tumour core and
enhancing tumour respectively. | [
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] |
Accurately determining a change in protein binding affinity upon mutations is
important for the discovery and design of novel therapeutics and to assist
mutagenesis studies. Determination of change in binding affinity upon mutations
requires sophisticated, expensive, and time-consuming wet-lab experiments that
can be aided with computational methods. Most of the computational prediction
techniques require protein structures that limit their applicability to protein
complexes with known structures. In this work, we explore the sequence-based
prediction of change in protein binding affinity upon mutation. We have used
protein sequence information instead of protein structures along with machine
learning techniques to accurately predict the change in protein binding
affinity upon mutation. Our proposed sequence-based novel change in protein
binding affinity predictor called PANDA gives better accuracy than existing
methods over the same validation set as well as on an external independent test
dataset. On an external test dataset, our proposed method gives a maximum
Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art
existing protein structure-based method called MutaBind which gives a maximum
Pearson correlation coefficient of 0.59. Our proposed protein sequence-based
method, to predict a change in binding affinity upon mutations, has wide
applicability and comparable performance in comparison to existing protein
structure-based methods. A cloud-based webserver implementation of PANDA and
its python code is available at
https://sites.google.com/view/wajidarshad/software and
https://github.com/wajidarshad/panda. | [
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Recommender systems research tends to evaluate model performance offline and
on randomly sampled targets, yet the same systems are later used to predict
user behavior sequentially from a fixed point in time. Simulating online
recommender system performance is notoriously difficult and the discrepancy
between online and offline behaviors is typically not accounted for in offline
evaluations. This disparity permits weaknesses to go unnoticed until the model
is deployed in a production setting. In this paper, we first demonstrate how
omitting temporal context when evaluating recommender system performance leads
to false confidence. To overcome this, we postulate that offline evaluation
protocols can only model real-life use-cases if they account for temporal
context. Next, we propose a training procedure to further embed the temporal
context in existing models. We use a multi-objective approach to introduce
temporal context into traditionally time-unaware recommender systems and
confirm its advantage via the proposed evaluation protocol. Finally, we
validate that the Pareto Fronts obtained with the added objective dominate
those produced by state-of-the-art models that are only optimized for accuracy
on three real-world publicly available datasets. The results show that
including our temporal objective can improve recall@20 by up to 20%. | [
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] |
The research on and application of artificial intelligence (AI) has triggered
a comprehensive scientific, economic, social and political discussion. Here we
argue that statistics, as an interdisciplinary scientific field, plays a
substantial role both for the theoretical and practical understanding of AI and
for its future development. Statistics might even be considered a core element
of AI. With its specialist knowledge of data evaluation, starting with the
precise formulation of the research question and passing through a study design
stage on to analysis and interpretation of the results, statistics is a natural
partner for other disciplines in teaching, research and practice. This paper
aims at contributing to the current discussion by highlighting the relevance of
statistical methodology in the context of AI development. In particular, we
discuss contributions of statistics to the field of artificial intelligence
concerning methodological development, planning and design of studies,
assessment of data quality and data collection, differentiation of causality
and associations and assessment of uncertainty in results. Moreover, the paper
also deals with the equally necessary and meaningful extension of curricula in
schools and universities. | [
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] |
Recent approaches for learning policies to improve caching, target just one
out of the prefetching, admission and eviction processes. In contrast, we
propose an end to end pipeline to learn all three policies using machine
learning. We also take inspiration from the success of pretraining on large
corpora to learn specialized embeddings for the task. We model prefetching as a
sequence prediction task based on past misses. Following previous works
suggesting that frequency and recency are the two orthogonal fundamental
attributes for caching, we use an online reinforcement learning technique to
learn the optimal policy distribution between two orthogonal eviction
strategies based on them. While previous approaches used the past as an
indicator of the future, we instead explicitly model the future frequency and
recency in a multi-task fashion with prefetching, leveraging the abilities of
deep networks to capture futuristic trends and use them for learning eviction
and admission. We also model the distribution of the data in an online fashion
using Kernel Density Estimation in our approach, to deal with the problem of
caching non-stationary data. We present our approach as a "proof of concept" of
learning all three components of cache strategies using machine learning and
leave improving practical deployment for future work. | [
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] |
Given access to accurate dynamical models, modern planning approaches are
effective in computing feasible and optimal plans for repetitive robotic tasks.
However, it is difficult to model the true dynamics of the real world before
execution, especially for tasks requiring interactions with objects whose
parameters are unknown. A recent planning approach, CMAX, tackles this problem
by adapting the planner online during execution to bias the resulting plans
away from inaccurately modeled regions. CMAX, while being provably guaranteed
to reach the goal, requires strong assumptions on the accuracy of the model
used for planning and fails to improve the quality of the solution over
repetitions of the same task. In this paper we propose CMAX++, an approach that
leverages real-world experience to improve the quality of resulting plans over
successive repetitions of a robotic task. CMAX++ achieves this by integrating
model-free learning using acquired experience with model-based planning using
the potentially inaccurate model. We provide provable guarantees on the
completeness and asymptotic convergence of CMAX++ to the optimal path cost as
the number of repetitions increases. CMAX++ is also shown to outperform
baselines in simulated robotic tasks including 3D mobile robot navigation where
the track friction is incorrectly modeled, and a 7D pick-and-place task where
the mass of the object is unknown leading to discrepancy between true and
modeled dynamics. | [
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] |
Answer Set Programming (ASP) is a declarative logic formalism that allows to
encode computational problems via logic programs. Despite the declarative
nature of the formalism, some advanced expertise is required, in general, for
designing an ASP encoding that can be efficiently evaluated by an actual ASP
system. A common way for trying to reduce the burden of manually tweaking an
ASP program consists in automatically rewriting the input encoding according to
suitable techniques, for producing alternative, yet semantically equivalent,
ASP programs. However, rewriting does not always grant benefits in terms of
performance; hence, proper means are needed for predicting their effects with
this respect. In this paper we describe an approach based on Machine Learning
(ML) to automatically decide whether to rewrite. In particular, given an ASP
program and a set of input facts, our approach chooses whether and how to
rewrite input rules based on a set of features measuring their structural
properties and domain information. To this end, a Multilayer Perceptrons model
has then been trained to guide the ASP grounder I-DLV on rewriting input rules.
We report and discuss the results of an experimental evaluation over a
prototypical implementation. | [
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] |
(This work has been submitted to the IEEE for possible publication. Copyright
may be transferred without notice, after which this version may no longer be
accessible.)
To improve the efficiency of deep reinforcement learning (DRL)-based methods
for robot manipulator trajectory planning in random working environments, we
present three dense reward functions. These rewards differ from the traditional
sparse reward. First, a posture reward function is proposed to speed up the
learning process with a more reasonable trajectory by modeling the distance and
direction constraints, which can reduce the blindness of exploration. Second, a
stride reward function is proposed to improve the stability of the learning
process by modeling the distance and movement distance of joint constraints.
Finally, in order to further improve learning efficiency, we are inspired by
the cognitive process of human behavior and propose a stage incentive
mechanism, including a hard stage incentive reward function and a soft stage
incentive reward function. Extensive experiments show that the soft stage
incentive reward function is able to improve the convergence rate by up to
46.9% with the state-of-the-art DRL methods. The percentage increase in the
convergence mean reward was 4.4-15.5% and the percentage decreases with respect
to standard deviation were 21.9-63.2%. In the evaluation experiments, the
success rate of trajectory planning for a robot manipulator reached 99.6%. | [
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] |
Screening mammograms are a routine imaging exam performed to detect breast
cancer in its early stages to reduce morbidity and mortality attributed to this
disease. In order to maximize the efficacy of breast cancer screening programs,
proper mammographic positioning is paramount. Proper positioning ensures
adequate visualization of breast tissue and is necessary for effective breast
cancer detection. Therefore, breast-imaging radiologists must assess each
mammogram for the adequacy of positioning before providing a final
interpretation of the examination; this often necessitates return patient
visits for additional imaging. In this paper, we propose a deep
learning-algorithm method that mimics and automates this decision-making
process to identify poorly positioned mammograms. Our objective for this
algorithm is to assist mammography technologists in recognizing inadequately
positioned mammograms real-time, improve the quality of mammographic
positioning and performance, and ultimately reducing repeat visits for patients
with initially inadequate imaging. The proposed model showed a true positive
rate for detecting correct positioning of 91.35% in the mediolateral oblique
view and 95.11% in the craniocaudal view. In addition to these results, we also
present an automatically generated report which can aid the mammography
technologist in taking corrective measures during the patient visit. | [
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] |
In supervised learning, training and test datasets are often sampled from
distinct distributions. Domain adaptation techniques are thus required.
Covariate shift adaptation yields good generalization performance when domains
differ only by the marginal distribution of features. Covariate shift
adaptation is usually implemented using importance weighting, which may fail,
according to common wisdom, due to small effective sample sizes (ESS). Previous
research argues this scenario is more common in high-dimensional settings.
However, how effective sample size, dimensionality, and model
performance/generalization are formally related in supervised learning,
considering the context of covariate shift adaptation, is still somewhat
obscure in the literature. Thus, a main challenge is presenting a unified
theory connecting those points. Hence, in this paper, we focus on building a
unified view connecting the ESS, data dimensionality, and generalization in the
context of covariate shift adaptation. Moreover, we also demonstrate how
dimensionality reduction or feature selection can increase the ESS, and argue
that our results support dimensionality reduction before covariate shift
adaptation as a good practice. | [
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] |
Large content providers and content distribution network operators usually
connect with large Internet service providers (eyeball networks) through
dedicated private peering. The capacity of these private network interconnects
is provisioned to match the volume of the real content demand by the users.
Unfortunately, in case of a surge in traffic demand, for example due to a
content trending in a certain country, the capacity of the private interconnect
may deplete and the content provider/distributor would have to reroute the
excess traffic through transit providers. Although, such overflow events are
rare, they have significant negative impacts on content providers, Internet
service providers, and end-users. These include unexpected delays and
disruptions reducing the user experience quality, as well as direct costs paid
by the Internet service provider to the transit providers. If the traffic
overflow events could be predicted, the Internet service providers would be
able to influence the routes chosen for the excess traffic to reduce the costs
and increase user experience quality. In this article we propose a method based
on an ensemble of deep learning models to predict overflow events over a short
term horizon of 2-6 hours and predict the specific interconnections that will
ingress the overflow traffic. The method was evaluated with 2.5 years' traffic
measurement data from a large European Internet service provider resulting in a
true-positive rate of 0.8 while maintaining a 0.05 false-positive rate. The
lockdown imposed by the COVID-19 pandemic reduced the overflow prediction
accuracy. Nevertheless, starting from the end of April 2020 with the gradual
lockdown release, the old models trained before the pandemic perform equally
well. | [
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Explainability has been a goal for Artificial Intelligence (AI) systems since
their conception, with the need for explainability growing as more complex AI
models are increasingly used in critical, high-stakes settings such as
healthcare. Explanations have often added to an AI system in a non-principled,
post-hoc manner. With greater adoption of these systems and emphasis on
user-centric explainability, there is a need for a structured representation
that treats explainability as a primary consideration, mapping end user needs
to specific explanation types and the system's AI capabilities. We design an
explanation ontology to model both the role of explanations, accounting for the
system and user attributes in the process, and the range of different
literature-derived explanation types. We indicate how the ontology can support
user requirements for explanations in the domain of healthcare. We evaluate our
ontology with a set of competency questions geared towards a system designer
who might use our ontology to decide which explanation types to include, given
a combination of users' needs and a system's capabilities, both in system
design settings and in real-time operations. Through the use of this ontology,
system designers will be able to make informed choices on which explanations AI
systems can and should provide. | [
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] |
Similarity analysis is one of the crucial steps in most fMRI studies.
Representational Similarity Analysis (RSA) can measure similarities of neural
signatures generated by different cognitive states. This paper develops Deep
Representational Similarity Learning (DRSL), a deep extension of RSA that is
appropriate for analyzing similarities between various cognitive tasks in fMRI
datasets with a large number of subjects, and high-dimensionality -- such as
whole-brain images. Unlike the previous methods, DRSL is not limited by a
linear transformation or a restricted fixed nonlinear kernel function -- such
as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping
neural responses to linear space, where this network can implement a customized
nonlinear transformation for each subject separately. Furthermore, utilizing a
gradient-based optimization in DRSL can significantly reduce runtime of
analysis on large datasets because it uses a batch of samples in each iteration
rather than all neural responses to find an optimal solution. Empirical studies
on multi-subject fMRI datasets with various tasks -- including visual stimuli,
decision making, flavor, and working memory -- confirm that the proposed method
achieves superior performance to other state-of-the-art RSA algorithms. | [
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] |
Techniques to discover Petri nets from event data assume precisely one case
identifier per event. These case identifiers are used to correlate events, and
the resulting discovered Petri net aims to describe the life-cycle of
individual cases. In reality, there is not one possible case notion, but
multiple intertwined case notions. For example, events may refer to mixtures of
orders, items, packages, customers, and products. A package may refer to
multiple items, multiple products, one order, and one customer. Therefore, we
need to assume that each event refers to a collection of objects, each having a
type (instead of a single case identifier). Such object-centric event logs are
closer to data in real-life information systems. From an object-centric event
log, we want to discover an object-centric Petri net with places that
correspond to object types and transitions that may consume and produce
collections of objects of different types. Object-centric Petri nets visualize
the complex relationships among objects from different types. This paper
discusses a novel process discovery approach implemented in PM4Py. As will be
demonstrated, it is indeed feasible to discover holistic process models that
can be used to drill-down into specific viewpoints if needed. | [
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An effective approach to exploration in reinforcement learning is to rely on
an agent's uncertainty over the optimal policy, which can yield near-optimal
exploration strategies in tabular settings. However, in non-tabular settings
that involve function approximators, obtaining accurate uncertainty estimates
is almost as challenging a problem. In this paper, we highlight that value
estimates are easily biased and temporally inconsistent. In light of this, we
propose a novel method for estimating uncertainty over the value function that
relies on inducing a distribution over temporal difference errors. This
exploration signal controls for state-action transitions so as to isolate
uncertainty in value that is due to uncertainty over the agent's parameters.
Because our measure of uncertainty conditions on state-action transitions, we
cannot act on this measure directly. Instead, we incorporate it as an intrinsic
reward and treat exploration as a separate learning problem, induced by the
agent's temporal difference uncertainties. We introduce a distinct exploration
policy that learns to collect data with high estimated uncertainty, which gives
rise to a curriculum that smoothly changes throughout learning and vanishes in
the limit of perfect value estimates. We evaluate our method on hard
exploration tasks, including Deep Sea and Atari 2600 environments and find that
our proposed form of exploration facilitates both diverse and deep exploration. | [
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] |
Software often produces biased outputs. In particular, machine learning (ML)
based software are known to produce erroneous predictions when processing
discriminatory inputs. Such unfair program behavior can be caused by societal
bias. In the last few years, Amazon, Microsoft and Google have provided
software services that produce unfair outputs, mostly due to societal bias
(e.g. gender or race). In such events, developers are saddled with the task of
conducting fairness testing. Fairness testing is challenging; developers are
tasked with generating discriminatory inputs that reveal and explain biases.
We propose a grammar-based fairness testing approach (called ASTRAEA) which
leverages context-free grammars to generate discriminatory inputs that reveal
fairness violations in software systems. Using probabilistic grammars, ASTRAEA
also provides fault diagnosis by isolating the cause of observed software bias.
ASTRAEA's diagnoses facilitate the improvement of ML fairness.
ASTRAEA was evaluated on 18 software systems that provide three major natural
language processing (NLP) services. In our evaluation, ASTRAEA generated
fairness violations with a rate of ~18%. ASTRAEA generated over 573K
discriminatory test cases and found over 102K fairness violations. Furthermore,
ASTRAEA improves software fairness by ~76%, via model-retraining. | [
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] |
We propose a method for improving adversarial robustness by addition of a new
bounded function just before softmax. Recent studies hypothesize that small
logits (inputs of softmax) by logit regularization can improve adversarial
robustness of deep learning. Following this hypothesis, we analyze norms of
logit vectors at the optimal point under the assumption of universal
approximation and explore new methods for constraining logits by addition of a
bounded function before softmax. We theoretically and empirically reveal that
small logits by addition of a common activation function, e.g., hyperbolic
tangent, do not improve adversarial robustness since input vectors of the
function (pre-logit vectors) can have large norms. From the theoretical
findings, we develop the new bounded function. The addition of our function
improves adversarial robustness because it makes logit and pre-logit vectors
have small norms. Since our method only adds one activation function before
softmax, it is easy to combine our method with adversarial training. Our
experiments demonstrate that our method is comparable to logit regularization
methods in terms of accuracies on adversarially perturbed datasets without
adversarial training. Furthermore, it is superior or comparable to logit
regularization methods and a recent defense method (TRADES) when using
adversarial training. | [
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The tremendous progress of autoencoders and generative adversarial networks
(GANs) has led to their application to multiple critical tasks, such as fraud
detection and sanitized data generation. This increasing adoption has fostered
the study of security and privacy risks stemming from these models. However,
previous works have mainly focused on membership inference attacks. In this
work, we explore one of the most severe attacks against machine learning
models, namely the backdoor attack, against both autoencoders and GANs. The
backdoor attack is a training time attack where the adversary implements a
hidden backdoor in the target model that can only be activated by a secret
trigger. State-of-the-art backdoor attacks focus on classification-based tasks.
We extend the applicability of backdoor attacks to autoencoders and GAN-based
models. More concretely, we propose the first backdoor attack against
autoencoders and GANs where the adversary can control what the decoded or
generated images are when the backdoor is activated. Our results show that the
adversary can build a backdoored autoencoder that returns a target output for
all backdoored inputs, while behaving perfectly normal on clean inputs.
Similarly, for the GANs, our experiments show that the adversary can generate
data from a different distribution when the backdoor is activated, while
maintaining the same utility when the backdoor is not. | [
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] |
Representation Learning in a heterogeneous space with mixed variables of
numerical and categorical types has interesting challenges due to its complex
feature manifold. Moreover, feature learning in an unsupervised setup, without
class labels and a suitable learning loss function, adds to the problem
complexity. Further, the learned representation and subsequent predictions
should not reflect discriminatory behavior towards certain sensitive groups or
attributes. The proposed feature map should preserve maximum variations present
in the data and needs to be fair with respect to the sensitive variables. We
propose, in the first phase of our work, an efficient encoder-decoder framework
to capture the mixed-domain information. The second phase of our work focuses
on de-biasing the mixed space representations by adding relevant fairness
constraints. This ensures minimal information loss between the representations
before and after the fairness-preserving projections. Both the information
content and the fairness aspect of the final representation learned has been
validated through several metrics where it shows excellent performance. Our
work (FairMixRep) addresses the problem of Mixed Space Fair Representation
learning from an unsupervised perspective and learns a Universal representation
that is timely, unique, and a novel research contribution. | [
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] |
Adversarial training and its variants have become de facto standards for
learning robust deep neural networks. In this paper, we explore the landscape
around adversarial training in a bid to uncover its limits. We systematically
study the effect of different training losses, model sizes, activation
functions, the addition of unlabeled data (through pseudo-labeling) and other
factors on adversarial robustness. We discover that it is possible to train
robust models that go well beyond state-of-the-art results by combining larger
models, Swish/SiLU activations and model weight averaging. We demonstrate large
improvements on CIFAR-10 and CIFAR-100 against $\ell_\infty$ and $\ell_2$
norm-bounded perturbations of size $8/255$ and $128/255$, respectively. In the
setting with additional unlabeled data, we obtain an accuracy under attack of
65.88% against $\ell_\infty$ perturbations of size $8/255$ on CIFAR-10 (+6.35%
with respect to prior art). Without additional data, we obtain an accuracy
under attack of 57.20% (+3.46%). To test the generality of our findings and
without any additional modifications, we obtain an accuracy under attack of
80.53% (+7.62%) against $\ell_2$ perturbations of size $128/255$ on CIFAR-10,
and of 36.88% (+8.46%) against $\ell_\infty$ perturbations of size $8/255$ on
CIFAR-100. All models are available at
https://github.com/deepmind/deepmind-research/tree/master/adversarial_robustness. | [
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Reliable bipedal walking over complex terrain is a challenging problem, using
a curriculum can help learning. Curriculum learning is the idea of starting
with an achievable version of a task and increasing the difficulty as a success
criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement
Learning policies for bipedal walking over various challenging terrains. In the
first stage, the agent starts on an easy terrain and the terrain difficulty is
gradually increased, while forces derived from a target policy are applied to
the robot joints and the base. In the second stage, the guiding forces are
gradually reduced to zero. Finally, in the third stage, random perturbations
with increasing magnitude are applied to the robot base, so the robustness of
the policies are improved. In simulation experiments, we show that our approach
is effective in learning walking policies, separate from each other, for five
terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate
that in the absence of human demonstrations, a simple hand designed walking
trajectory is a sufficient prior to learn to traverse complex terrain types. In
ablation studies, we show that taking out any one of the three stages of the
curriculum degrades the learning performance. | [
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] |
Software development is becoming increasingly open and collaborative with the
advent of platforms such as GitHub. Given its crucial role, there is a need to
better understand and model the dynamics of GitHub as a social platform.
Previous work has mostly considered the dynamics of traditional social
networking sites like Twitter and Facebook. We propose GitEvolve, a system to
predict the evolution of GitHub repositories and the different ways by which
users interact with them. To this end, we develop an end-to-end multi-task
sequential deep neural network that given some seed events, simultaneously
predicts which user-group is next going to interact with a given repository,
what the type of the interaction is, and when it happens. To facilitate
learning, we use graph based representation learning to encode relationship
between repositories. We map users to groups by modelling common interests to
better predict popularity and to generalize to unseen users during inference.
We introduce an artificial event type to better model varying levels of
activity of repositories in the dataset. The proposed multi-task architecture
is generic and can be extended to model information diffusion in other social
networks. In a series of experiments, we demonstrate the effectiveness of the
proposed model, using multiple metrics and baselines. Qualitative analysis of
the model's ability to predict popularity and forecast trends proves its
applicability. | [
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Speaker recognition is an active research area that contains notable usage in
biometric security and authentication system. Currently, there exist many
well-performing models in the speaker recognition domain. However, most of the
advanced models implement deep learning that requires GPU support for real-time
speech recognition, and it is not suitable for low-end devices. In this paper,
we propose a lightweight text-independent speaker recognition model based on
random forest classifier. It also introduces new features that are used for
both speaker verification and identification tasks. The proposed model uses
human speech based timbral properties as features that are classified using
random forest. Timbre refers to the very basic properties of sound that allow
listeners to discriminate among them. The prototype uses seven most actively
searched timbre properties, boominess, brightness, depth, hardness, roughness,
sharpness, and warmth as features of our speaker recognition model. The
experiment is carried out on speaker verification and speaker identification
tasks and shows the achievements and drawbacks of the proposed model. In the
speaker identification phase, it achieves a maximum accuracy of 78%. On the
contrary, in the speaker verification phase, the model maintains an accuracy of
80% having an equal error rate (ERR) of 0.24. | [
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The transformer is the most critical algorithm innovation of the Nature
Language Processing (NLP) field in recent years. Unlike the Recurrent Neural
Network (RNN) models, Transformers can process on dimensions of sequence
lengths in parallel, therefore leading to better accuracy on long sequences.
However, efficient deployments of them for online services in data centers
equipped with GPUs are not easy. First, more computation introduced by
transformer structures makes it more challenging to meet the latency and
throughput constraints of serving. Second, NLP tasks take in sentences of
variable length. The variability of input dimensions brings a severe problem to
efficient memory management and serving optimization.
This paper designed a transformer serving system called TurboTransformers,
which consists of a computing runtime and a serving framework to solve the
above challenges. Three innovative features make it stand out from other
similar works. An efficient parallel algorithm is proposed for GPU-based batch
reduction operations, like Softmax and LayerNorm, major hot spots besides BLAS
routines. A memory allocation algorithm, which better balances the memory
footprint and allocation/free efficiency, is designed for variable-length input
situations. A serving framework equipped with a new batch scheduler using
dynamic programming achieves the optimal throughput on variable-length
requests. The system can achieve the state-of-the-art transformer model serving
performance on GPU platforms and can be seamlessly integrated into your PyTorch
code with a few lines of code. | [
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] |
Deep neural networks are widely used in personalized recommendation systems.
Unlike regular DNN inference workloads, recommendation inference is
memory-bound due to the many random memory accesses needed to lookup the
embedding tables. The inference is also heavily constrained in terms of latency
because producing a recommendation for a user must be done in about tens of
milliseconds. In this paper, we propose MicroRec, a high-performance inference
engine for recommendation systems. MicroRec accelerates recommendation
inference by (1) redesigning the data structures involved in the embeddings to
reduce the number of lookups needed and (2) taking advantage of the
availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the
latency by enabling parallel lookups. We have implemented the resulting design
on an FPGA board including the embedding lookup step as well as the complete
inference process. Compared to the optimized CPU baseline (16 vCPU,
AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone
and 2.5$~5.4x speedup for the entire recommendation inference in terms of
throughput. As for latency, CPU-based engines needs milliseconds for inferring
a recommendation while MicroRec only takes microseconds, a significant
advantage in real-time recommendation systems. | [
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Drive-thru is a popular sales channel in the fast food industry where
consumers can make food purchases without leaving their cars. Drive-thru
recommendation systems allow restaurants to display food recommendations on the
digital menu board as guests are making their orders. Popular recommendation
models in eCommerce scenarios rely on user attributes (such as user profiles or
purchase history) to generate recommendations, while such information is hard
to obtain in the drive-thru use case. Thus, in this paper, we propose a new
recommendation model Transformer Cross Transformer (TxT), which exploits the
guest order behavior and contextual features (such as location, time, and
weather) using Transformer encoders for drive-thru recommendations. Empirical
results show that our TxT model achieves superior results in Burger King's
drive-thru production environment compared with existing recommendation
solutions. In addition, we implement a unified system to run end-to-end big
data analytics and deep learning workloads on the same cluster. We find that in
practice, maintaining a single big data cluster for the entire pipeline is more
efficient and cost-saving. Our recommendation system is not only beneficial for
drive-thru scenarios, and it can also be generalized to other customer
interaction channels. | [
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] |
Application markets provide a communication channel between app developers
and their end-users in form of app reviews, which allow users to provide
feedback about the apps. Although security and privacy in mobile apps are one
of the biggest issues, it is unclear how much people are aware of these or
discuss them in reviews.
In this study, we explore the privacy and security concerns of users using
reviews in the Google Play Store. For this, we conducted a study by analyzing
around 2.2M reviews from the top 539 apps of this Android market. We found that
0.5\% of these reviews are related to the security and privacy concerns of the
users. We further investigated these apps by performing dynamic analysis which
provided us valuable insights into their actual behaviors. Based on the
different perspectives, we categorized the apps and evaluated how the different
factors influence the users' perception of the apps. It was evident from the
results that the number of permissions that the apps request plays a dominant
role in this matter. We also found that sending out the location can affect the
users' thoughts about the app. The other factors do not directly affect the
privacy and security concerns for the users. | [
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] |
Social insect colonies routinely face large vertebrate predators, against
which they need to mount a collective defense. To do so, honeybees use an alarm
pheromone that recruits nearby bees into mass stinging of the perceived threat.
This alarm pheromone is carried directly on the stinger, hence its
concentration builds up during the course of the attack. Here, we investigate
how individual bees react to different alarm pheromone concentrations, and how
this evolved response-pattern leads to better coordination at the group level.
We first present an individual dose-response curve to the alarm pheromone,
obtained experimentally. Second, we apply Projective Simulation to model each
bee as an artificial learning agent that relies on the pheromone concentration
to decide whether to sting or not. If the emergent collective performance
benefits the colony, the individual reactions that led to it are enhanced via
reinforcement learning, thus emulating natural selection. Predators are modeled
in a realistic way so that the effect of factors such as their resistance,
their killing rate or their frequency of attacks can be studied. We are able to
reproduce the experimentally measured response-pattern of real bees, and to
identify the main selection pressures that shaped it. Finally, we apply the
model to a case study: by tuning the parameters to represent the environmental
conditions of European or African bees, we can predict the difference in
aggressiveness observed between these two subspecies. | [
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] |
Probabilistic forecasting consists in predicting a distribution of possible
future outcomes. In this paper, we address this problem for non-stationary time
series, which is very challenging yet crucially important. We introduce the
STRIPE model for representing structured diversity based on shape and time
features, ensuring both probable predictions while being sharp and accurate.
STRIPE is agnostic to the forecasting model, and we equip it with a
diversification mechanism relying on determinantal point processes (DPP). We
introduce two DPP kernels for modeling diverse trajectories in terms of shape
and time, which are both differentiable and proved to be positive
semi-definite. To have an explicit control on the diversity structure, we also
design an iterative sampling mechanism to disentangle shape and time
representations in the latent space. Experiments carried out on synthetic
datasets show that STRIPE significantly outperforms baseline methods for
representing diversity, while maintaining accuracy of the forecasting model. We
also highlight the relevance of the iterative sampling scheme and the
importance to use different criteria for measuring quality and diversity.
Finally, experiments on real datasets illustrate that STRIPE is able to
outperform state-of-the-art probabilistic forecasting approaches in the best
sample prediction. | [
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] |
For in-vehicle application,task type and vehicle state information, i.e.,
vehicle speed, bear a significant impact on the task delay requirement.
However, the joint impact of task type and vehicle speed on the task delay
constraint has not been studied, and this lack of study may cause a mismatch
between the requirement of the task delay and allocated computation and
wireless resources. In this paper, we propose a joint task type and vehicle
speed-aware task offloading and resource allocation strategy to decrease the
vehicl's energy cost for executing tasks and increase the revenue of the
vehicle for processing tasks within the delay constraint. First, we establish
the joint task type and vehicle speed-aware delay constraint model. Then, the
delay, energy cost and revenue for task execution in the vehicular edge
computing (VEC) server, local terminal and terminals of other vehicles are
calculated. Based on the energy cost and revenue from task execution,the
utility function of the vehicle is acquired. Next, we formulate a joint
optimization of task offloading and resource allocation to maximize the utility
level of the vehicles subject to the constraints of task delay, computation
resources and wireless resources. To obtain a near-optimal solution of the
formulated problem, a joint offloading and resource allocation based on the
multi-agent deep deterministic policy gradient (JORA-MADDPG) algorithm is
proposed to maximize the utility level of vehicles. Simulation results show
that our algorithm can achieve superior performance in task completion delay,
vehicles' energy cost and processing revenue. | [
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] |
Despite the many attempts and approaches for anomaly detection explored over
the years, the automatic detection of rare events in data communication
networks remains a complex problem. In this paper we introduce Net-GAN, a novel
approach to network anomaly detection in time-series, using recurrent neural
networks (RNNs) and generative adversarial networks (GAN). Different from the
state of the art, which traditionally focuses on univariate measurements,
Net-GAN detects anomalies in multivariate time-series, exploiting temporal
dependencies through RNNs. Net-GAN discovers the underlying distribution of the
baseline, multivariate data, without making any assumptions on its nature,
offering a powerful approach to detect anomalies in complex, difficult to model
network monitoring data. We further exploit the concepts behind generative
models to conceive Net-VAE, a complementary approach to Net-GAN for network
anomaly detection, based on variational auto-encoders (VAE). We evaluate
Net-GAN and Net-VAE in different monitoring scenarios, including anomaly
detection in IoT sensor data, and intrusion detection in network measurements.
Generative models represent a promising approach for network anomaly detection,
especially when considering the complexity and ever-growing number of
time-series to monitor in operational networks. | [
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] |
Constrained motion planning is a challenging field of research, aiming for
computationally efficient methods that can find a collision-free path on the
constraint manifolds between a given start and goal configuration. These
planning problems come up surprisingly frequently, such as in robot
manipulation for performing daily life assistive tasks. However, few solutions
to constrained motion planning are available, and those that exist struggle
with high computational time complexity in finding a path solution on the
manifolds. To address this challenge, we present Constrained Motion Planning
Networks X (CoMPNetX). It is a neural planning approach, comprising a
conditional deep neural generator and discriminator with neural gradients-based
fast projection operator. We also introduce neural task and scene
representations conditioned on which the CoMPNetX generates implicit manifold
configurations to turbo-charge any underlying classical planner such as
Sampling-based Motion Planning methods for quickly solving complex constrained
planning tasks. We show that our method finds path solutions with high success
rates and lower computation times than state-of-the-art traditional
path-finding tools on various challenging scenarios. | [
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] |
Question Answering (QA) is a widely-used framework for developing and
evaluating an intelligent machine. In this light, QA on Electronic Health
Records (EHR), namely EHR QA, can work as a crucial milestone towards
developing an intelligent agent in healthcare. EHR data are typically stored in
a relational database, which can also be converted to a directed acyclic graph,
allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based
QA. We hypothesize that the graph-based approach is more suitable for EHR QA as
graphs can represent relations between entities and values more naturally
compared to tables, which essentially require JOIN operations. In this paper,
we propose a graph-based EHR QA where natural language queries are converted to
SPARQL instead of SQL. To validate our hypothesis, we create four EHR QA
datasets (graph-based VS table-based, and simplified database schema VS
original database schema), based on a table-based dataset MIMICSQL. We test
both a simple Seq2Seq model and a state-of-the-art EHR QA model on all datasets
where the graph-based datasets facilitated up to 34% higher accuracy than the
table-based dataset without any modification to the model architectures.
Finally, all datasets are open-sourced to encourage further EHR QA research in
both directions. | [
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] |
Central to the design of many robot systems and their controllers is solving
a constrained blackbox optimization problem. This paper presents CNMA, a new
method of solving this problem that is conservative in the number of
potentially expensive blackbox function evaluations; allows specifying complex,
even recursive constraints directly rather than as hard-to-design penalty or
barrier functions; and is resilient to the non-termination of function
evaluations. CNMA leverages the ability of neural networks to approximate any
continuous function, their transformation into equivalent mixed integer linear
programs (MILPs) and their optimization subject to constraints with industrial
strength MILP solvers. A new learning-from-failure step guides the learning to
be relevant to solving the constrained optimization problem. Thus, the amount
of learning is orders of magnitude smaller than that needed to learn functions
over their entire domains. CNMA is illustrated with the design of several
robotic systems: wave-energy propelled boat, lunar lander, hexapod, cartpole,
acrobot and parallel parking. These range from 6 real-valued dimensions to 36.
We show that CNMA surpasses the Nelder-Mead, Gaussian and Random Search
optimization methods against the metric of number of function evaluations. | [
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] |
Intelligent vehicular systems and smart city applications are the fastest
growing Internet of things (IoT) implementations at a compound annual growth
rate of 30%. In view of the recent advances in IoT devices and the emerging new
breed of IoT applications driven by artificial intelligence (AI), fog radio
access network (F-RAN) has been recently introduced for the fifth generation
(5G) wireless communications to overcome the latency limitations of cloud-RAN
(C-RAN). We consider the network slicing problem of allocating the limited
resources at the network edge (fog nodes) to vehicular and smart city users
with heterogeneous latency and computing demands in dynamic environments. We
develop a network slicing model based on a cluster of fog nodes (FNs)
coordinated with an edge controller (EC) to efficiently utilize the limited
resources at the network edge. For each service request in a cluster, the EC
decides which FN to execute the task, i.e., locally serve the request at the
edge, or to reject the task and refer it to the cloud. We formulate the problem
as infinite-horizon Markov decision process (MDP) and propose a deep
reinforcement learning (DRL) solution to adaptively learn the optimal slicing
policy. The performance of the proposed DRL-based slicing method is evaluated
by comparing it with other slicing approaches in dynamic environments and for
different scenarios of design objectives. Comprehensive simulation results
corroborate that the proposed DRL-based EC quickly learns the optimal policy
through interaction with the environment, which enables adaptive and automated
network slicing for efficient resource allocation in dynamic vehicular and
smart city environments. | [
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] |
Emotions are an inseparable part of human nature affecting our behavior in
response to the outside world. Although most empirical studies have been
dominated by two theoretical models including discrete categories of emotion
and dichotomous dimensions, results from neuroscience approaches suggest a
multi-processes mechanism underpinning emotional experience with a large
overlap across different emotions. While these findings are consistent with the
influential theories of emotion in psychology that emphasize a role for
multiple component processes to generate emotion episodes, few studies have
systematically investigated the relationship between discrete emotions and a
full componential view. This paper applies a componential framework with a
data-driven approach to characterize emotional experiences evoked during movie
watching. The results suggest that differences between various emotions can be
captured by a few (at least 6) latent dimensions, each defined by features
associated with component processes, including appraisal, expression,
physiology, motivation, and feeling. In addition, the link between discrete
emotions and component model is explored and results show that a componential
model with a limited number of descriptors is still able to predict the level
of experienced discrete emotion(s) to a satisfactory level. Finally, as
appraisals may vary according to individual dispositions and biases, we also
study the relationship between personality traits and emotions in our
computational framework and show that the role of personality on discrete
emotion differences can be better justified using the component model. | [
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] |
One of the fundamental problems in Artificial Intelligence is to perform
complex multi-hop logical reasoning over the facts captured by a knowledge
graph (KG). This problem is challenging, because KGs can be massive and
incomplete. Recent approaches embed KG entities in a low dimensional space and
then use these embeddings to find the answer entities. However, it has been an
outstanding challenge of how to handle arbitrary first-order logic (FOL)
queries as present methods are limited to only a subset of FOL operators. In
particular, the negation operator is not supported. An additional limitation of
present methods is also that they cannot naturally model uncertainty. Here, we
present BetaE, a probabilistic embedding framework for answering arbitrary FOL
queries over KGs. BetaE is the first method that can handle a complete set of
first-order logical operations: conjunction ($\wedge$), disjunction ($\vee$),
and negation ($\neg$). A key insight of BetaE is to use probabilistic
distributions with bounded support, specifically the Beta distribution, and
embed queries/entities as distributions, which as a consequence allows us to
also faithfully model uncertainty. Logical operations are performed in the
embedding space by neural operators over the probabilistic embeddings. We
demonstrate the performance of BetaE on answering arbitrary FOL queries on
three large, incomplete KGs. While being more general, BetaE also increases
relative performance by up to 25.4% over the current state-of-the-art KG
reasoning methods that can only handle conjunctive queries without negation. | [
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] |
Deep reinforcement learning (RL) agents are able to learn contact-rich
manipulation tasks by maximizing a reward signal, but require large amounts of
experience, especially in environments with many obstacles that complicate
exploration. In contrast, motion planners use explicit models of the agent and
environment to plan collision-free paths to faraway goals, but suffer from
inaccurate models in tasks that require contacts with the environment. To
combine the benefits of both approaches, we propose motion planner augmented RL
(MoPA-RL) which augments the action space of an RL agent with the long-horizon
planning capabilities of motion planners. Based on the magnitude of the action,
our approach smoothly transitions between directly executing the action and
invoking a motion planner. We evaluate our approach on various simulated
manipulation tasks and compare it to alternative action spaces in terms of
learning efficiency and safety. The experiments demonstrate that MoPA-RL
increases learning efficiency, leads to a faster exploration, and results in
safer policies that avoid collisions with the environment. Videos and code are
available at https://clvrai.com/mopa-rl . | [
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] |
For a successful business, engaging in an effective campaign is a key task
for marketers. Most previous studies used various mathematical models to
segment customers without considering the correlation between customer
segmentation and a campaign. This work presents a conceptual model by studying
the significant campaign-dependent variables of customer targeting in customer
segmentation context. In this way, the processes of customer segmentation and
targeting thus can be linked and solved together. The outcomes of customer
segmentation of this study could be more meaningful and relevant for marketers.
This investigation applies a customer life time value (LTV) model to assess the
fitness between targeted customer groups and marketing strategies. To integrate
customer segmentation and customer targeting, this work uses the genetic
algorithm (GA) to determine the optimized marketing strategy. Later, we suggest
using C&RT (Classification and Regression Tree) in SPSS PASW Modeler as the
replacement to Genetic Algorithm technique to accomplish these results. We also
suggest using LOSSYCOUNTING and Counting Bloom Filter to dynamically design the
right and up-to-date offer to the right customer. | [
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] |
As more and more autonomous vehicles (AVs) are being deployed on public
roads, designing socially compatible behaviors for them is becoming
increasingly important. In order to generate safe and efficient actions, AVs
need to not only predict the future behaviors of other traffic participants,
but also be aware of the uncertainties associated with such behavior
prediction. In this paper, we propose an uncertain-aware integrated prediction
and planning (UAPP) framework. It allows the AVs to infer the characteristics
of other road users online and generate behaviors optimizing not only their own
rewards, but also their courtesy to others, and their confidence regarding the
prediction uncertainties. We first propose the definitions for courtesy and
confidence. Based on that, their influences on the behaviors of AVs in
interactive driving scenarios are explored. Moreover, we evaluate the proposed
algorithm on naturalistic human driving data by comparing the generated
behavior against ground truth. Results show that the online inference can
significantly improve the human-likeness of the generated behaviors.
Furthermore, we find that human drivers show great courtesy to others, even for
those without right-of-way. We also find that such driving preferences vary
significantly in different cultures. | [
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] |
The introduction of unexpected system disturbances and new system dynamics
does not allow guaranteed continuous system stability. In this research we
present a novel approach for detecting early failure indicators of non-linear
highly chaotic system and accordingly predict the best parameter calibrations
to offset such instability using deep machine learning regression model. The
approach proposed continuously monitors the system and controller signals. The
Re-calibration of the system and controller parameters is triggered according
to a set of conditions designed to maintain system stability without compromise
to the system speed, intended outcome or required processing power. The deep
neural model predicts the parameter values that would best counteract the
expected system in-stability. To demonstrate the effectiveness of the proposed
approach, it is applied to the non-linear complex combination of Duffing Van
der pol oscillators. The approach is also tested under different scenarios the
system and controller parameters are initially chosen incorrectly or the system
parameters are changed while running or new system dynamics are introduced
while running to measure effectiveness and reaction time. | [
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] |
Earlier work has shown that reusing experience from prior motion planning
problems can improve the efficiency of similar, future motion planning queries.
However, for robots with many degrees-of-freedom, these methods exhibit poor
generalization across different environments and often require large datasets
that are impractical to gather. We present SPARK and FLAME , two
experience-based frameworks for sampling-based planning applicable to complex
manipulators in 3 D environments. Both combine samplers associated with
features from a workspace decomposition into a global biased sampling
distribution. SPARK decomposes the environment based on exact geometry while
FLAME is more general, and uses an octree-based decomposition obtained from
sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch
robot tasked with challenging pick-and-place manipulation problems. Our
approaches can be trained incrementally and significantly improve performance
with only a handful of examples, generalizing better over diverse tasks and
environments as compared to prior approaches. | [
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] |
The main novelty of the proposed approach is that it allows a robot to learn
an end-to-end policy which can adapt to changes in the environment during
execution. While goal conditioning of policies has been studied in the RL
literature, such approaches are not easily extended to cases where the robot's
goal can change during execution. This is something that humans are naturally
able to do. However, it is difficult for robots to learn such reflexes (i.e.,
to naturally respond to dynamic environments), especially when the goal
location is not explicitly provided to the robot, and instead needs to be
perceived through a vision sensor. In the current work, we present a method
that can achieve such behavior by combining traditional kinematic planning,
deep learning, and deep reinforcement learning in a synergistic fashion to
generalize to arbitrary environments. We demonstrate the proposed approach for
several reaching and pick-and-place tasks in simulation, as well as on a real
system of a 6-DoF industrial manipulator. A video describing our work could be
found \url{https://youtu.be/hE-Ew59GRPQ}. | [
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Legged robots often use separate control policiesthat are highly engineered
for traversing difficult terrain suchas stairs, gaps, and steps, where
switching between policies isonly possible when the robot is in a region that
is commonto adjacent controllers. Deep Reinforcement Learning (DRL)is a
promising alternative to hand-crafted control design,though typically requires
the full set of test conditions to beknown before training. DRL policies can
result in complex(often unrealistic) behaviours that have few or no
overlappingregions between adjacent policies, making it difficult to
switchbehaviours. In this work we develop multiple DRL policieswith Curriculum
Learning (CL), each that can traverse asingle respective terrain condition,
while ensuring an overlapbetween policies. We then train a network for each
destinationpolicy that estimates the likelihood of successfully switchingfrom
any other policy. We evaluate our switching methodon a previously unseen
combination of terrain artifacts andshow that it performs better than heuristic
methods. Whileour method is trained on individual terrain types, it
performscomparably to a Deep Q Network trained on the full set ofterrain
conditions. This approach allows the development ofseparate policies in
constrained conditions with embedded priorknowledge about each behaviour, that
is scalable to any numberof behaviours, and prepares DRL methods for
applications inthe real world | [
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] |
Proper training and analytics in eSports require accurately collected and
annotated data. Most eSports research focuses exclusively on in-game data
analysis, and there is a lack of prior work involving eSports athletes'
psychophysiological data. In this paper, we present a dataset collected from
professional and amateur teams in 22 matches in League of Legends video game
with more than 40 hours of recordings. Recorded data include the players'
physiological activity, e.g. movements, pulse, saccades, obtained from various
sensors, self-reported aftermatch survey, and in-game data. An important
feature of the dataset is simultaneous data collection from five players, which
facilitates the analysis of sensor data on a team level. Upon the collection of
dataset we carried out its validation. In particular, we demonstrate that
stress and concentration levels for professional players are less correlated,
meaning more independent playstyle. Also, we show that the absence of team
communication does not affect the professional players as much as amateur ones.
To investigate other possible use cases of the dataset, we have trained
classical machine learning algorithms for skill prediction and player
re-identification using 3-minute sessions of sensor data. Best models achieved
0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation set
for skill prediction and player re-id problems, respectively. The dataset is
available at https://github.com/smerdov/eSports Sensors Dataset. | [
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] |
The Visibility-based Persistent Monitoring (VPM) problem seeks to find a set
of trajectories (or controllers) for robots to persistently monitor a changing
environment. Each robot has a sensor, such as a camera, with a limited
field-of-view that is obstructed by obstacles in the environment. The robots
may need to coordinate with each other to ensure no point in the environment is
left unmonitored for long periods of time. We model the problem such that there
is a penalty that accrues every time step if a point is left unmonitored.
However, the dynamics of the penalty are unknown to us. We present a
Multi-Agent Reinforcement Learning (MARL) algorithm for the VPM problem.
Specifically, we present a Multi-Agent Graph Attention Proximal Policy
Optimization (MA-G-PPO) algorithm that takes as input the local observations of
all agents combined with a low resolution global map to learn a policy for each
agent. The graph attention allows agents to share their information with others
leading to an effective joint policy. Our main focus is to understand how
effective MARL is for the VPM problem. We investigate five research questions
with this broader goal. We find that MA-G-PPO is able to learn a better policy
than the non-RL baseline in most cases, the effectiveness depends on agents
sharing information with each other, and the policy learnt shows emergent
behavior for the agents. | [
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Conventional supervised learning methods, especially deep ones, are found to
be sensitive to out-of-distribution (OOD) examples, largely because the learned
representation mixes the semantic factor with the variation factor due to their
domain-specific correlation, while only the semantic factor causes the output.
To address the problem, we propose a Causal Semantic Generative model (CSG)
based on a causal reasoning so that the two factors are modeled separately, and
develop methods for OOD prediction from a single training domain, which is
common and challenging. The methods are based on the causal invariance
principle, with a novel design in variational Bayes for both efficient learning
and easy prediction. Theoretically, we prove that under certain conditions, CSG
can identify the semantic factor by fitting training data, and this
semantic-identification guarantees the boundedness of OOD generalization error
and the success of adaptation. Empirical study shows improved OOD performance
over prevailing baselines. | [
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] |
Autonomous vehicles (AVs) need to share the road with multiple, heterogeneous
road users in a variety of driving scenarios. It is overwhelming and
unnecessary to carefully interact with all observed agents, and AVs need to
determine whether and when to interact with each surrounding agent. In order to
facilitate the design and testing of prediction and planning modules of AVs,
in-depth understanding of interactive behavior is expected with proper
representation, and events in behavior data need to be extracted and
categorized automatically. Answers to what are the essential patterns of
interactions are also crucial for these motivations in addition to answering
whether and when. Thus, learning to extract interactive driving events and
patterns from human data for tackling the whether-when-what tasks is of
critical importance for AVs. There is, however, no clear definition and
taxonomy of interactive behavior, and most of the existing works are based on
either manual labelling or hand-crafted rules and features. In this paper, we
propose the Interactive Driving event and pattern Extraction Network (IDE-Net),
which is a deep learning framework to automatically extract interaction events
and patterns directly from vehicle trajectories. In IDE-Net, we leverage the
power of multi-task learning and proposed three auxiliary tasks to assist the
pattern extraction in an unsupervised fashion. We also design a unique
spatial-temporal block to encode the trajectory data. Experimental results on
the INTERACTION dataset verified the effectiveness of such designs in terms of
better generalizability and effective pattern extraction. We find three
interpretable patterns of interactions, bringing insights for driver behavior
representation, modeling and comprehension. Both objective and subjective
evaluation metrics are adopted in our analysis of the learned patterns. | [
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] |
Autonomous robot-assisted feeding requires the ability to acquire a wide
variety of food items. However, it is impossible for such a system to be
trained on all types of food in existence. Therefore, a key challenge is
choosing a manipulation strategy for a previously unseen food item. Previous
work showed that the problem can be represented as a linear bandit with visual
context. However, food has a wide variety of multi-modal properties relevant to
manipulation that can be hard to distinguish visually. Our key insight is that
we can leverage the haptic context we collect during and after manipulation
(i.e., "post hoc") to learn some of these properties and more quickly adapt our
visual model to previously unseen food. In general, we propose a modified
linear contextual bandit framework augmented with post hoc context observed
after action selection to empirically increase learning speed and reduce
cumulative regret. Experiments on synthetic data demonstrate that this effect
is more pronounced when the dimensionality of the context is large relative to
the post hoc context or when the post hoc context model is particularly easy to
learn. Finally, we apply this framework to the bite acquisition problem and
demonstrate the acquisition of 8 previously unseen types of food with 21% fewer
failures across 64 attempts. | [
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] |
In this paper we first present a novel operator extrapolation (OE) method for
solving deterministic variational inequality (VI) problems. Similar to the
gradient (operator) projection method, OE updates one single search sequence by
solving a single projection subproblem in each iteration. We show that OE can
achieve the optimal rate of convergence for solving a variety of VI problems in
a much simpler way than existing approaches. We then introduce the stochastic
operator extrapolation (SOE) method and establish its optimal convergence
behavior for solving different stochastic VI problems. In particular, SOE
achieves the optimal complexity for solving a fundamental problem, i.e.,
stochastic smooth and strongly monotone VI, for the first time in the
literature. We also present a stochastic block operator extrapolations (SBOE)
method to further reduce the iteration cost for the OE method applied to
large-scale deterministic VIs with a certain block structure. Numerical
experiments have been conducted to demonstrate the potential advantages of the
proposed algorithms. In fact, all these algorithms are applied to solve
generalized monotone variational inequality (GMVI) problems whose operator is
not necessarily monotone. We will also discuss optimal OE-based policy
evaluation methods for reinforcement learning in a companion paper. | [
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