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Music is often experienced as a progression of concurrent streams of notes,
or voices. The degree to which this happens depends on the position along a
voice-leading continuum, ranging from monophonic, to homophonic, to polyphonic,
which complicates the design of automatic voice separation models. We address
this continuum by defining voice separation as the task of decomposing music
into streams that exhibit both a high degree of external perceptual separation
from the other streams and a high degree of internal perceptual consistency.
The proposed voice separation task allows for a voice to diverge to multiple
voices and also for multiple voices to converge to the same voice. Equipped
with this flexible task definition, we manually annotated a corpus of popular
music and used it to train neural networks that assign notes to voices either
separately for each note in a chord (note-level), or jointly to all notes in a
chord (chord-level). The trained neural models greedily assign notes to voices
in a left to right traversal of the input chord sequence, using a diverse set
of perceptually informed input features. When evaluated on the extraction of
consecutive within voice note pairs, both models surpass a strong baseline
based on an iterative application of an envelope extraction function, with the
chord-level model consistently edging out the note-level model. The two models
are also shown to outperform previous approaches on separating the voices in
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] |
In astronomy, neural networks are often trained on simulated data with the
prospect of being applied to real observations. Unfortunately, simply training
a deep neural network on images from one domain does not guarantee satisfactory
performance on new images from a different domain. The ability to share
cross-domain knowledge is the main advantage of modern deep domain adaptation
techniques. Here we demonstrate the use of two techniques - Maximum Mean
Discrepancy (MMD) and adversarial training with Domain Adversarial Neural
Networks (DANN) - for the classification of distant galaxy mergers from the
Illustris-1 simulation, where the two domains presented differ only due to
inclusion of observational noise. We show how the addition of either MMD or
adversarial training greatly improves the performance of the classifier on the
target domain when compared to conventional machine learning algorithms,
thereby demonstrating great promise for their use in astronomy. | [
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] |
Thermal management in the hyper-scale cloud data centers is a critical
problem. Increased host temperature creates hotspots which significantly
increases cooling cost and affects reliability. Accurate prediction of host
temperature is crucial for managing the resources effectively. Temperature
estimation is a non-trivial problem due to thermal variations in the data
center. Existing solutions for temperature estimation are inefficient due to
their computational complexity and lack of accurate prediction. However,
data-driven machine learning methods for temperature prediction is a promising
approach. In this regard, we collect and study data from a private cloud and
show the presence of thermal variations. We investigate several machine
learning models to accurately predict the host temperature. Specifically, we
propose a gradient boosting machine learning model for temperature prediction.
The experiment results show that our model accurately predicts the temperature
with the average RMSE value of 0.05 or an average prediction error of 2.38
degree Celsius, which is 6 degree Celsius less as compared to an existing
theoretical model. In addition, we propose a dynamic scheduling algorithm to
minimize the peak temperature of hosts. The results show that our algorithm
reduces the peak temperature by 6.5 degree Celsius and consumes 34.5% less
energy as compared to the baseline algorithm. | [
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] |
The aim of this paper is to study the reward based policy exploration problem
in a supervised learning approach and enable robots to form complex movement
trajectories in challenging reward settings and search spaces. For this, the
experience of the robot, which can be bootstrapped from demonstrated
trajectories, is used to train a novel Neural Processes-based deep network that
samples from its latent space and generates the required trajectories given
desired rewards. Our framework can generate progressively improved trajectories
by sampling them from high reward landscapes, increasing the reward gradually.
Variational inference is used to create a stochastic latent space to sample
varying trajectories in generating population of trajectories given target
rewards. We benefit from Evolutionary Strategies and propose a novel crossover
operation, which is applied in the self-organized latent space of the
individual policies, allowing blending of the individuals that might address
different factors in the reward function. Using a number of tasks that require
sequential reaching to multiple points or passing through gaps between objects,
we showed that our method provides stable learning progress and significant
sample efficiency compared to a number of state-of-the-art robotic
reinforcement learning methods. Finally, we show the real-world suitability of
our method through real robot execution involving obstacle avoidance. | [
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] |
The active search for objects of interest in an unknown environment has many
robotics applications including search and rescue, detecting gas leaks or
locating animal poachers. Existing algorithms often prioritize the location
accuracy of objects of interest while other practical issues such as the
reliability of object detection as a function of distance and lines of sight
remain largely ignored. Additionally, in many active search scenarios,
communication infrastructure may be unreliable or unestablished, making
centralized control of multiple agents impractical. We present an algorithm
called Noise-Aware Thompson Sampling (NATS) that addresses these issues for
multiple ground-based robots performing active search considering two sources
of sensory information from monocular optical imagery and depth maps. By
utilizing Thompson Sampling, NATS allows for decentralized coordination among
multiple agents. NATS also considers object detection uncertainty from depth as
well as environmental occlusions and operates while remaining agnostic of the
number of objects of interest. Using simulation results, we show that NATS
significantly outperforms existing methods such as information-greedy policies
or exhaustive search. We demonstrate the real-world viability of NATS using a
pseudo-realistic environment created in the Unreal Engine 4 game development
platform with the AirSim plugin. | [
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] |
Untangling ropes, wires, and cables is a challenging task for robots due to
the high-dimensional configuration space, visual homogeneity, self-occlusions,
and complex dynamics. We consider dense (tight) knots that lack space between
self-intersections and present an iterative approach that uses learned
geometric structure in configurations. We instantiate this into an algorithm,
HULK: Hierarchical Untangling from Learned Keypoints, which combines
learning-based perception with a geometric planner into a policy that guides a
bilateral robot to untangle knots. To evaluate the policy, we perform
experiments both in a novel simulation environment modelling cables with varied
knot types and textures and in a physical system using the da Vinci surgical
robot. We find that HULK is able to untangle cables with dense figure-eight and
overhand knots and generalize to varied textures and appearances. We compare
two variants of HULK to three baselines and observe that HULK achieves 43.3%
higher success rates on a physical system compared to the next best baseline.
HULK successfully untangles a cable from a dense initial configuration
containing up to two overhand and figure-eight knots in 97.9% of 378 simulation
experiments with an average of 12.1 actions per trial. In physical experiments,
HULK achieves 61.7% untangling success, averaging 8.48 actions per trial.
Supplementary material, code, and videos can be found at
https://tinyurl.com/y3a88ycu. | [
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] |
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality
which enables the recovery of functional tissue parameters such as blood
oxygenation. However, the underlying inverse problems are potentially
ill-posed, meaning that radically different tissue properties may - in theory -
yield comparable measurements. In this work, we present a new approach for
handling this specific type of uncertainty by leveraging the concept of
conditional invertible neural networks (cINNs). Specifically, we propose going
beyond commonly used point estimates for tissue oxygenation and converting
single-pixel initial pressure spectra to the full posterior probability
density. This way, the inherent ambiguity of a problem can be encoded with
multiple modes in the output. Based on the presented architecture, we
demonstrate two use cases which leverage this information to not only detect
and quantify but also to compensate for uncertainties: (1) photoacoustic device
design and (2) optimization of photoacoustic image acquisition. Our in silico
studies demonstrate the potential of the proposed methodology to become an
important building block for uncertainty-aware reconstruction of physiological
parameters with PAI. | [
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] |
The quality of healthcare provided by hospitals is subject to considerable
variability. Consequently, accurate measurements of hospital performance are
essential for various decision-makers, including patients, hospital managers
and health insurers. Hospital performance is assessed via the health outcomes
of their patients. However, as the risk profiles of patients between hospitals
vary, measuring hospital performance requires adjustment for patient risk. This
task is formalized in the state-of-the-art procedure through a hierarchical
generalized linear model, that isolates hospital fixed-effects from the effect
of patient risk on health outcomes. Due to the linear nature of this approach,
any non-linear relations or interaction terms between risk variables are
neglected.
In this work, we propose a novel method for measuring hospital performance
adjusted for patient risk. This method captures non-linear relationships as
well as interactions among patient risk variables, specifically the effect of
co-occurring health conditions on health outcomes. For this purpose, we develop
a tailored neural network architecture that is partially interpretable: a
non-linear part is used to encode risk factors, while a linear structure models
hospital fixed-effects, such that the risk-adjusted hospital performance can be
estimated. We base our evaluation on more than 13 million patient admissions
across almost 1,900 US hospitals as provided by the Nationwide Readmissions
Database. Our model improves the ROC-AUC over the state-of-the-art by 4.1
percent. These findings demonstrate that a large portion of the variance in
health outcomes can be attributed to non-linear relationships between patient
risk variables and implicate that the current approach of measuring hospital
performance should be expanded. | [
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] |
There has been significant recent work on data-driven algorithms for learning
general-purpose grasping policies. However, these policies can consistently
fail to grasp challenging objects which are significantly out of the
distribution of objects in the training data or which have very few high
quality grasps. Motivated by such objects, we propose a novel problem setting,
Exploratory Grasping, for efficiently discovering reliable grasps on an unknown
polyhedral object via sequential grasping, releasing, and toppling. We
formalize Exploratory Grasping as a Markov Decision Process, study the
theoretical complexity of Exploratory Grasping in the context of reinforcement
learning and present an efficient bandit-style algorithm, Bandits for Online
Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the
problem to efficiently discover high performing grasps for each object stable
pose. BORGES can be used to complement any general-purpose grasping algorithm
with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn
policies for objects in which they exhibit persistent failures. Simulation
experiments suggest that BORGES can significantly outperform both
general-purpose grasping pipelines and two other online learning algorithms and
achieves performance within 5% of the optimal policy within 1000 and 8000
timesteps on average across 46 challenging objects from the Dex-Net adversarial
and EGAD! object datasets, respectively. Initial physical experiments suggest
that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with
just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping
for supplementary material and videos. | [
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] |
We propose answer-set programs that specify and compute counterfactual
interventions on entities that are input on a classification model. In relation
to the outcome of the model, the resulting counterfactual entities serve as a
basis for the definition and computation of causality-based explanation scores
for the feature values in the entity under classification, namely
"responsibility scores". The approach and the programs can be applied with
black-box models, and also with models that can be specified as logic programs,
such as rule-based classifiers. The main focus of this work is on the
specification and computation of "best" counterfactual entities, i.e. those
that lead to maximum responsibility scores. From them one can read off the
explanations as maximum responsibility feature values in the original entity.
We also extend the programs to bring into the picture semantic or domain
knowledge. We show how the approach could be extended by means of probabilistic
methods, and how the underlying probability distributions could be modified
through the use of constraints. Several examples of programs written in the
syntax of the DLV ASP-solver, and run with it, are shown. | [
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] |
Sleep state classification is vital in managing and understanding sleep
patterns and is generally the first step in identifying acute or chronic sleep
disorders. However, it is essential to do this without affecting the natural
environment or conditions of the subject during their sleep. Techniques such as
Polysomnography(PSG) are obtrusive and are not convenient for regular sleep
monitoring. Fortunately, The rise of novel technologies and advanced computing
has given a recent resurgence to monitoring sleep techniques. One such
contactless and unobtrusive monitoring technique is Ballistocradiography(BCG),
in which vitals are monitored by measuring the body's reaction to the cardiac
ejection of blood. In this study, we propose a Multi-Head 1D-Convolution based
Deep Neural Network to classify sleep-wake state and predict sleep-wake time
accurately using the signals coming from a BCG sensor. Our method achieves a
sleep-wake classification score of 95.5%, which is on par with researches based
on the PSG system. We further conducted two independent studies in a controlled
and uncontrolled environment to test the sleep-wake prediction accuracy. We
achieve a score of 94.16% in a controlled environment on 115 subjects and
94.90% in an uncontrolled environment on 350 subjects. The high accuracy and
contactless nature of the proposed system make it a convenient method for long
term monitoring of sleep states. | [
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Indoor autonomous navigation requires a precise and accurate localization
system able to guide robots through cluttered, unstructured and dynamic
environments. Ultra-wideband (UWB) technology, as an indoor positioning system,
offers precise localization and tracking, but moving obstacles and
non-line-of-sight occurrences can generate noisy and unreliable signals. That,
combined with sensors noise, unmodeled dynamics and environment changes can
result in a failure of the guidance algorithm of the robot. We demonstrate how
a power-efficient and low computational cost point-to-point local planner,
learnt with deep reinforcement learning (RL), combined with UWB localization
technology can constitute a robust and resilient to noise short-range guidance
system complete solution. We trained the RL agent on a simulated environment
that encapsulates the robot dynamics and task constraints and then, we tested
the learnt point-to-point navigation policies in a real setting with more than
two-hundred experimental evaluations using UWB localization. Our results show
that the computational efficient end-to-end policy learnt in plain simulation,
that directly maps low-range sensors signals to robot controls, deployed in
combination with ultra-wideband noisy localization in a real environment, can
provide a robust, scalable and at-the-edge low-cost navigation system solution. | [
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] |
This paper presents an eXplainable Fault Detection and Diagnosis System
(XFDDS) for incipient faults in PV panels. The XFDDS is a hybrid approach that
combines the model-based and data-driven framework. Model-based FDD for PV
panels lacks high fidelity models at low irradiance conditions for detecting
incipient faults. To overcome this, a novel irradiance based three diode model
(IB3DM) is proposed. It is a nine parameter model that provides higher accuracy
even at low irradiance conditions, an important aspect for detecting incipient
faults from noise. To exploit PV data, extreme gradient boosting (XGBoost) is
used due to its ability to detecting incipient faults. Lack of explainability,
feature variability for sample instances, and false alarms are challenges with
data-driven FDD methods. These shortcomings are overcome by hybridization of
XGBoost and IB3DM, and using eXplainable Artificial Intelligence (XAI)
techniques. To combine the XGBoost and IB3DM, a fault-signature metric is
proposed that helps reducing false alarms and also trigger an explanation on
detecting incipient faults. To provide explainability, an eXplainable
Artificial Intelligence (XAI) application is developed. It uses the local
interpretable model-agnostic explanations (LIME) framework and provides
explanations on classifier outputs for data instances. These explanations help
field engineers/technicians for performing troubleshooting and maintenance
operations. The proposed XFDDS is illustrated using experiments on different PV
technologies and our results demonstrate the perceived benefits. | [
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] |
In many numerical schemes, the computational complexity scales non-linearly
with the problem size. Solving a linear system of equations using direct
methods or most iterative methods is a typical example. Algebraic multi-grid
(AMG) methods are numerical methods used to solve large linear systems of
equations efficiently. One of the main differences between AMG methods is how
the coarser grid is constructed from a given fine grid. There are two main
classes of AMG methods; graph and aggregation based coarsening methods. Here we
propose an aggregation-based coarsening framework leveraging graph
representation learning and clustering algorithms. Our method introduces the
power of machine learning into the AMG research field and opens a new
perspective for future researches. The proposed method uses graph
representation learning techniques to learn latent features of the graph
obtained from the underlying matrix of coefficients. Using these extracted
features, we generated a coarser grid from the fine grid. The proposed method
is highly capable of parallel computations. Our experiments show that the
proposed method's efficiency in solving large systems is closely comparable
with other aggregation-based methods, demonstrating the high capability of
graph representation learning in designing multi-grid solvers. | [
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] |
While the evolution of linear initial conditions present in the early
universe into extended halos of dark matter at late times can be computed using
cosmological simulations, a theoretical understanding of this complex process
remains elusive. Here, we build a deep learning framework to learn this
non-linear relationship, and develop techniques to physically interpret the
learnt mapping. A three-dimensional convolutional neural network (CNN) is
trained to predict the mass of dark matter halos from the initial conditions.
N-body simulations follow the microphysical laws of gravity, whereas the CNN
model provides a simplified description of halo collapse where features are
extracted from the initial conditions through convolutions and combined in a
non-linear way to provide a halo mass prediction. We find no significant change
in the predictive accuracy of the model if we retrain it removing anisotropic
information from the inputs, suggesting that the features learnt by the CNN are
equivalent to spherical averages over the initial conditions. Despite including
all possible feature combinations that can be extracted by convolutions in the
model, the final halo mass predictions do not depend on anisotropic aspects of
the initial conditions. Our results indicate that deep learning frameworks can
provide a powerful tool for extracting physical insight into cosmological
structure formation. | [
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] |
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which
two teams of three small robots play against each other. Traditionally, a
deterministic coach agent will choose the most suitable strategy and formation
for each adversary's strategy. Therefore, the role of a coach is of great
importance to the game. In this sense, this paper proposes an end-to-end
approach for the coaching task based on Reinforcement Learning (RL). The
proposed system processes the information during the simulated matches to learn
an optimal policy that chooses the current formation, depending on the opponent
and game conditions. We trained two RL policies against three different teams
(balanced, offensive, and heavily offensive) in a simulated environment. Our
results were assessed against one of the top teams of the VSSS league, showing
promising results after achieving a win/loss ratio of approximately 2.0. | [
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] |
Large software systems tune hundreds of 'constants' to optimize their runtime
performance. These values are commonly derived through intuition, lab tests, or
A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best
value depends on runtime context. In this paper, we provide an experimental
approach to replace constants with learned contextual functions for Skype - a
widely used real-time communication (RTC) application. We present Resonance, a
system based on contextual bandits (CB). We describe experiences from three
real-world experiments: applying it to the audio, video, and transport
components in Skype. We surface a unique and practical challenge of performing
machine learning (ML) inference in large software systems written using
encapsulation principles. Finally, we open-source FeatureBroker, a library to
reduce the friction in adopting ML models in such development environments | [
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] |
This study combines simulated annealing with delta evaluation to solve the
joint stratification and sample allocation problem. In this problem, atomic
strata are partitioned into mutually exclusive and collectively exhaustive
strata. Each partition of atomic strata is a possible solution to the
stratification problem, the quality of which is measured by its cost. The Bell
number of possible solutions is enormous, for even a moderate number of atomic
strata, and an additional layer of complexity is added with the evaluation time
of each solution. Many larger scale combinatorial optimisation problems cannot
be solved to optimality, because the search for an optimum solution requires a
prohibitive amount of computation time. A number of local search heuristic
algorithms have been designed for this problem but these can become trapped in
local minima preventing any further improvements. We add, to the existing suite
of local search algorithms, a simulated annealing algorithm that allows for an
escape from local minima and uses delta evaluation to exploit the similarity
between consecutive solutions, and thereby reduces the evaluation time. We
compared the simulated annealing algorithm with two recent algorithms. In both
cases, the simulated annealing algorithm attained a solution of comparable
quality in considerably less computation time. | [
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] |
This work explores the signal awareness of AI models for source code
understanding. Using a software vulnerability detection use case, we evaluate
the models' ability to capture the correct vulnerability signals to produce
their predictions. Our prediction-preserving input minimization (P2IM) approach
systematically reduces the original source code to a minimal snippet which a
model needs to maintain its prediction. The model's reliance on incorrect
signals is then uncovered when the vulnerability in the original code is
missing in the minimal snippet, both of which the model however predicts as
being vulnerable. We measure the signal awareness of models using a new metric
we propose- Signal-aware Recall (SAR). We apply P2IM on three different neural
network architectures across multiple datasets. The results show a sharp drop
in the model's Recall from the high 90s to sub-60s with the new metric,
highlighting that the models are presumably picking up a lot of noise or
dataset nuances while learning their vulnerability detection logic. Although
the drop in model performance may be perceived as an adversarial attack, but
this isn't P2IM's objective. The idea is rather to uncover the signal-awareness
of a black-box model in a data-driven manner via controlled queries. SAR's
purpose is to measure the impact of task-agnostic model training, and not to
suggest a shortcoming in the Recall metric. The expectation, in fact, is for
SAR to match Recall in the ideal scenario where the model truly captures
task-specific signals. | [
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] |
The game industry is moving into an era where old-style game engines are
being replaced by re-engineered systems with embedded machine learning
technologies for the operation, analysis and understanding of game play. In
this paper, we describe our machine learning course designed for graduate
students interested in applying recent advances of deep learning and
reinforcement learning towards gaming. This course serves as a bridge to foster
interdisciplinary collaboration among graduate schools and does not require
prior experience designing or building games. Graduate students enrolled in
this course apply different fields of machine learning techniques such as
computer vision, natural language processing, computer graphics, human computer
interaction, robotics and data analysis to solve open challenges in gaming.
Student projects cover use-cases such as training AI-bots in gaming benchmark
environments and competitions, understanding human decision patterns in gaming,
and creating intelligent non-playable characters or environments to foster
engaging gameplay. Projects demos can help students open doors for an industry
career, aim for publications, or lay the foundations of a future product. Our
students gained hands-on experience in applying state of the art machine
learning techniques to solve real-life problems in gaming. | [
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] |
Next destination recommendation is an important task in the transportation
domain of taxi and ride-hailing services, where users are recommended with
personalized destinations given their current origin location. However, recent
recommendation works do not satisfy this origin-awareness property, and only
consider learning from historical destination locations, without origin
information. Thus, the resulting approaches are unable to learn and predict
origin-aware recommendations based on the user's current location, leading to
sub-optimal performance and poor real-world practicality. Hence, in this work,
we study the origin-aware next destination recommendation task. We propose the
Spatial-Temporal Origin-Destination Personalized Preference Attention
(STOD-PPA) encoder-decoder model to learn origin-origin (OO),
destination-destination (DD), and origin-destination (OD) relationships by
first encoding both origin and destination sequences with spatial and temporal
factors in local and global views, then decoding them through personalized
preference attention to predict the next destination. Experimental results on
seven real-world user trajectory taxi datasets show that our model
significantly outperforms baseline and state-of-the-art methods. | [
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] |
Physical human-robot interactions (pHRI) are less efficient and communicative
than human-human interactions, and a key reason is a lack of informative sense
of touch in robotic systems. Interpreting human touch gestures is a nuanced,
challenging task with extreme gaps between human and robot capability. Among
prior works that demonstrate human touch recognition capability, differences in
sensors, gesture classes, feature sets, and classification algorithms yield a
conglomerate of non-transferable results and a glaring lack of a standard. To
address this gap, this work presents 1) four proposed touch gesture classes
that cover an important subset of the gesture characteristics identified in the
literature, 2) the collection of an extensive force dataset on a common pHRI
robotic arm with only its internal wrist force-torque sensor, and 3) an
exhaustive performance comparison of combinations of feature sets and
classification algorithms on this dataset. We demonstrate high classification
accuracies among our proposed gesture definitions on a test set, emphasizing
that neural net-work classifiers on the raw data outperform other combinations
of feature sets and algorithms. The accompanying video is here:
https://youtu.be/gJPVImNKU68 | [
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] |
Research on localization and perception for Autonomous Driving is mainly
focused on camera and LiDAR datasets, rarely on radar data. Manually labeling
sparse radar point clouds is challenging. For a dataset generation, we propose
the cross sensor Radar Artifact Labeling Framework (RALF). Automatically
generated labels for automotive radar data help to cure radar shortcomings like
artifacts for the application of artificial intelligence. RALF provides
plausibility labels for radar raw detections, distinguishing between artifacts
and targets. The optical evaluation backbone consists of a generalized
monocular depth image estimation of surround view cameras plus LiDAR scans.
Modern car sensor sets of cameras and LiDAR allow to calibrate image-based
relative depth information in overlapping sensing areas. K-Nearest Neighbors
matching relates the optical perception point cloud with raw radar detections.
In parallel, a temporal tracking evaluation part considers the radar
detections' transient behavior. Based on the distance between matches,
respecting both sensor and model uncertainties, we propose a plausibility
rating of every radar detection. We validate the results by evaluating error
metrics on semi-manually labeled ground truth dataset of $3.28\cdot10^6$
points. Besides generating plausible radar detections, the framework enables
further labeled low-level radar signal datasets for applications of perception
and Autonomous Driving learning tasks. | [
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] |
We consider a scenario in which two reinforcement learning agents repeatedly
play a matrix game against each other and update their parameters after each
round. The agents' decision-making is transparent to each other, which allows
each agent to predict how their opponent will play against them. To prevent an
infinite regress of both agents recursively predicting each other indefinitely,
each agent is required to give an opponent-independent response with some
probability at least epsilon. Transparency also allows each agent to anticipate
and shape the other agent's gradient step, i.e. to move to regions of parameter
space in which the opponent's gradient points in a direction favourable to
them. We study the resulting dynamics experimentally, using two algorithms from
previous literature (LOLA and SOS) for opponent-aware learning. We find that
the combination of mutually transparent decision-making and opponent-aware
learning robustly leads to mutual cooperation in a single-shot prisoner's
dilemma. In a game of chicken, in which both agents try to manoeuvre their
opponent towards their preferred equilibrium, converging to a mutually
beneficial outcome turns out to be much harder, and opponent-aware learning can
even lead to worst-case outcomes for both agents. This highlights the need to
develop opponent-aware learning algorithms that achieve acceptable outcomes in
social dilemmas involving an equilibrium selection problem. | [
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] |
T-cell receptors can recognize foreign peptides bound to major
histocompatibility complex (MHC) class-I proteins, and thus trigger the
adaptive immune response. Therefore, identifying peptides that can bind to MHC
class-I molecules plays a vital role in the design of peptide vaccines. Many
computational methods, for example, the state-of-the-art allele-specific method
MHCflurry, have been developed to predict the binding affinities between
peptides and MHC molecules. In this manuscript, we develop two allele-specific
Convolutional Neural Network (CNN)-based methods named ConvM and SpConvM to
tackle the binding prediction problem. Specifically, we formulate the problem
as to optimize the rankings of peptide-MHC bindings via ranking-based learning
objectives. Such optimization is more robust and tolerant to the measurement
inaccuracy of binding affinities, and therefore enables more accurate
prioritization of binding peptides. In addition, we develop a new position
encoding method in ConvM and SpConvM to better identify the most important
amino acids for the binding events. Our experimental results demonstrate that
our models significantly outperform the state-of-the-art methods including
MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on
ROC5 across 128 alleles. | [
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] |
This paper presents Systolic-CNN, an OpenCL-defined scalable,
run-time-flexible FPGA accelerator architecture, optimized for accelerating the
inference of various convolutional neural networks (CNNs) in multi-tenancy
cloud/edge computing. The existing OpenCL-defined FPGA accelerators for CNN
inference are insufficient due to limited flexibility for supporting multiple
CNN models at run time and poor scalability resulting in underutilized FPGA
resources and limited computational parallelism. Systolic-CNN adopts a highly
pipelined and paralleled 1-D systolic array architecture, which efficiently
explores both spatial and temporal parallelism for accelerating CNN inference
on FPGAs. Systolic-CNN is highly scalable and parameterized, which can be
easily adapted by users to achieve up to 100% utilization of the coarse-grained
computation resources (i.e., DSP blocks) for a given FPGA. Systolic-CNN is also
run-time-flexible in the context of multi-tenancy cloud/edge computing, which
can be time-shared to accelerate a variety of CNN models at run time without
the need of recompiling the FPGA kernel hardware nor reprogramming the FPGA.
The experiment results based on an Intel Arria/Stratix 10 GX FPGA Development
board show that the optimized single-precision implementation of Systolic-CNN
can achieve an average inference latency of 7ms/2ms, 84ms/33ms, 202ms/73ms,
1615ms/873ms, and 900ms/498ms per image for accelerating AlexNet, ResNet-50,
ResNet-152, RetinaNet, and Light-weight RetinaNet, respectively. Codes are
available at https://github.com/PSCLab-ASU/Systolic-CNN. | [
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] |
Background: The inception of next generations sequencing technologies have
exponentially increased the volume of biological sequence data. Protein
sequences, being quoted as the `language of life', has been analyzed for a
multitude of applications and inferences.
Motivation: Owing to the rapid development of deep learning, in recent years
there have been a number of breakthroughs in the domain of Natural Language
Processing. Since these methods are capable of performing different tasks when
trained with a sufficient amount of data, off-the-shelf models are used to
perform various biological applications. In this study, we investigated the
applicability of the popular Skip-gram model for protein sequence analysis and
made an attempt to incorporate some biological insights into it.
Results: We propose a novel $k$-mer embedding scheme, Align-gram, which is
capable of mapping the similar $k$-mers close to each other in a vector space.
Furthermore, we experiment with other sequence-based protein representations
and observe that the embeddings derived from Align-gram aids modeling and
training deep learning models better. Our experiments with a simple baseline
LSTM model and a much complex CNN model of DeepGoPlus shows the potential of
Align-gram in performing different types of deep learning applications for
protein sequence analysis. | [
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] |
With the ever-growing achievements in Artificial Intelligence (AI) and the
recent boosted enthusiasm in Financial Technology (FinTech), applications such
as credit scoring have gained substantial academic interest. Credit scoring
helps financial experts make better decisions regarding whether or not to
accept a loan application, such that loans with a high probability of default
are not accepted. Apart from the noisy and highly imbalanced data challenges
faced by such credit scoring models, recent regulations such as the `right to
explanation' introduced by the General Data Protection Regulation (GDPR) and
the Equal Credit Opportunity Act (ECOA) have added the need for model
interpretability to ensure that algorithmic decisions are understandable and
coherent. An interesting concept that has been recently introduced is
eXplainable AI (XAI), which focuses on making black-box models more
interpretable. In this work, we present a credit scoring model that is both
accurate and interpretable. For classification, state-of-the-art performance on
the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is
achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then
further enhanced with a 360-degree explanation framework, which provides
different explanations (i.e. global, local feature-based and local
instance-based) that are required by different people in different situations.
Evaluation through the use of functionallygrounded, application-grounded and
human-grounded analysis show that the explanations provided are simple,
consistent as well as satisfy the six predetermined hypotheses testing for
correctness, effectiveness, easy understanding, detail sufficiency and
trustworthiness. | [
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] |
We emphasize the importance of asking the right question when interpreting
the decisions of a learning model. We discuss a natural extension of the
theoretical machinery from Janzing et. al. 2020, which answers the question
"Why did my model predict a person has cancer?" for answering a more involved
question, "What caused my model to predict a person has cancer?" While the
former quantifies the direct effects of variables on the model, the latter also
accounts for indirect effects, thereby providing meaningful insights wherever
human beings can reason in terms of cause and effect. We propose three broad
categories for interpretations: observational, model-specific and causal each
of which are significant in their own right. Furthermore, this paper quantifies
feature relevance by weaving different natures of interpretations together with
different measures as characteristic functions for Shapley symmetrization.
Besides the widely used expected value of the model, we also discuss measures
of statistical uncertainty and dispersion as informative candidates, and their
merits in generating explanations for each data point, some of which are used
in this context for the first time. These measures are not only useful for
studying the influence of variables on the model output, but also on the
predictive performance of the model, and for that we propose relevant
characteristic functions that are also used for the first time. | [
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A unifying mathematical formulation is needed to move from one-off digital
twins built through custom implementations to robust digital twin
implementations at scale. This work proposes a probabilistic graphical model as
a formal mathematical representation of a digital twin and its associated
physical asset. We create an abstraction of the asset-twin system as a set of
coupled dynamical systems, evolving over time through their respective
state-spaces and interacting via observed data and control inputs. The formal
definition of this coupled system as a probabilistic graphical model enables us
to draw upon well-established theory and methods from Bayesian statistics,
dynamical systems, and control theory. The declarative and general nature of
the proposed digital twin model make it rigorous yet flexible, enabling its
application at scale in a diverse range of application areas. We demonstrate
how the model is instantiated to enable a structural digital twin of an
unmanned aerial vehicle (UAV). The digital twin is calibrated using
experimental data from a physical UAV asset. Its use in dynamic decision making
is then illustrated in a synthetic example where the UAV undergoes an in-flight
damage event and the digital twin is dynamically updated using sensor data. The
graphical model foundation ensures that the digital twin calibration and
updating process is principled, unified, and able to scale to an entire fleet
of digital twins. | [
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] |
This paper presents c2g-HOF networks which learn to generate cost-to-go
functions for manipulator motion planning. The c2g-HOF architecture consists of
a cost-to-go function over the configuration space represented as a neural
network (c2g-network) as well as a Higher Order Function (HOF) network which
outputs the weights of the c2g-network for a given input workspace. Both
networks are trained end-to-end in a supervised fashion using costs computed
from traditional motion planners. Once trained, c2g-HOF can generate a smooth
and continuous cost-to-go function directly from workspace sensor inputs
(represented as a point cloud in 3D or an image in 2D). At inference time, the
weights of the c2g-network are computed very efficiently and near-optimal
trajectories are generated by simply following the gradient of the cost-to-go
function. We compare c2g-HOF with traditional planning algorithms for various
robots and planning scenarios. The experimental results indicate that planning
with c2g-HOF is significantly faster than other motion planning algorithms,
resulting in orders of magnitude improvement when including collision checking.
Furthermore, despite being trained from sparsely sampled trajectories in
configuration space, c2g-HOF generalizes to generate smoother, and often lower
cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF
manipulator arm where motion planning in a complex workspace requires only 0.13
seconds for the entire trajectory. | [
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] |
The phenomenon of self-organization has been of special interest to the
neural network community for decades. In this paper, we study a variant of the
Self-Organizing Map (SOM) that models the phenomenon of self-organization of
the particles forming a string when the string is tightened from one or both
ends. The proposed variant, called the String Tightening Self-Organizing Neural
Network (STON), can be used to solve certain practical problems, such as
computation of shortest homotopic paths, smoothing paths to avoid sharp turns,
and computation of convex hull. These problems are of considerable interest in
computational geometry, robotics path planning, AI (diagrammatic reasoning),
VLSI routing, and geographical information systems. Given a set of obstacles
and a string with two fixed terminal points in a two dimensional space, the
STON model continuously tightens the given string until the unique shortest
configuration in terms of the Euclidean metric is reached. The STON minimizes
the total length of a string on convergence by dynamically creating and
selecting feature vectors in a competitive manner. Proof of correctness of this
anytime algorithm and experimental results obtained by its deployment are
presented in the paper. | [
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] |
Imitation Learning is a promising paradigm for learning complex robot
manipulation skills by reproducing behavior from human demonstrations. However,
manipulation tasks often contain bottleneck regions that require a sequence of
precise actions to make meaningful progress, such as a robot inserting a pod
into a coffee machine to make coffee. Trained policies can fail in these
regions because small deviations in actions can lead the policy into states not
covered by the demonstrations. Intervention-based policy learning is an
alternative that can address this issue -- it allows human operators to monitor
trained policies and take over control when they encounter failures. In this
paper, we build a data collection system tailored to 6-DoF manipulation
settings, that enables remote human operators to monitor and intervene on
trained policies. We develop a simple and effective algorithm to train the
policy iteratively on new data collected by the system that encourages the
policy to learn how to traverse bottlenecks through the interventions. We
demonstrate that agents trained on data collected by our intervention-based
system and algorithm outperform agents trained on an equivalent number of
samples collected by non-interventional demonstrators, and further show that
our method outperforms multiple state-of-the-art baselines for learning from
the human interventions on a challenging robot threading task and a coffee
making task. Additional results and videos at
https://sites.google.com/stanford.edu/iwr . | [
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] |
Imitation Learning (IL) is a powerful paradigm to teach robots to perform
manipulation tasks by allowing them to learn from human demonstrations
collected via teleoperation, but has mostly been limited to single-arm
manipulation. However, many real-world tasks require multiple arms, such as
lifting a heavy object or assembling a desk. Unfortunately, applying IL to
multi-arm manipulation tasks has been challenging -- asking a human to control
more than one robotic arm can impose significant cognitive burden and is often
only possible for a maximum of two robot arms. To address these challenges, we
present Multi-Arm RoboTurk (MART), a multi-user data collection platform that
allows multiple remote users to simultaneously teleoperate a set of robotic
arms and collect demonstrations for multi-arm tasks. Using MART, we collected
demonstrations for five novel two and three-arm tasks from several
geographically separated users. From our data we arrived at a critical insight:
most multi-arm tasks do not require global coordination throughout its full
duration, but only during specific moments. We show that learning from such
data consequently presents challenges for centralized agents that directly
attempt to model all robot actions simultaneously, and perform a comprehensive
study of different policy architectures with varying levels of centralization
on our tasks. Finally, we propose and evaluate a base-residual policy framework
that allows trained policies to better adapt to the mixed coordination setting
common in multi-arm manipulation, and show that a centralized policy augmented
with a decentralized residual model outperforms all other models on our set of
benchmark tasks. Additional results and videos at
https://roboturk.stanford.edu/multiarm . | [
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] |
Despite the recent progress on neural network architectures for speech
separation, the balance between the model size, model complexity and model
performance is still an important and challenging problem for the deployment of
such models to low-resource platforms. In this paper, we propose two simple
modules, group communication and context codec, that can be easily applied to a
wide range of architectures to jointly decrease the model size and complexity
without sacrificing the performance. A group communication module splits a
high-dimensional feature into groups of low-dimensional features and captures
the inter-group dependency. A separation module with a significantly smaller
model size can then be shared by all the groups. A context codec module,
containing a context encoder and a context decoder, is designed as a learnable
downsampling and upsampling module to decrease the length of a sequential
feature processed by the separation module. The combination of the group
communication and the context codec modules is referred to as the GC3 design.
Experimental results show that applying GC3 on multiple network architectures
for speech separation can achieve on-par or better performance with as small as
2.5% model size and 17.6% model complexity, respectively. | [
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] |
Humans can leverage hierarchical structures to split a task into sub-tasks
and solve problems efficiently. Both imitation and reinforcement learning or a
combination of them with hierarchical structures have been proven to be an
efficient way for robots to learn complex tasks with sparse rewards. However,
in the previous work of hierarchical imitation and reinforcement learning, the
tested environments are in relatively simple 2D games, and the action spaces
are discrete. Furthermore, many imitation learning works focusing on improving
the policies learned from the expert polices that are hard-coded or trained by
reinforcement learning algorithms, rather than human experts. In the scenarios
of human-robot interaction, humans can be required to provide demonstrations to
teach the robot, so it is crucial to improve the learning efficiency to reduce
expert efforts, and know human's perception about the learning/training
process. In this project, we explored different imitation learning algorithms
and designed active learning algorithms upon the hierarchical imitation and
reinforcement learning framework we have developed. We performed an experiment
where five participants were asked to guide a randomly initialized agent to a
random goal in a maze. Our experimental results showed that using DAgger and
reward-based active learning method can achieve better performance while saving
more human efforts physically and mentally during the training process. | [
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] |
In-home health monitoring has attracted great attention for the ageing
population worldwide. With the abundant user health data accessed by Internet
of Things (IoT) devices and recent development in machine learning, smart
healthcare has seen many successful stories. However, existing approaches for
in-home health monitoring do not pay sufficient attention to user data privacy
and thus are far from being ready for large-scale practical deployment. In this
paper, we propose FedHome, a novel cloud-edge based federated learning
framework for in-home health monitoring, which learns a shared global model in
the cloud from multiple homes at the network edges and achieves data privacy
protection by keeping user data locally. To cope with the imbalanced and
non-IID distribution inherent in user's monitoring data, we design a generative
convolutional autoencoder (GCAE), which aims to achieve accurate and
personalized health monitoring by refining the model with a generated
class-balanced dataset from user's personal data. Besides, GCAE is lightweight
to transfer between the cloud and edges, which is useful to reduce the
communication cost of federated learning in FedHome. Extensive experiments
based on realistic human activity recognition data traces corroborate that
FedHome significantly outperforms existing widely-adopted methods. | [
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] |
We present a sound and complete algorithm for recovering causal graphs from
observed, non-interventional data, in the possible presence of latent
confounders and selection bias. We rely on the causal Markov and faithfulness
assumptions and recover the equivalence class of the underlying causal graph by
performing a series of conditional independence (CI) tests between observed
variables. We propose a single step that is applied iteratively, such that the
independence and causal relations entailed from the resulting graph, after any
iteration, is correct and becomes more informative with successive iteration.
Essentially, we tie the size of the CI condition set to its distance from the
tested nodes on the resulting graph. Each iteration refines the skeleton and
orientation by performing CI tests having condition sets that are larger than
in the preceding iteration. In an iteration, condition sets of CI tests are
constructed from nodes that are within a specified search distance, and the
sizes of these condition sets is equal to this search distance. The algorithm
then iteratively increases the search distance along with the condition set
sizes. Thus, each iteration refines a graph, that was recovered by previous
iterations having smaller condition sets -- having a higher statistical power.
We demonstrate that our algorithm requires significantly fewer CI tests and
smaller condition sets compared to the FCI algorithm. This is evident for both
recovering the true underlying graph using a perfect CI oracle, and accurately
estimating the graph using limited observed data. | [
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] |
To understand the important dimensions of service quality from the
passenger's perspective and tailor service offerings for competitive advantage,
airlines can capitalize on the abundantly available online customer reviews
(OCR). The objective of this paper is to discover company- and
competitor-specific intelligence from OCR using an unsupervised text analytics
approach. First, the key aspects (or topics) discussed in the OCR are extracted
using three topic models - probabilistic latent semantic analysis (pLSA) and
two variants of Latent Dirichlet allocation (LDA-VI and LDA-GS). Subsequently,
we propose an ensemble-assisted topic model (EA-TM), which integrates the
individual topic models, to classify each review sentence to the most
representative aspect. Likewise, to determine the sentiment corresponding to a
review sentence, an ensemble sentiment analyzer (E-SA), which combines the
predictions of three opinion mining methods (AFINN, SentiStrength, and VADER),
is developed. An aspect-based opinion summary (AOS), which provides a snapshot
of passenger-perceived strengths and weaknesses of an airline, is established
by consolidating the sentiments associated with each aspect. Furthermore, a
bi-gram analysis of the labeled OCR is employed to perform root cause analysis
within each identified aspect. A case study involving 99,147 airline reviews of
a US-based target carrier and four of its competitors is used to validate the
proposed approach. The results indicate that a cost- and time-effective
performance summary of an airline and its competitors can be obtained from OCR.
Finally, besides providing theoretical and managerial implications based on our
results, we also provide implications for post-pandemic preparedness in the
airline industry considering the unprecedented impact of coronavirus disease
2019 (COVID-19) and predictions on similar pandemics in the future. | [
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] |
Learning from Demonstration (LfD) constitutes one of the most robust
methodologies for constructing efficient cognitive robotic systems. Despite the
large body of research works already reported, current key technological
challenges include those of multi-agent learning and long-term autonomy.
Towards this direction, a novel cognitive architecture for multi-agent LfD
robotic learning is introduced, targeting to enable the reliable deployment of
open, scalable and expandable robotic systems in large-scale and complex
environments. In particular, the designed architecture capitalizes on the
recent advances in the Artificial Intelligence (AI) field, by establishing a
Federated Learning (FL)-based framework for incarnating a multi-human
multi-robot collaborative learning environment. The fundamental
conceptualization relies on employing multiple AI-empowered cognitive processes
(implementing various robotic tasks) that operate at the edge nodes of a
network of robotic platforms, while global AI models (underpinning the
aforementioned robotic tasks) are collectively created and shared among the
network, by elegantly combining information from a large number of human-robot
interaction instances. Regarding pivotal novelties, the designed cognitive
architecture a) introduces a new FL-based formalism that extends the
conventional LfD learning paradigm to support large-scale multi-agent
operational settings, b) elaborates previous FL-based self-learning robotic
schemes so as to incorporate the human in the learning loop and c) consolidates
the fundamental principles of FL with additional sophisticated AI-enabled
learning methodologies for modelling the multi-level inter-dependencies among
the robotic tasks. The applicability of the proposed framework is explained
using an example of a real-world industrial case study for agile
production-based Critical Raw Materials (CRM) recovery. | [
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] |
Classification of quantum data is essential for quantum machine learning and
near-term quantum technologies. In this paper, we propose a new hybrid
quantum-classical framework for supervised quantum learning, which we call
Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes
the classical shadows of quantum data, which fundamentally represent the side
information of quantum data with respect to certain physical observables.
Specifically, we first use variational shadow quantum circuits to extract
classical features in a convolution way and then utilize a fully-connected
neural network to complete the classification task. We show that this method
could sharply reduce the number of parameters and thus better facilitate
quantum circuit training. Simultaneously, less noise will be introduced since
fewer quantum gates are employed in such shadow circuits. Moreover, we show
that the Barren Plateau issue, a significant gradient vanishing problem in
quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the
efficiency of VSQL in quantum classification via numerical experiments on the
classification of quantum states and the recognition of multi-labeled
handwritten digits. In particular, our VSQL approach outperforms existing
variational quantum classifiers in the test accuracy in the binary case of
handwritten digit recognition and notably requires much fewer parameters. | [
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] |
We describe mechanisms for the allocation of a scarce resource among multiple
users in a way that is efficient, fair, and strategy-proof, but when users do
not know their resource requirements. The mechanism is repeated for multiple
rounds and a user's requirements can change on each round. At the end of each
round, users provide feedback about the allocation they received, enabling the
mechanism to learn user preferences over time. Such situations are common in
the shared usage of a compute cluster among many users in an organisation,
where all teams may not precisely know the amount of resources needed to
execute their jobs. By understating their requirements, users will receive less
than they need and consequently not achieve their goals. By overstating them,
they may siphon away precious resources that could be useful to others in the
organisation. We formalise this task of online learning in fair division via
notions of efficiency, fairness, and strategy-proofness applicable to this
setting, and study this problem under three types of feedback: when the users'
observations are deterministic, when they are stochastic and follow a
parametric model, and when they are stochastic and nonparametric. We derive
mechanisms inspired by the classical max-min fairness procedure that achieve
these requisites, and quantify the extent to which they are achieved via
asymptotic rates. We corroborate these insights with an experimental evaluation
on synthetic problems and a web-serving task. | [
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] |
Kalman Filter (KF) is widely used in various domains to perform sequential
learning or variable estimation. In the context of autonomous vehicles, KF
constitutes the core component of many Advanced Driver Assistance Systems
(ADAS), such as Forward Collision Warning (FCW). It tracks the states
(distance, velocity etc.) of relevant traffic objects based on sensor
measurements. The tracking output of KF is often fed into downstream logic to
produce alerts, which will then be used by human drivers to make driving
decisions in near-collision scenarios. In this paper, we study adversarial
attacks on KF as part of the more complex machine-human hybrid system of
Forward Collision Warning. Our attack goal is to negatively affect human
braking decisions by causing KF to output incorrect state estimations that lead
to false or delayed alerts. We accomplish this by sequentially manipulating
measure ments fed into the KF, and propose a novel Model Predictive Control
(MPC) approach to compute the optimal manipulation. Via experiments conducted
in a simulated driving environment, we show that the attacker is able to
successfully change FCW alert signals through planned manipulation over
measurements prior to the desired target time. These results demonstrate that
our attack can stealthily mislead a distracted human driver and cause vehicle
collisions. | [
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] |
Click-Through Rate (CTR) prediction is critical for industrial recommender
systems, where most deep CTR models follow an Embedding \& Feature Interaction
paradigm. However, the majority of methods focus on designing network
architectures to better capture feature interactions while the feature
embedding, especially for numerical features, has been overlooked. Existing
approaches for numerical features are difficult to capture informative
knowledge because of the low capacity or hard discretization based on the
offline expertise feature engineering. In this paper, we propose a novel
embedding learning framework for numerical features in CTR prediction (AutoDis)
with high model capacity, end-to-end training and unique representation
properties preserved. AutoDis consists of three core components:
meta-embeddings, automatic discretization and aggregation. Specifically, we
propose meta-embeddings for each numerical field to learn global knowledge from
the perspective of field with a manageable number of parameters. Then the
differentiable automatic discretization performs soft discretization and
captures the correlations between the numerical features and meta-embeddings.
Finally, distinctive and informative embeddings are learned via an aggregation
function. Comprehensive experiments on two public and one industrial datasets
are conducted to validate the effectiveness of AutoDis. Moreover, AutoDis has
been deployed onto a mainstream advertising platform, where online A/B test
demonstrates the improvement over the base model by 2.1% and 2.7% in terms of
CTR and eCPM, respectively. In addition, the code of our framework is publicly
available in
MindSpore(https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/recommend/autodis). | [
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] |
Recent works within machine learning have been tackling inputs of
ever-increasing size, with cybersecurity presenting sequence classification
problems of particularly extreme lengths. In the case of Windows executable
malware detection, inputs may exceed $100$ MB, which corresponds to a time
series with $T=100,000,000$ steps. To date, the closest approach to handling
such a task is MalConv, a convolutional neural network capable of processing up
to $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has prevented
further application of CNNs to malware. In this work, we develop a new approach
to temporal max pooling that makes the required memory invariant to the
sequence length $T$. This makes MalConv $116\times$ more memory efficient, and
up to $25.8\times$ faster to train on its original dataset, while removing the
input length restrictions to MalConv. We re-invest these gains into improving
the MalConv architecture by developing a new Global Channel Gating design,
giving us an attention mechanism capable of learning feature interactions
across 100 million time steps in an efficient manner, a capability lacked by
the original MalConv CNN. Our implementation can be found at
https://github.com/NeuromorphicComputationResearchProgram/MalConv2 | [
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] |
Electroencephalography is frequently used for diagnostic evaluation of
various brain-related disorders due to its excellent resolution, non-invasive
nature and low cost. However, manual analysis of EEG signals could be strenuous
and a time-consuming process for experts. It requires long training time for
physicians to develop expertise in it and additionally experts have low
inter-rater agreement (IRA) among themselves. Therefore, many Computer Aided
Diagnostic (CAD) based studies have considered the automation of interpreting
EEG signals to alleviate the workload and support the final diagnosis. In this
paper, we present an automatic binary classification framework for brain
signals in multichannel EEG recordings. We propose to use Wavelet Packet
Decomposition (WPD) techniques to decompose the EEG signals into frequency
sub-bands and extract a set of statistical features from each of the selected
coefficients. Moreover, we propose a novel method to reduce the dimension of
the feature space without compromising the quality of the extracted features.
The extracted features are classified using different Gradient Boosting
Decision Tree (GBDT) based classification frameworks, which are CatBoost,
XGBoost and LightGBM. We used Temple University Hospital EEG Abnormal Corpus
V2.0.0 to test our proposed technique. We found that CatBoost classifier
achieves the binary classification accuracy of 87.68%, and outperforms
state-of-the-art techniques on the same dataset by more than 1% in accuracy and
more than 3% in sensitivity. The obtained results in this research provide
important insights into the usefulness of WPD feature extraction and GBDT
classifiers for EEG classification. | [
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] |
We investigate an important research question for solving the car sequencing
problem, that is, which characteristics make an instance hard to solve? To do
so, we carry out an instance space analysis for the car sequencing problem, by
extracting a vector of problem features to characterize an instance. In order
to visualize the instance space, the feature vectors are projected onto a
two-dimensional space using dimensionality reduction techniques. The resulting
two-dimensional visualizations provide new insights into the characteristics of
the instances used for testing and how these characteristics influence the
behaviours of an optimization algorithm. This analysis guides us in
constructing a new set of benchmark instances with a range of instance
properties. We demonstrate that these new instances are more diverse than the
previous benchmarks, including some instances that are significantly more
difficult to solve. We introduce two new algorithms for solving the car
sequencing problem and compare them with four existing methods from the
literature. Our new algorithms are shown to perform competitively for this
problem but no single algorithm can outperform all others over all instances.
This observation motivates us to build an algorithm selection model based on
machine learning, to identify the niche in the instance space that an algorithm
is expected to perform well on. Our analysis helps to understand problem
hardness and select an appropriate algorithm for solving a given car sequencing
problem instance. | [
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] |
The recent availability of huge, many-dimensional data sets, like those
arising from genome-wide association studies (GWAS), provides many
opportunities for strengthening causal inference. One popular approach is to
utilize these many-dimensional measurements as instrumental variables
(instruments) for improving the causal effect estimate between other pairs of
variables. Unfortunately, searching for proper instruments in a
many-dimensional set of candidates is a daunting task due to the intractable
model space and the fact that we cannot directly test which of these candidates
are valid, so most existing search methods either rely on overly stringent
modeling assumptions or fail to capture the inherent model uncertainty in the
selection process. We show that, as long as at least some of the candidates are
(close to) valid, without knowing a priori which ones, they collectively still
pose enough restrictions on the target interaction to obtain a reliable causal
effect estimate. We propose a general and efficient causal inference algorithm
that accounts for model uncertainty by performing Bayesian model averaging over
the most promising many-dimensional instrumental variable models, while at the
same time employing weaker assumptions regarding the data generating process.
We showcase the efficiency, robustness and predictive performance of our
algorithm through experimental results on both simulated and real-world data. | [
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] |
In modern software engineering, Continuous Integration (CI) has become an
indispensable step towards systematically managing the life cycles of software
development. Large companies struggle with keeping the pipeline updated and
operational, in useful time, due to the large amount of changes and addition of
features, that build on top of each other and have several developers, working
on different platforms. Associated with such software changes, there is always
a strong component of Testing. As teams and projects grow, exhaustive testing
quickly becomes inhibitive, becoming adamant to select the most relevant test
cases earlier, without compromising software quality. We have developed a new
tool called Neural Network Embeeding for Test Case Prioritization (NNE-TCP) is
a novel Machine-Learning (ML) framework that analyses which files were modified
when there was a test status transition and learns relationships between these
files and tests by mapping them into multidimensional vectors and grouping them
by similarity. When new changes are made, tests that are more likely to be
linked to the files modified are prioritized, reducing the resources needed to
find newly introduced faults. Furthermore, NNE-TCP enables entity visualization
in low-dimensional space, allowing for other manners of grouping files and
tests by similarity and to reduce redundancies. By applying NNE-TCP, we show
for the first time that the connection between modified files and tests is
relevant and competitive relative to other traditional methods. | [
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] |
Cryptanalysis on standard quantum cryptographic systems generally involves
finding optimal adversarial attack strategies on the underlying protocols. The
core principle of modelling quantum attacks in many cases reduces to the
adversary's ability to clone unknown quantum states which facilitates the
extraction of some meaningful secret information. Explicit optimal attack
strategies typically require high computational resources due to large circuit
depths or, in many cases, are unknown. In this work, we propose variational
quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm
which allows an adversary to obtain optimal (approximate) cloning strategies
with short depth quantum circuits, trained using hybrid classical-quantum
techniques. The algorithm contains operationally meaningful cost functions with
theoretical guarantees, quantum circuit structure learning and gradient descent
based optimisation. Our approach enables the end-to-end discovery of hardware
efficient quantum circuits to clone specific families of quantum states, which
in turn leads to an improvement in cloning fidelites when implemented on
quantum hardware: the Rigetti Aspen chip. Finally, we connect these results to
quantum cryptographic primitives, in particular quantum coin flipping. We
derive attacks on two protocols as examples, based on quantum cloning and
facilitated by VQC. As a result, our algorithm can improve near term attacks on
these protocols, using approximate quantum cloning as a resource. | [
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] |
Synthesizing programs from examples requires searching over a vast,
combinatorial space of possible programs. In this search process, a key
challenge is representing the behavior of a partially written program before it
can be executed, to judge if it is on the right track and predict where to
search next. We introduce a general technique for representing partially
written programs in a program synthesis engine. We take inspiration from the
technique of abstract interpretation, in which an approximate execution model
is used to determine if an unfinished program will eventually satisfy a goal
specification. Here we learn an approximate execution model implemented as a
modular neural network. By constructing compositional program representations
that implicitly encode the interpretation semantics of the underlying
programming language, we can represent partial programs using a flexible
combination of concrete execution state and learned neural representations,
using the learned approximate semantics when concrete semantics are not known
(in unfinished parts of the program). We show that these hybrid neuro-symbolic
representations enable execution-guided synthesizers to use more powerful
language constructs, such as loops and higher-order functions, and can be used
to synthesize programs more accurately for a given search budget than pure
neural approaches in several domains. | [
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] |
The increasing bandwidth requirement of new wireless applications has lead to
standardization of the millimeter wave spectrum for high-speed wireless
communication. The millimeter wave spectrum is part of 5G and covers
frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10
to 1 mm. Although millimeter wave is often considered as a communication
medium, it has also proved to be an excellent 'sensor', thanks to its narrow
beams, operation across a wide bandwidth, and interaction with atmospheric
constituents. In this paper, which is to the best of our knowledge the first
review that completely covers millimeter wave sensing application pipelines, we
provide a comprehensive overview and analysis of different basic application
pipeline building blocks, including hardware, algorithms, analytical models,
and model evaluation techniques. The review also provides a taxonomy that
highlights different millimeter wave sensing application domains. By performing
a thorough analysis, complying with the systematic literature review
methodology and reviewing 165 papers, we not only extend previous
investigations focused only on communication aspects of the millimeter wave
technology and using millimeter wave technology for active imaging, but also
highlight scientific and technological challenges and trends, and provide a
future perspective for applications of millimeter wave as a sensing technology. | [
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Recent technological progress in the development of Unmanned Aerial Vehicles
(UAVs) together with decreasing acquisition costs make the application of drone
fleets attractive for a wide variety of tasks. In agriculture, disaster
management, search and rescue operations, commercial and military applications,
the advantage of applying a fleet of drones originates from their ability to
cooperate autonomously. Multi-Agent Reinforcement Learning approaches that aim
to optimize a neural network based control policy, such as the best performing
actor-critic policy gradient algorithms, struggle to effectively back-propagate
errors of distinct rewards signal sources and tend to favor lucrative signals
while neglecting coordination and exploitation of previously learned
similarities. We propose a Multi-Critic Policy Optimization architecture with
multiple value estimating networks and a novel advantage function that
optimizes a stochastic actor policy network to achieve optimal coordination of
agents. Consequently, we apply the algorithm to several tasks that require the
collaboration of multiple drones in a physics-based reinforcement learning
environment. Our approach achieves a stable policy network update and
similarity in reward signal development for an increasing number of agents. The
resulting policy achieves optimal coordination and compliance with constraints
such as collision avoidance. | [
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] |
Sensors are being extensively deployed and are expected to expand at
significant rates in the coming years. They typically generate a large volume
of data on the internet of things (IoT) application areas like smart cities,
intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog
computing are potential and competitive strategies for collecting, processing,
and distributing IoT data. However, cloud, edge, and fog-based solutions need
to tackle the distribution of a high volume of IoT data efficiently through
constrained and limited resource network infrastructures. This paper addresses
the issue of conveying a massive volume of IoT data through a network with
limited communications resources (bandwidth) using a cognitive communications
resource allocation based on Reinforcement Learning (RL) with SARSA algorithm.
The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe
architecture to access massive and highly distributed IoT data. It is
demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on
SARSA enhances the IoT aggregator buffer occupation and network link
utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing
according to the Pub/Sub topic's priority and network constraint requirements. | [
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] |
Microarray technology is known as one of the most important tools for
collecting DNA expression data. This technology allows researchers to
investigate and examine types of diseases and their origins. However,
microarray data are often associated with challenges such as small sample size,
a significant number of genes, imbalanced data, etc. that make classification
models inefficient. Thus, a new hybrid solution based on multi-filter and
adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is
presented to solve the gene selection problem and construct the Ensemble
Classifier. In the proposed solution, to reduce the dataset's dimensions, a
multi-filter model uses a combination of five filter methods to remove
redundant and irrelevant genes. Then, an AC-MOFOA based on the concepts of
non-dominated sorting, crowding distance, chaos theory, and adaptive operators
is presented. AC-MOFOA as a wrapper method aimed at reducing dataset
dimensions, optimizing KELM, and increasing the accuracy of the classification,
simultaneously. Next, in this method, an ensemble classifier model is presented
using AC-MOFOA results to classify microarray data. The performance of the
proposed algorithm was evaluated on nine public microarray datasets, and its
results were compared in terms of the number of selected genes, classification
efficiency, execution time, time complexity, and hypervolume indicator
criterion with five hybrid multi-objective methods. According to the results,
the proposed hybrid method could increase the accuracy of the KELM in most
datasets by reducing the dataset's dimensions and achieve similar or superior
performance compared to other multi-objective methods. Furthermore, the
proposed Ensemble Classifier model could provide better classification accuracy
and generalizability in microarray data compared to conventional ensemble
methods. | [
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] |
Psychological theories of habit posit that when a strong habit is formed
through behavioral repetition, it can trigger behavior automatically in the
same environment. Given the reciprocal relationship between habit and behavior,
changing lifestyle behaviors (e.g., toothbrushing) is largely a task of
breaking old habits and creating new and healthy ones. Thus, representing
users' habit strengths can be very useful for behavior change support systems
(BCSS), for example, to predict behavior or to decide when an intervention
reaches its intended effect. However, habit strength is not directly observable
and existing self-report measures are taxing for users. In this paper, built on
recent computational models of habit formation, we propose a method to enable
intelligent systems to compute habit strength based on observable behavior. The
hypothesized advantage of using computed habit strength for behavior prediction
was tested using data from two intervention studies, where we trained
participants to brush their teeth twice a day for three weeks and monitored
their behaviors using accelerometers. Through hierarchical cross-validation, we
found that for the task of predicting future brushing behavior, computed habit
strength clearly outperformed self-reported habit strength (in both studies)
and was also superior to models based on past behavior frequency (in the larger
second study). Our findings provide initial support for our theory-based
approach of modeling user habits and encourages the use of habit computation to
deliver personalized and adaptive interventions. | [
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] |
Counterfactual inference is a useful tool for comparing outcomes of
interventions on complex systems. It requires us to represent the system in
form of a structural causal model, complete with a causal diagram,
probabilistic assumptions on exogenous variables, and functional assignments.
Specifying such models can be extremely difficult in practice. The process
requires substantial domain expertise, and does not scale easily to large
systems, multiple systems, or novel system modifications. At the same time,
many application domains, such as molecular biology, are rich in structured
causal knowledge that is qualitative in nature. This manuscript proposes a
general approach for querying a causal biological knowledge graph, and
converting the qualitative result into a quantitative structural causal model
that can learn from data to answer the question. We demonstrate the
feasibility, accuracy and versatility of this approach using two case studies
in systems biology. The first demonstrates the appropriateness of the
underlying assumptions and the accuracy of the results. The second demonstrates
the versatility of the approach by querying a knowledge base for the molecular
determinants of a severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to
estimate the causal effect of medical countermeasures for severely ill
patients. | [
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] |
Although AI holds promise for improving human decision making in societally
critical domains, it remains an open question how human-AI teams can reliably
outperform AI alone and human alone in challenging prediction tasks (also known
as complementary performance). We explore two directions to understand the gaps
in achieving complementary performance. First, we argue that the typical
experimental setup limits the potential of human-AI teams. To account for lower
AI performance out-of-distribution than in-distribution because of distribution
shift, we design experiments with different distribution types and investigate
human performance for both in-distribution and out-of-distribution examples.
Second, we develop novel interfaces to support interactive explanations so that
humans can actively engage with AI assistance. Using virtual pilot studies and
large-scale randomized experiments across three tasks, we demonstrate a clear
difference between in-distribution and out-of-distribution, and observe mixed
results for interactive explanations: while interactive explanations improve
human perception of AI assistance's usefulness, they may reinforce human biases
and lead to limited performance improvement. Overall, our work points out
critical challenges and future directions towards enhancing human performance
with AI assistance. | [
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] |
Polymer electrolytes are promising candidates for the next generation
lithium-ion battery technology. Large scale screening of polymer electrolytes
is hindered by the significant cost of molecular dynamics (MD) simulation in
amorphous systems: the amorphous structure of polymers requires multiple,
repeated sampling to reduce noise and the slow relaxation requires long
simulation time for convergence. Here, we accelerate the screening with a
multi-task graph neural network that learns from a large amount of noisy,
unconverged, short MD data and a small number of converged, long MD data. We
achieve accurate predictions of 4 different converged properties and screen a
space of 6247 polymers that is orders of magnitude larger than previous
computational studies. Further, we extract several design principles for
polymer electrolytes and provide an open dataset for the community. Our
approach could be applicable to a broad class of material discovery problems
that involve the simulation of complex, amorphous materials. | [
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] |
The move from boxed products to services and the widespread adoption of cloud
computing has had a huge impact on the software development life cycle and
DevOps processes. Particularly, incident management has become critical for
developing and operating large-scale services. Prior work on incident
management has heavily focused on the challenges with incident triaging and
de-duplication. In this work, we address the fundamental problem of structured
knowledge extraction from service incidents. We have built SoftNER, a framework
for mining Knowledge Graphs from incident reports. First, we build a novel
multi-task learning based BiLSTM-CRF model which leverages not just the
semantic context but also the data-types for extracting factual information in
the form of named entities. Next, we present an approach to mine relations
between the named entities for automatically constructing knowledge graphs. We
have deployed SoftNER at Microsoft, a major cloud service provider and have
evaluated it on more than 2 months of cloud incidents. We show that the
unsupervised machine learning pipeline has a high precision of 0.96. Our
multi-task learning based deep learning model also outperforms the
state-of-the-art NER models. Lastly, using the knowledge extracted by SoftNER,
we are able to build accurate models for applications such as incident triaging
and recommending entities based on their relevance to incident titles. | [
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] |
Computerized Adaptive Testing (CAT) is emerging as a promising testing
application in many scenarios, such as education, game and recruitment, which
targets at diagnosing the knowledge mastery levels of examinees on required
concepts. It shows the advantage of tailoring a personalized testing procedure
for each examinee, which selects questions step by step, depending on her
performance. While there are many efforts on developing CAT systems, existing
solutions generally follow an inflexible model-specific fashion. That is, they
need to observe a specific cognitive model which can estimate examinee's
knowledge levels and design the selection strategy according to the model
estimation. In this paper, we study a novel model-agnostic CAT problem, where
we aim to propose a flexible framework that can adapt to different cognitive
models. Meanwhile, this work also figures out CAT solution with addressing the
problem of how to generate both high-quality and diverse questions
simultaneously, which can give a comprehensive knowledge diagnosis for each
examinee. Inspired by Active Learning, we propose a novel framework, namely
Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three
sophisticated modules including Quality Module, Diversity Module and Importance
Module. Extensive experimental results on two real-world datasets clearly
demonstrate that our MAAT can support CAT with guaranteeing both quality and
diversity perspectives. | [
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] |
Finding an expert plays a crucial role in driving successful collaborations
and speeding up high-quality research development and innovations. However, the
rapid growth of scientific publications and digital expertise data makes
identifying the right experts a challenging problem. Existing approaches for
finding experts given a topic can be categorised into information retrieval
techniques based on vector space models, document language models, and
graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new
ensemble model for expert finding, that integrates a novel $N$-gram vector
space model, denoted as $n$VSM, and a graph-based model, denoted as
$\textit{$\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm.
The key of $n$VSM is to exploit recent inverse document frequency weighting
method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into
$\textit{$\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate
$\textit{ExpFinder}$ on four different datasets from the academic domains in
comparison with six different expert finding models. The evaluation results
show that $\textit{ExpFinder}$ is a highly effective model for expert finding,
substantially outperforming all the compared models in 19% to 160.2%. | [
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] |
A universal fault-tolerant quantum computer that can solve efficiently
problems such as integer factorization and unstructured database search
requires millions of qubits with low error rates and long coherence times.
While the experimental advancement towards realizing such devices will
potentially take decades of research, noisy intermediate-scale quantum (NISQ)
computers already exist. These computers are composed of hundreds of noisy
qubits, i.e. qubits that are not error-corrected, and therefore perform
imperfect operations in a limited coherence time. In the search for quantum
advantage with these devices, algorithms have been proposed for applications in
various disciplines spanning physics, machine learning, quantum chemistry and
combinatorial optimization. The goal of such algorithms is to leverage the
limited available resources to perform classically challenging tasks. In this
review, we provide a thorough summary of NISQ computational paradigms and
algorithms. We discuss the key structure of these algorithms, their
limitations, and advantages. We additionally provide a comprehensive overview
of various benchmarking and software tools useful for programming and testing
NISQ devices. | [
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] |
The objective of personalized learning is to design an effective knowledge
acquisition track that matches the learner's strengths and bypasses her
weaknesses to ultimately meet her desired goal. This concept emerged several
years ago and is being adopted by a rapidly-growing number of educational
institutions around the globe. In recent years, the boost of artificial
intelligence (AI) and machine learning (ML), together with the advances in big
data analysis, has unfolded novel perspectives to enhance personalized
education in numerous dimensions. By taking advantage of AI/ML methods, the
educational platform precisely acquires the student's characteristics. This is
done, in part, by observing the past experiences as well as analyzing the
available big data through exploring the learners' features and similarities.
It can, for example, recommend the most appropriate content among numerous
accessible ones, advise a well-designed long-term curriculum, connect
appropriate learners by suggestion, accurate performance evaluation, and the
like. Still, several aspects of AI-based personalized education remain
unexplored. These include, among others, compensating for the adverse effects
of the absence of peers, creating and maintaining motivations for learning,
increasing diversity, removing the biases induced by the data and algorithms,
and the like. In this paper, while providing a brief review of state-of-the-art
research, we investigate the challenges of AI/ML-based personalized education
and discuss potential solutions. | [
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The Internet of Things (IoT) is becoming an indispensable part of everyday
life, enabling a variety of emerging services and applications. However, the
presence of rogue IoT devices has exposed the IoT to untold risks with severe
consequences. The first step in securing the IoT is detecting rogue IoT devices
and identifying legitimate ones. Conventional approaches use cryptographic
mechanisms to authenticate and verify legitimate devices' identities. However,
cryptographic protocols are not available in many systems. Meanwhile, these
methods are less effective when legitimate devices can be exploited or
encryption keys are disclosed. Therefore, non-cryptographic IoT device
identification and rogue device detection become efficient solutions to secure
existing systems and will provide additional protection to systems with
cryptographic protocols. Non-cryptographic approaches require more effort and
are not yet adequately investigated. In this paper, we provide a comprehensive
survey on machine learning technologies for the identification of IoT devices
along with the detection of compromised or falsified ones from the viewpoint of
passive surveillance agents or network operators. We classify the IoT device
identification and detection into four categories: device-specific pattern
recognition, Deep Learning enabled device identification, unsupervised device
identification, and abnormal device detection. Meanwhile, we discuss various
ML-related enabling technologies for this purpose. These enabling technologies
include learning algorithms, feature engineering on network traffic traces and
wireless signals, continual learning, and abnormality detection. | [
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] |
Human Activity Recognition (HAR), based on machine and deep learning
algorithms is considered one of the most promising technologies to monitor
professional and daily life activities for different categories of people
(e.g., athletes, elderly, kids, employers) in order to provide a variety of
services related, for example to well-being, empowering of technical
performances, prevention of risky situation, and educational purposes. However,
the analysis of the effectiveness and the efficiency of HAR methodologies
suffers from the lack of a standard workflow, which might represent the
baseline for the estimation of the quality of the developed pattern recognition
models. This makes the comparison among different approaches a challenging
task. In addition, researchers can make mistakes that, when not detected,
definitely affect the achieved results. To mitigate such issues, this paper
proposes an open-source automatic and highly configurable framework, named
B-HAR, for the definition, standardization, and development of a baseline
framework in order to evaluate and compare HAR methodologies. It implements the
most popular data processing methods for data preparation and the most commonly
used machine and deep learning pattern recognition models. | [
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] |
Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses
data privacy, security, access rights and access to heterogeneous information
issues by training a global model using distributed nodes. Despite its
advantages, there is an increased potential for cyberattacks on FL-based ML
techniques that can undermine the benefits. Model-poisoning attacks on FL
target the availability of the model. The adversarial objective is to disrupt
the training. We propose attestedFL, a defense mechanism that monitors the
training of individual nodes through state persistence in order to detect a
malicious worker. A fine-grained assessment of the history of the worker
permits the evaluation of its behavior in time and results in innovative
detection strategies. We present three lines of defense that aim at assessing
if the worker is reliable by observing if the node is really training,
advancing towards a goal. Our defense exposes an attacker's malicious behavior
and removes unreliable nodes from the aggregation process so that the FL
process converge faster. Through extensive evaluations and against various
adversarial settings, attestedFL increased the accuracy of the model between
12% to 58% under different scenarios such as attacks performed at different
stages of convergence, attackers colluding and continuous attacks. | [
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The current state-of-the-art Android malware detection systems are based on
machine learning and deep learning models. Despite having superior performance,
these models are susceptible to adversarial attacks. Therefore in this paper,
we developed eight Android malware detection models based on machine learning
and deep neural network and investigated their robustness against adversarial
attacks. For this purpose, we created new variants of malware using
Reinforcement Learning, which will be misclassified as benign by the existing
Android malware detection models. We propose two novel attack strategies,
namely single policy attack and multiple policy attack using reinforcement
learning for white-box and grey-box scenario respectively. Putting ourselves in
the adversary's shoes, we designed adversarial attacks on the detection models
with the goal of maximizing fooling rate, while making minimum modifications to
the Android application and ensuring that the app's functionality and behavior
do not change. We achieved an average fooling rate of 44.21% and 53.20% across
all the eight detection models with a maximum of five modifications using a
single policy attack and multiple policy attack, respectively. The highest
fooling rate of 86.09% with five changes was attained against the decision
tree-based model using the multiple policy approach. Finally, we propose an
adversarial defense strategy that reduces the average fooling rate by threefold
to 15.22% against a single policy attack, thereby increasing the robustness of
the detection models i.e. the proposed model can effectively detect variants
(metamorphic) of malware. The experimental analysis shows that our proposed
Android malware detection system using reinforcement learning is more robust
against adversarial attacks. | [
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] |
Counterfactual explanations, which deal with "why not?" scenarios, can
provide insightful explanations to an AI agent's behavior. In this work, we
focus on generating counterfactual explanations for deep reinforcement learning
(RL) agents which operate in visual input environments like Atari. We introduce
counterfactual state explanations, a novel example-based approach to
counterfactual explanations based on generative deep learning. Specifically, a
counterfactual state illustrates what minimal change is needed to an Atari game
image such that the agent chooses a different action. We also evaluate the
effectiveness of counterfactual states on human participants who are not
machine learning experts. Our first user study investigates if humans can
discern if the counterfactual state explanations are produced by the actual
game or produced by a generative deep learning approach. Our second user study
investigates if counterfactual state explanations can help non-expert
participants identify a flawed agent; we compare against a baseline approach
based on a nearest neighbor explanation which uses images from the actual game.
Our results indicate that counterfactual state explanations have sufficient
fidelity to the actual game images to enable non-experts to more effectively
identify a flawed RL agent compared to the nearest neighbor baseline and to
having no explanation at all. | [
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] |
In this paper, we propose a novel mechanism to normalize metamorphic and
obfuscated malware down at the opcode level and hence create an advanced
metamorphic malware de-obfuscation and defense system. We name this system
DRLDO, for Deep Reinforcement Learning based De-Obfuscator. With the inclusion
of the DRLDO as a sub-component, an existing Intrusion Detection System could
be augmented with defensive capabilities against 'zero-day' attacks from
obfuscated and metamorphic variants of existing malware. This gains importance,
not only because there exists no system to date that uses advanced DRL to
intelligently and automatically normalize obfuscation down even to the opcode
level, but also because the DRLDO system does not mandate any changes to the
existing IDS. The DRLDO system does not even mandate the IDS' classifier to be
retrained with any new dataset containing obfuscated samples. Hence DRLDO could
be easily retrofitted into any existing IDS deployment. We designed, developed,
and conducted experiments on the system to evaluate the same against
multiple-simultaneous attacks from obfuscations generated from malware samples
from a standardized dataset that contains multiple generations of malware.
Experimental results prove that DRLDO was able to successfully make the
otherwise un-detectable obfuscated variants of the malware detectable by an
existing pre-trained malware classifier. The detection probability was raised
well above the cut-off mark to 0.6 for the classifier to detect the obfuscated
malware unambiguously. Further, the de-obfuscated variants generated by DRLDO
achieved a very high correlation (of 0.99) with the base malware. This
observation validates that the DRLDO system is actually learning to
de-obfuscate and not exploiting a trivial trick. | [
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Medical toxicology is the clinical specialty that treats the toxic effects of
substances, be it an overdose, a medication error, or a scorpion sting. The
volume of toxicological knowledge and research has, as with other medical
specialties, outstripped the ability of the individual clinician to entirely
master and stay current with it. The application of machine learning techniques
to medical toxicology is challenging because initial treatment decisions are
often based on a few pieces of textual data and rely heavily on prior
knowledge. ML techniques often do not represent knowledge in a way that is
transparent for the physician, raising barriers to usability. Rule-based
systems and decision tree learning are more transparent approaches, but often
generalize poorly and require expert curation to implement and maintain. Here,
we construct a probabilistic logic network to represent a portion of the
knowledge base of a medical toxicologist. Our approach transparently mimics the
knowledge representation and clinical decision-making of practicing clinicians.
The software, dubbed Tak, performs comparably to humans on straightforward
cases and intermediate difficulty cases, but is outperformed by humans on
challenging clinical cases. Tak outperforms a decision tree classifier at all
levels of difficulty. Probabilistic logic provides one form of explainable
artificial intelligence that may be more acceptable for use in healthcare, if
it can achieve acceptable levels of performance. | [
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] |
An emerging amount of intelligent applications have been developed with the
surge of Machine Learning (ML). Deep Neural Networks (DNNs) have demonstrated
unprecedented performance across various fields such as medical diagnosis and
autonomous driving. While DNNs are widely employed in security-sensitive
fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that
are controlled and activated by the stealthy trigger. We call this vulnerable
model adversarial artificial intelligence (AI). In this paper, we target to
design a robust Trojan detection scheme that inspects whether a pre-trained AI
model has been Trojaned before its deployment. Prior works are oblivious of the
intrinsic property of trigger distribution and try to reconstruct the trigger
pattern using simple heuristics, i.e., stimulating the given model to incorrect
outputs. As a result, their detection time and effectiveness are limited. We
leverage the observation that the pixel trigger typically features spatial
dependency and propose TAD, the first trigger approximation based Trojan
detection framework that enables fast and scalable search of the trigger in the
input space. Furthermore, TAD can also detect Trojans embedded in the feature
space where certain filter transformations are used to activate the Trojan. We
perform extensive experiments to investigate the performance of the TAD across
various datasets and ML models. Empirical results show that TAD achieves a
ROC-AUC score of 0:91 on the public TrojAI dataset 1 and the average detection
time per model is 7:1 minutes. | [
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] |
Many of the traditional recommendation algorithms are designed based on the
fundamental idea of mining or learning correlative patterns from data to
estimate the user-item correlative preference. However, pure correlative
learning may lead to Simpson's paradox in predictions, and thus results in
sacrificed recommendation performance. Simpson's paradox is a well-known
statistical phenomenon, which causes confusions in statistical conclusions and
ignoring the paradox may result in inaccurate decisions. Fortunately, causal
and counterfactual modeling can help us to think outside of the observational
data for user modeling and personalization so as to tackle such issues. In this
paper, we propose Causal Collaborative Filtering (CCF) -- a general framework
for modeling causality in collaborative filtering and recommendation. We
provide a unified causal view of CF and mathematically show that many of the
traditional CF algorithms are actually special cases of CCF under simplified
causal graphs. We then propose a conditional intervention approach for
$do$-operations so that we can estimate the user-item causal preference based
on the observational data. Finally, we further propose a general counterfactual
constrained learning framework for estimating the user-item preferences.
Experiments are conducted on two types of real-world datasets -- traditional
and randomized trial data -- and results show that our framework can improve
the recommendation performance and reduce the Simpson's paradox problem of many
CF algorithms. | [
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Pushing is an essential non-prehensile manipulation skill used for tasks
ranging from pre-grasp manipulation to scene rearrangement, reasoning about
object relations in the scene, and thus pushing actions have been widely
studied in robotics. The effective use of pushing actions often requires an
understanding of the dynamics of the manipulated objects and adaptation to the
discrepancies between prediction and reality. For this reason, effect
prediction and parameter estimation with pushing actions have been heavily
investigated in the literature. However, current approaches are limited because
they either model systems with a fixed number of objects or use image-based
representations whose outputs are not very interpretable and quickly accumulate
errors. In this paper, we propose a graph neural network based framework for
effect prediction and parameter estimation of pushing actions by modeling
object relations based on contacts or articulations. Our framework is validated
both in real and simulated environments containing different shaped multi-part
objects connected via different types of joints and objects with different
masses, and it outperforms image-based representations on physics prediction.
Our approach enables the robot to predict and adapt the effect of a pushing
action as it observes the scene. It can also be used for tool manipulation with
never-seen tools. Further, we demonstrate 6D effect prediction in the lever-up
action in the context of robot-based hard-disk disassembly. | [
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] |
Inference attacks against Machine Learning (ML) models allow adversaries to
learn sensitive information about training data, model parameters, etc. While
researchers have studied, in depth, several kinds of attacks, they have done so
in isolation. As a result, we lack a comprehensive picture of the risks caused
by the attacks, e.g., the different scenarios they can be applied to, the
common factors that influence their performance, the relationship among them,
or the effectiveness of possible defenses. In this paper, we fill this gap by
presenting a first-of-its-kind holistic risk assessment of different inference
attacks against machine learning models. We concentrate on four attacks --
namely, membership inference, model inversion, attribute inference, and model
stealing -- and establish a threat model taxonomy.
Our extensive experimental evaluation, run on five model architectures and
four image datasets, shows that the complexity of the training dataset plays an
important role with respect to the attack's performance, while the
effectiveness of model stealing and membership inference attacks are negatively
correlated. We also show that defenses like DP-SGD and Knowledge Distillation
can only mitigate some of the inference attacks. Our analysis relies on a
modular re-usable software, ML-Doctor, which enables ML model owners to assess
the risks of deploying their models, and equally serves as a benchmark tool for
researchers and practitioners. | [
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] |
The use of reinforcement learning has proven to be very promising for solving
complex activities without human supervision during their learning process.
However, their successful applications are predominantly focused on fictional
and entertainment problems - such as games. Based on the above, this work aims
to shed light on the application of reinforcement learning to solve this
relevant real-world problem, the genome assembly. By expanding the only
approach found in the literature that addresses this problem, we carefully
explored the aspects of intelligent agent learning, performed by the Q-learning
algorithm, to understand its suitability to be applied in scenarios whose
characteristics are more similar to those faced by real genome projects. The
improvements proposed here include changing the previously proposed reward
system and including state space exploration optimization strategies based on
dynamic pruning and mutual collaboration with evolutionary computing. These
investigations were tried on 23 new environments with larger inputs than those
used previously. All these environments are freely available on the internet
for the evolution of this research by the scientific community. The results
suggest consistent performance progress using the proposed improvements,
however, they also demonstrate the limitations of them, especially related to
the high dimensionality of state and action spaces. We also present, later, the
paths that can be traced to tackle genome assembly efficiently in real
scenarios considering recent, successfully reinforcement learning applications
- including deep reinforcement learning - from other domains dealing with
high-dimensional inputs. | [
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Modern radio telescopes produce unprecedented amounts of data, which are
passed through many processing pipelines before the delivery of scientific
results. Hyperparameters of these pipelines need to be tuned by hand to produce
optimal results. Because many thousands of observations are taken during a
lifetime of a telescope and because each observation will have its unique
settings, the fine tuning of pipelines is a tedious task. In order to automate
this process of hyperparameter selection in data calibration pipelines, we
introduce the use of reinforcement learning. We test two reinforcement learning
techniques, twin delayed deep deterministic policy gradient (TD3) and soft
actor-critic (SAC), to train an autonomous agent to perform this fine tuning.
For the sake of generalization, we consider the pipeline to be a black-box
system where the summarized state of the performance of the pipeline is used by
the autonomous agent. The autonomous agent trained in this manner is able to
determine optimal settings for diverse observations and is therefore able to
perform 'smart' calibration, minimizing the need for human intervention. | [
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] |
A new federated learning (FL) framework enabled by large-scale wireless
connectivity is proposed for designing the autonomous controller of connected
and autonomous vehicles (CAVs). In this framework, the learning models used by
the controllers are collaboratively trained among a group of CAVs. To capture
the varying CAV participation in the FL training process and the diverse local
data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is
proposed that accounts for the mobility of CAVs, the wireless fading channels,
as well as the unbalanced and nonindependent and identically distributed data
across CAVs. A rigorous convergence analysis is performed for the proposed
algorithm to identify how fast the CAVs converge to using the optimal
autonomous controller. In particular, the impacts of varying CAV participation
in the FL process and diverse CAV data quality on the convergence of the
proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an
incentive mechanism based on contract theory is designed to improve the FL
convergence speed. Simulation results using real vehicular data traces show
that the proposed DFP-based controller can accurately track the target CAV
speed over time and under different traffic scenarios. Moreover, the results
show that the proposed DFP algorithm has a much faster convergence compared to
popular FL algorithms such as federated averaging (FedAvg) and federated
proximal (FedProx). The results also validate the feasibility of the
contract-theoretic incentive mechanism and show that the proposed mechanism can
improve the convergence speed of the DFP algorithm by 40% compared to the
baselines. | [
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] |
Testing and evaluation is a crucial step in the development and deployment of
Connected and Automated Vehicles (CAVs). To comprehensively evaluate the
performance of CAVs, it is of necessity to test the CAVs in safety-critical
scenarios, which rarely happen in naturalistic driving environment. Therefore,
how to purposely and systematically generate these corner cases becomes an
important problem. Most existing studies focus on generating adversarial
examples for perception systems of CAVs, whereas limited efforts have been put
on the decision-making systems, which is the highlight of this paper. As the
CAVs need to interact with numerous background vehicles (BVs) for a long
duration, variables that define the corner cases are usually high dimensional,
which makes the generation a challenging problem. In this paper, a unified
framework is proposed to generate corner cases for the decision-making systems.
To address the challenge brought by high dimensionality, the driving
environment is formulated based on Markov Decision Process, and the deep
reinforcement learning techniques are applied to learn the behavior policy of
BVs. With the learned policy, BVs will behave and interact with the CAVs more
aggressively, resulting in more corner cases. To further analyze the generated
corner cases, the techniques of feature extraction and clustering are utilized.
By selecting representative cases of each cluster and outliers, the valuable
corner cases can be identified from all generated corner cases. Simulation
results of a highway driving environment show that the proposed methods can
effectively generate and identify the valuable corner cases. | [
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] |
Recent years have witnessed the rapid accumulation of massive electronic
medical records (EMRs), which highly support the intelligent medical services
such as drug recommendation. However, prior arts mainly follow the traditional
recommendation strategies like collaborative filtering, which usually treat
individual drugs as mutually independent, while the latent interactions among
drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To
that end, in this paper, we target at developing a new paradigm for drug
package recommendation with considering the interaction effect within drugs, in
which the interaction effects could be affected by patient conditions.
Specifically, we first design a pre-training method based on neural
collaborative filtering to get the initial embedding of patients and drugs.
Then, the drug interaction graph will be initialized based on medical records
and domain knowledge. Along this line, we propose a new Drug Package
Recommendation (DPR) framework with two variants, respectively DPR on Weighted
Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in
which each the interactions will be described as signed weights or attribute
vectors. In detail, a mask layer is utilized to capture the impact of patient
condition, and graph neural networks (GNNs) are leveraged for the final graph
induction task to embed the package. Extensive experiments on a real-world data
set from a first-rate hospital demonstrate the effectiveness of our DPR
framework compared with several competitive baseline methods, and further
support the heuristic study for the drug package generation task with adequate
performance. | [
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] |
Microservices are becoming the defacto design choice for software
architecture. It involves partitioning the software components into finer
modules such that the development can happen independently. It also provides
natural benefits when deployed on the cloud since resources can be allocated
dynamically to necessary components based on demand. Therefore, enterprises as
part of their journey to cloud, are increasingly looking to refactor their
monolith application into one or more candidate microservices; wherein each
service contains a group of software entities (e.g., classes) that are
responsible for a common functionality. Graphs are a natural choice to
represent a software system. Each software entity can be represented as nodes
and its dependencies with other entities as links. Therefore, this problem of
refactoring can be viewed as a graph based clustering task. In this work, we
propose a novel method to adapt the recent advancements in graph neural
networks in the context of code to better understand the software and apply
them in the clustering task. In that process, we also identify the outliers in
the graph which can be directly mapped to top refactor candidates in the
software. Our solution is able to improve state-of-the-art performance compared
to works from both software engineering and existing graph representation based
techniques. | [
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] |
Speaker recognition deals with recognizing speakers by their speech. Most
speaker recognition systems are built upon two stages, the first stage extracts
low dimensional correlation embeddings from speech, and the second performs the
classification task. The robustness of a speaker recognition system mainly
depends on the extraction process of speech embeddings, which are primarily
pre-trained on a large-scale dataset. As the embedding systems are pre-trained,
the performance of speaker recognition models greatly depends on domain
adaptation policy, which may reduce if trained using inadequate data. This
paper introduces a speaker recognition strategy dealing with unlabeled data,
which generates clusterable embedding vectors from small fixed-size speech
frames. The unsupervised training strategy involves an assumption that a small
speech segment should include a single speaker. Depending on such a belief, a
pairwise constraint is constructed with noise augmentation policies, used to
train AutoEmbedder architecture that generates speaker embeddings. Without
relying on domain adaption policy, the process unsupervisely produces
clusterable speaker embeddings, termed unsupervised vectors (u-vectors). The
evaluation is concluded in two popular speaker recognition datasets for English
language, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to
illustrate the diversity of the domain shifts for speaker recognition systems.
Finally, we conclude that the proposed approach achieves satisfactory
performance using pairwise architectures. | [
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] |
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as
metamodels. Standard GPs, however, can only handle numerical or quantitative
variables. In this paper, we introduce latent map Gaussian processes (LMGPs)
that inherit the attractive properties of GPs and are also applicable to mixed
data which have both quantitative and qualitative inputs. The core idea behind
LMGPs is to learn a continuous, low-dimensional latent space or manifold which
encodes all qualitative inputs. To learn this manifold, we first assign a
unique prior vector representation to each combination of qualitative inputs.
We then use a low-rank linear map to project these priors on a manifold that
characterizes the posterior representations. As the posteriors are
quantitative, they can be directly used in any standard correlation function
such as the Gaussian or Matern. Hence, the optimal map and the corresponding
manifold, along with other hyperparameters of the correlation function, can be
systematically learned via maximum likelihood estimation. Through a wide range
of analytic and real-world examples, we demonstrate the advantages of LMGPs
over state-of-the-art methods in terms of accuracy and versatility. In
particular, we show that LMGPs can handle variable-length inputs, have an
explainable neural network interpretation, and provide insights into how
qualitative inputs affect the response or interact with each other. We also
employ LMGPs in Bayesian optimization and illustrate that they can discover
optimal compound compositions more efficiently than conventional methods that
convert compositions to qualitative variables via manual featurization. | [
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] |
Human brain performs remarkably well in segregating a particular speaker from
interfering ones in a multi-speaker scenario. It has been recently shown that
we can quantitatively evaluate the segregation capability by modelling the
relationship between the speech signals present in an auditory scene and the
cortical signals of the listener measured using electroencephalography (EEG).
This has opened up avenues to integrate neuro-feedback into hearing aids
whereby the device can infer user's attention and enhance the attended speaker.
Commonly used algorithms to infer the auditory attention are based on linear
systems theory where the speech cues such as envelopes are mapped on to the EEG
signals. Here, we present a joint convolutional neural network (CNN) - long
short-term memory (LSTM) model to infer the auditory attention. Our joint
CNN-LSTM model takes the EEG signals and the spectrogram of the multiple
speakers as inputs and classifies the attention to one of the speakers. We
evaluated the reliability of our neural network using three different datasets
comprising of 61 subjects where, each subject undertook a dual-speaker
experiment. The three datasets analysed corresponded to speech stimuli
presented in three different languages namely German, Danish and Dutch. Using
the proposed joint CNN-LSTM model, we obtained a median decoding accuracy of
77.2% at a trial duration of three seconds. Furthermore, we evaluated the
amount of sparsity that our model can tolerate by means of magnitude pruning
and found that the model can tolerate up to 50% sparsity without substantial
loss of decoding accuracy. | [
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] |
Clinical trials are crucial for drug development but are time consuming,
expensive, and often burdensome on patients. More importantly, clinical trials
face uncertain outcomes due to issues with efficacy, safety, or problems with
patient recruitment. If we were better at predicting the results of clinical
trials, we could avoid having to run trials that will inevitably fail more
resources could be devoted to trials that are likely to succeed. In this paper,
we propose Hierarchical INteraction Network (HINT) for more general, clinical
trial outcome predictions for all diseases based on a comprehensive and diverse
set of web data including molecule information of the drugs, target disease
information, trial protocol and biomedical knowledge. HINT first encode these
multi-modal data into latent embeddings, where an imputation module is designed
to handle missing data. Next, these embeddings will be fed into the knowledge
embedding module to generate knowledge embeddings that are pretrained using
external knowledge on pharmaco-kinetic properties and trial risk from the web.
Then the interaction graph module will connect all the embedding via domain
knowledge to fully capture various trial components and their complex relations
as well as their influences on trial outcomes. Finally, HINT learns a dynamic
attentive graph neural network to predict trial outcome. Comprehensive
experimental results show that HINT achieves strong predictive performance,
obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and
indication outcome prediction, respectively. It also consistently outperforms
the best baseline method by up to 12.4\% on PR-AUC. | [
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] |
Synthesis from linear temporal logic (LTL) specifications provides assured
controllers for systems operating in stochastic and potentially adversarial
environments. Automatic synthesis tools, however, require a model of the
environment to construct controllers. In this work, we introduce a model-free
reinforcement learning (RL) approach to derive controllers from given LTL
specifications even when the environment is completely unknown. We model the
problem as a stochastic game (SG) between the controller and the adversarial
environment; we then learn optimal control strategies that maximize the
probability of satisfying the LTL specifications against the worst-case
environment behavior. We first construct a product game using the deterministic
parity automaton (DPA) translated from the given LTL specification. By deriving
distinct rewards and discount factors from the acceptance condition of the DPA,
we reduce the maximization of the worst-case probability of satisfying the LTL
specification into the maximization of a discounted reward objective in the
product game; this enables the use of model-free RL algorithms to learn an
optimal controller strategy. To deal with the common scalability problems when
the number of sets defining the acceptance condition of the DPA (usually
referred as colors), is large, we propose a lazy color generation method where
distinct rewards and discount factors are utilized only when needed, and an
approximate method where the controller eventually focuses on only one color.
In several case studies, we show that our approach is scalable to a wide range
of LTL formulas, significantly outperforming existing methods for learning
controllers from LTL specifications in SGs. | [
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] |
Round-the-clock monitoring of human behavior and emotions is required in many
healthcare applications which is very expensive but can be automated using
machine learning (ML) and sensor technologies. Unfortunately, the lack of
infrastructure for collection and sharing of such data is a bottleneck for ML
research applied to healthcare. Our goal is to circumvent this bottleneck by
simulating a human body in virtual environment. This will allow generation of
potentially infinite amounts of shareable data from an individual as a function
of his actions, interactions and emotions in a care facility or at home, with
no risk of confidentiality breach or privacy invasion. In this paper, we
develop for the first time a system consisting of computational models for
synchronously synthesizing skeletal motion, electrocardiogram, blood pressure,
respiration, and skin conductance signals as a function of an open-ended set of
actions and emotions. Our experimental evaluations, involving user studies,
benchmark datasets and comparison to findings in the literature, show that our
models can generate skeletal motion and physiological signals with high
fidelity. The proposed framework is modular and allows the flexibility to
experiment with different models. In addition to facilitating ML research for
round-the-clock monitoring at a reduced cost, the proposed framework will allow
reusability of code and data, and may be used as a training tool for ML
practitioners and healthcare professionals. | [
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] |
The cost involved in training deep neural networks (DNNs) on von-Neumann
architectures has motivated the development of novel solutions for efficient
DNN training accelerators. We propose a hybrid in-memory computing (HIC)
architecture for the training of DNNs on hardware accelerators that results in
memory-efficient inference and outperforms baseline software accuracy in
benchmark tasks. We introduce a weight representation technique that exploits
both binary and multi-level phase-change memory (PCM) devices, and this leads
to a memory-efficient inference accelerator. Unlike previous in-memory
computing-based implementations, we use a low precision weight update
accumulator that results in more memory savings. We trained the ResNet-32
network to classify CIFAR-10 images using HIC. For a comparable model size,
HIC-based training outperforms baseline network, trained in floating-point
32-bit (FP32) precision, by leveraging appropriate network width multiplier.
Furthermore, we observe that HIC-based training results in about 50% less
inference model size to achieve baseline comparable accuracy. We also show that
the temporal drift in PCM devices has a negligible effect on post-training
inference accuracy for extended periods (year). Finally, our simulations
indicate HIC-based training naturally ensures that the number of write-erase
cycles seen by the devices is a small fraction of the endurance limit of PCM,
demonstrating the feasibility of this architecture for achieving hardware
platforms that can learn in the field. | [
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] |
Threat intelligence on malware attacks and campaigns is increasingly being
shared with other security experts for a cost or for free. Other security
analysts use this intelligence to inform them of indicators of compromise,
attack techniques, and preventative actions. Security analysts prepare threat
analysis reports after investigating an attack, an emerging cyber threat, or a
recently discovered vulnerability. Collectively known as cyber threat
intelligence (CTI), the reports are typically in an unstructured format and,
therefore, challenging to integrate seamlessly into existing intrusion
detection systems. This paper proposes a framework that uses the aggregated CTI
for analysis and defense at scale. The information is extracted and stored in a
structured format using knowledge graphs such that the semantics of the threat
intelligence can be preserved and shared at scale with other security analysts.
Specifically, we propose the first semi-supervised open-source knowledge
graph-based framework, TINKER, to capture cyber threat information and its
context. Following TINKER, we generate a Cyberthreat Intelligence Knowledge
Graph (CTI-KG) and demonstrate the usage using different use cases. | [
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] |
The networked opinion diffusion in online social networks (OSN) is often
governed by the two genres of opinions - endogenous opinions that are driven by
the influence of social contacts among users, and exogenous opinions which are
formed by external effects like news, feeds etc. Accurate demarcation of
endogenous and exogenous messages offers an important cue to opinion modeling,
thereby enhancing its predictive performance. In this paper, we design a suite
of unsupervised classification methods based on experimental design approaches,
in which, we aim to select the subsets of events which minimize different
measures of mean estimation error. In more detail, we first show that these
subset selection tasks are NP-Hard. Then we show that the associated objective
functions are weakly submodular, which allows us to cast efficient
approximation algorithms with guarantees. Finally, we validate the efficacy of
our proposal on various real-world datasets crawled from Twitter as well as
diverse synthetic datasets. Our experiments range from validating prediction
performance on unsanitized and sanitized events to checking the effect of
selecting optimal subsets of various sizes. Through various experiments, we
have found that our method offers a significant improvement in accuracy in
terms of opinion forecasting, against several competitors. | [
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Cluster scheduler is crucial in high-performance computing (HPC). It
determines when and which user jobs should be allocated to available system
resources. Existing cluster scheduling heuristics are developed by human
experts based on their experience with specific HPC systems and workloads.
However, the increasing complexity of computing systems and the highly dynamic
nature of application workloads have placed tremendous burden on manually
designed and tuned scheduling heuristics. More aggressive optimization and
automation are needed for cluster scheduling in HPC. In this work, we present
an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for
Scheduling) by leveraging deep reinforcement learning. DRAS is built on a
novel, hierarchical neural network incorporating special HPC scheduling
features such as resource reservation and backfilling. A unique training
strategy is presented to enable DRAS to rapidly learn the target environment.
Once being provided a specific scheduling objective given by system manager,
DRAS automatically learns to improve its policy through interaction with the
scheduling environment and dynamically adjusts its policy as workload changes.
The experiments with different production workloads demonstrate that DRAS
outperforms the existing heuristic and optimization approaches by up to 45%. | [
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] |
The construction of approximate replication strategies for pricing and
hedging of derivative contracts in incomplete markets is a key problem of
financial engineering. Recently Reinforcement Learning algorithms for hedging
under realistic market conditions have attracted significant interest. While
research in the derivatives area mostly focused on variations of $Q$-learning,
in artificial intelligence Monte Carlo Tree Search is the recognized
state-of-the-art method for various planning problems, such as the games of
Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method
to solve the stochastic optimal control problem behind the pricing and hedging
tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree
search techniques. As a consequence Monte Carlo Tree Search has higher sample
efficiency, is less prone to over-fitting to specific market models and
generally learns stronger policies faster. In our experiments we find that
Monte Carlo Tree Search, being the world-champion in games like Chess and Go,
is easily capable of maximizing the utility of investor's terminal wealth
without setting up an auxiliary mathematical framework. | [
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] |
The incorporation of appropriate inductive bias plays a critical role in
learning dynamics from data. A growing body of work has been exploring ways to
enforce energy conservation in the learned dynamics by encoding Lagrangian or
Hamiltonian dynamics into the neural network architecture. These existing
approaches are based on differential equations, which do not allow
discontinuity in the states and thereby limit the class of systems one can
learn. However, in reality, most physical systems, such as legged robots and
robotic manipulators, involve contacts and collisions, which introduce
discontinuities in the states. In this paper, we introduce a differentiable
contact model, which can capture contact mechanics: frictionless/frictional, as
well as elastic/inelastic. This model can also accommodate inequality
constraints, such as limits on the joint angles. The proposed contact model
extends the scope of Lagrangian and Hamiltonian neural networks by allowing
simultaneous learning of contact and system properties. We demonstrate this
framework on a series of challenging 2D and 3D physical systems with different
coefficients of restitution and friction. The learned dynamics can be used as a
differentiable physics simulator for downstream gradient-based optimization
tasks, such as planning and control. | [
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A sequential recommender system aims to recommend attractive items to users
based on behaviour patterns. The predominant sequential recommendation models
are based on natural language processing models, such as the gated recurrent
unit, that embed items in some defined space and grasp the user's long-term and
short-term preferences based on the item embeddings. However, these approaches
lack fundamental insight into how such models are related to the user's
inherent decision-making process. To provide this insight, we propose a novel
recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC
divides the user's state into conscious and unconscious states, and the user's
decision process is modelled by Freud's two principles: the pleasure principle
and reality principle. To model the pleasure principle, i.e., free-floating
user's instinct, we place the user's unconscious state and item embeddings in
the same latent space and subject them to Newton's law of gravitation.
Moreover, to recommend items to users, we model the reality principle, i.e.,
balancing the conscious and unconscious states, via a gating function. Based on
extensive experiments on various benchmark datasets, this paper provides
insight into the characteristics of the proposed model. FaNC initiates a new
direction of sequential recommendations at the convergence of psychoanalysis
and recommender systems. | [
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] |
Motivated by modern applications, such as online advertisement and
recommender systems, we study the top-$k$ extreme contextual bandits problem,
where the total number of arms can be enormous, and the learner is allowed to
select $k$ arms and observe all or some of the rewards for the chosen arms. We
first propose an algorithm for the non-extreme realizable setting, utilizing
the Inverse Gap Weighting strategy for selecting multiple arms. We show that
our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log
(|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is
the class containing the regression function, while only requiring
$\tilde{O}(A)$ computation per time step. In the extreme setting, where the
total number of arms can be in the millions, we propose a practically-motivated
arm hierarchy model that induces a certain structure in mean rewards to ensure
statistical and computational efficiency. The hierarchical structure allows for
an exponential reduction in the number of relevant arms for each context, thus
resulting in a regret guarantee of $O(k\sqrt{(\log A-k+1)T \log
(|\mathcal{F}|T)})$. Finally, we implement our algorithm using a hierarchical
linear function class and show superior performance with respect to well-known
benchmarks on simulated bandit feedback experiments using extreme multi-label
classification datasets. On a dataset with three million arms, our reduction
scheme has an average inference time of only 7.9 milliseconds, which is a 100x
improvement. | [
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Static analysis tools are widely used for vulnerability detection as they
understand programs with complex behavior and millions of lines of code.
Despite their popularity, static analysis tools are known to generate an excess
of false positives. The recent ability of Machine Learning models to understand
programming languages opens new possibilities when applied to static analysis.
However, existing datasets to train models for vulnerability identification
suffer from multiple limitations such as limited bug context, limited size, and
synthetic and unrealistic source code. We propose D2A, a differential analysis
based approach to label issues reported by static analysis tools. The D2A
dataset is built by analyzing version pairs from multiple open source projects.
From each project, we select bug fixing commits and we run static analysis on
the versions before and after such commits. If some issues detected in a
before-commit version disappear in the corresponding after-commit version, they
are very likely to be real bugs that got fixed by the commit. We use D2A to
generate a large labeled dataset to train models for vulnerability
identification. We show that the dataset can be used to build a classifier to
identify possible false alarms among the issues reported by static analysis,
hence helping developers prioritize and investigate potential true positives
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] |
Flu circulates all over the world. The worldwide infection places a
substantial burden on people's health every year. Regardless of the
characteristic of the worldwide circulation of flu, most previous studies
focused on regional prediction of flu outbreaks. The methodology of considering
the spatio-temporal correlation could help forecast flu outbreaks more
precisely. Furthermore, forecasting a long-term flu outbreak, and understanding
flu infection trends more accurately could help hospitals, clinics, and
pharmaceutical companies to better prepare for annual flu outbreaks. Predicting
a sequence of values in the future, namely, the multi-step prediction of flu
outbreaks should cause concern. Therefore, we highlight the importance of
developing spatio-temporal methodologies to perform multi-step prediction of
worldwide flu outbreaks. We compared the MAPEs of SVM, RF, LSTM models of
predicting flu data of the 1-4 weeks ahead with and without other countries'
flu data. We found the LSTM models achieved the lowest MAPEs in most cases. As
for countries in the Southern hemisphere, the MAPEs of predicting flu data with
other countries are higher than those of predicting without other countries.
For countries in the Northern hemisphere, the MAPEs of predicting flu data of
the 2-4 weeks ahead with other countries are lower than those of predicting
without other countries; and the MAPEs of predicting flu data of the 1-weeks
ahead with other countries are higher than those of predicting without other
countries, except for the UK. In this study, we performed the spatio-temporal
multi-step prediction of influenza outbreaks. The methodology considering the
spatio-temporal features improves the multi-step prediction of flu outbreaks. | [
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] |
Mobile robotics is a research area that has witnessed incredible advances for
the last decades. Robot navigation is an essential task for mobile robots. Many
methods are proposed for allowing robots to navigate within different
environments. This thesis studies different deep learning-based approaches,
highlighting the advantages and disadvantages of each scheme. In fact, these
approaches are promising that some of them can navigate the robot in unknown
and dynamic environments. In this thesis, one of the deep learning methods
based on convolutional neural network (CNN) is realized by software
implementations. There are different preparation studies to complete this
thesis such as introduction to Linux, robot operating system (ROS), C++,
python, and GAZEBO simulator. Within this work, we modified the drone network
(namely, DroNet) approach to be used in an indoor environment by using a ground
robot in different cases. Indeed, the DroNet approach suffers from the absence
of goal-oriented motion. Therefore, this thesis mainly focuses on tackling this
problem via mapping using simultaneous localization and mapping (SLAM) and path
planning techniques using Dijkstra. Afterward, the combination between the
DroNet ground robot-based, mapping, and path planning leads to a goal-oriented
motion, following the shortest path while avoiding the dynamic obstacle.
Finally, we propose a low-cost approach, for indoor applications such as
restaurants, museums, etc, on the base of using a monocular camera instead of a
laser scanner. | [
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] |
Generalization is a central problem in Machine Learning. Indeed most
prediction methods require careful calibration of hyperparameters usually
carried out on a hold-out \textit{validation} dataset to achieve
generalization. The main goal of this paper is to introduce a novel approach to
achieve generalization without any data splitting, which is based on a new risk
measure which directly quantifies a model's tendency to overfit. To fully
understand the intuition and advantages of this new approach, we illustrate it
in the simple linear regression model ($Y=X\beta+\xi$) where we develop a new
criterion. We highlight how this criterion is a good proxy for the true
generalization risk. Next, we derive different procedures which tackle several
structures simultaneously (correlation, sparsity,...). Noticeably, these
procedures \textbf{concomitantly} train the model and calibrate the
hyperparameters. In addition, these procedures can be implemented via classical
gradient descent methods when the criterion is differentiable w.r.t. the
hyperparameters. Our numerical experiments reveal that our procedures are
computationally feasible and compare favorably to the popular approach (Ridge,
LASSO and Elastic-Net combined with grid-search cross-validation) in term of
generalization. They also outperform the baseline on two additional tasks:
estimation and support recovery of $\beta$. Moreover, our procedures do not
require any expertise for the calibration of the initial parameters which
remain the same for all the datasets we experimented on. | [
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] |
Most existing personalization systems promote items that match a user's
previous choices or those that are popular among similar users. This results in
recommendations that are highly similar to the ones users are already exposed
to, resulting in their isolation inside familiar but insulated information
silos. In this context, we develop a novel recommendation framework with a goal
of improving information diversity using a modified random walk exploration of
the user-item graph. We focus on the problem of political content
recommendation, while addressing a general problem applicable to
personalization tasks in other social and information networks.
For recommending political content on social networks, we first propose a new
model to estimate the ideological positions for both users and the content they
share, which is able to recover ideological positions with high accuracy. Based
on these estimated positions, we generate diversified personalized
recommendations using our new random-walk based recommendation algorithm. With
experimental evaluations on large datasets of Twitter discussions, we show that
our method based on \emph{random walks with erasure} is able to generate more
ideologically diverse recommendations. Our approach does not depend on the
availability of labels regarding the bias of users or content producers. With
experiments on open benchmark datasets from other social and information
networks, we also demonstrate the effectiveness of our method in recommending
diverse long-tail items. | [
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