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Recommendation systems are unique as they show a conflation of compute and
memory intensity due to their deep learning and massive embedding tables.
Training these models typically involve a hybrid CPU-GPU mode, where GPUs
accelerate the deep learning portion and the CPUs store and process the
memory-intensive embedding tables. The hybrid mode incurs a substantial
CPU-to-GPU transfer time and relies on main memory bandwidth to feed embeddings
to GPU for deep learning acceleration. Alternatively, we can store the entire
embeddings across GPUs to avoid the transfer time and utilize the GPU's High
Bandwidth Memory (HBM). This approach requires GPU-to-GPU backend communication
and scales the number of GPUs with the size of the embedding tables. To
overcome these concerns, this paper offers a heterogeneous acceleration
pipeline, called Hotline.
Hotline leverages the insight that only a small number of embedding entries
are accessed frequently, and can easily fit in a single GPU's HBM. Hotline
implements a data-aware and model-aware scheduling pipeline that utilizes the
(1) CPU main memory for not-frequently-accessed embeddings and (2) GPUs' local
memory for frequently-accessed embeddings. Hotline improves the training
throughput by dynamically stitching the execution of popular and not-popular
inputs through a novel hardware accelerator and feeding to the GPUs. Results on
real-world datasets and recommender models show that Hotline reduces the
average training time by 3x and 1.8x in comparison to Intel-optimized CPU-GPU
DLRM and HugeCTR-optimized GPU-only baseline, respectively. Hotline increases
the overall training throughput to 35.7 epochs/hour in comparison to 5.3
epochs/hour for the Intel-optimized DLRM baseline | [
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] |
Satellite-derived measurements are negatively impacted by cloud cover and
surface reflectivity. These biases must be discarded and significantly increase
the amount of missing data within remote sensing images. This paper expands the
application of a partial convolutional neural network (PCNN) to incorporate
depthwise convolution layers, conferring temporal dimensionality to the
imputation process. The addition of a temporal dimension to the imputation
process adds a state of successive existence within the dataset which spatial
imputation cannot capture. The depthwise convolution process enables the PCNN
to independently convolve the data for each channel. The deep learning system
is trained with the Community Multiscale Air Quality model-simulated
tropospheric column density of Nitrogen Dioxide (TCDNO2) to impute TROPOspheric
Monitoring Instrument TCDNO2. The depthwise PCNN model achieves an index of
agreement of 0.82 and outperforms the default PCNN models, with and without
temporal dimensionality of data, and conventional data imputation methods such
as inverse distance weighting by 3-11% and 8-15% in the index of agreement and
correlation, respectively. The model demonstrates more consistency in the
reconstruction of TROPOspheric Monitoring Instrument tropospheric column
density of NO2 images. The model has also demonstrated the accurate imputation
of remote sensing images with over 95% of the data missing. PCNN enables the
accurate imputation of remote sensing data with large regions of missing data
and will benefit future researchers conducting data assimilation for numerical
models, emission studies, and human health impact analyses from air pollution. | [
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] |
For many years now, understanding the brain mechanism has been a great
research subject in many different fields. Brain signal processing and
especially electroencephalogram (EEG) has recently known a growing interest
both in academia and industry. One of the main examples is the increasing
number of Brain-Computer Interfaces (BCI) aiming to link brains and computers.
In this paper, we present a novel framework allowing us to retrieve the
attention state, i.e degree of attention given to a specific task, from EEG
signals. While previous methods often consider the spatial relationship in EEG
through electrodes and process them in recurrent or convolutional based
architecture, we propose here to also exploit the spatial and temporal
information with a transformer-based network that has already shown its
supremacy in many machine-learning (ML) related studies, e.g. machine
translation. In addition to this novel architecture, an extensive study on the
feature extraction methods, frequential bands and temporal windows length has
also been carried out. The proposed network has been trained and validated on
two public datasets and achieves higher results compared to state-of-the-art
models. As well as proposing better results, the framework could be used in
real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD)
symptoms or vigilance during a driving assessment. | [
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] |
Likelihood-based, or explicit, deep generative models use neural networks to
construct flexible high-dimensional densities. This formulation directly
contradicts the manifold hypothesis, which states that observed data lies on a
low-dimensional manifold embedded in high-dimensional ambient space. In this
paper we investigate the pathologies of maximum-likelihood training in the
presence of this dimensionality mismatch. We formally prove that degenerate
optima are achieved wherein the manifold itself is learned but not the
distribution on it, a phenomenon we call manifold overfitting. We propose a
class of two-step procedures consisting of a dimensionality reduction step
followed by maximum-likelihood density estimation, and prove that they recover
the data-generating distribution in the nonparametric regime, thus avoiding
manifold overfitting. We also show that these procedures enable density
estimation on the manifolds learned by implicit models, such as generative
adversarial networks, hence addressing a major shortcoming of these models.
Several recently proposed methods are instances of our two-step procedures; we
thus unify, extend, and theoretically justify a large class of models. | [
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] |
Imitation learning is a promising approach to help robots acquire dexterous
manipulation capabilities without the need for a carefully-designed reward or a
significant computational effort. However, existing imitation learning
approaches require sophisticated data collection infrastructure and struggle to
generalize beyond the training distribution. One way to address this limitation
is to gather additional data that better represents the full operating
conditions. In this work, we investigate characteristics of such additional
demonstrations and their impact on performance. Specifically, we study the
effects of corrective and randomly-sampled additional demonstrations on
learning a policy that guides a five-fingered robot hand through a
pick-and-place task. Our results suggest that corrective demonstrations
considerably outperform randomly-sampled demonstrations, when the proportion of
additional demonstrations sampled from the full task distribution is larger
than the number of original demonstrations sampled from a restrictive training
distribution. Conversely, when the number of original demonstrations are higher
than that of additional demonstrations, we find no significant differences
between corrective and randomly-sampled additional demonstrations. These
results provide insights into the inherent trade-off between the effort
required to collect corrective demonstrations and their relative benefits over
randomly-sampled demonstrations. Additionally, we show that inexpensive
vision-based sensors, such as LeapMotion, can be used to dramatically reduce
the cost of providing demonstrations for dexterous manipulation tasks. Our code
is available at
https://github.com/GT-STAR-Lab/corrective-demos-dexterous-manipulation. | [
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] |
An electrocardiogram (ECG) monitors the electrical activity generated by the
heart and is used to detect fatal cardiovascular diseases (CVDs).
Conventionally, to capture the precise electrical activity, clinical experts
use multiple-lead ECGs (typically 12 leads). But in recent times, large-size
deep learning models have been used to detect these diseases. However, such
models require heavy compute resources like huge memory and long inference
time. To alleviate these shortcomings, we propose a low-parameter model, named
Low Resource Heart-Network (LRH-Net), which uses fewer leads to detect ECG
anomalies in a resource-constrained environment. A multi-level knowledge
distillation process is used on top of that to get better generalization
performance on our proposed model. The multi-level knowledge distillation
process distills the knowledge to LRH-Net trained on a reduced number of leads
from higher parameter (teacher) models trained on multiple leads to reduce the
performance gap. The proposed model is evaluated on the PhysioNet-2020
challenge dataset with constrained input. The parameters of the LRH-Net are
106x less than our teacher model for detecting CVDs. The performance of the
LRH-Net was scaled up to 3.2% and the inference time scaled down by 75%
compared to the teacher model. In contrast to the compute- and
parameter-intensive deep learning techniques, the proposed methodology uses a
subset of ECG leads using the low resource LRH-Net, making it eminently
suitable for deployment on edge devices. | [
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] |
Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods. | [
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] |
Model-based reinforcement learning (RL) algorithms designed for handling
complex visual observations typically learn some sort of latent state
representation, either explicitly or implicitly. Standard methods of this sort
do not distinguish between functionally relevant aspects of the state and
irrelevant distractors, instead aiming to represent all available information
equally. We propose a modified objective for model-based RL that, in
combination with mutual information maximization, allows us to learn
representations and dynamics for visual model-based RL without reconstruction
in a way that explicitly prioritizes functionally relevant factors. The key
principle behind our design is to integrate a term inspired by variational
empowerment into a state-space model based on mutual information. This term
prioritizes information that is correlated with action, thus ensuring that
functionally relevant factors are captured first. Furthermore, the same
empowerment term also promotes faster exploration during the RL process,
especially for sparse-reward tasks where the reward signal is insufficient to
drive exploration in the early stages of learning. We evaluate the approach on
a suite of vision-based robot control tasks with natural video backgrounds, and
show that the proposed prioritized information objective outperforms
state-of-the-art model based RL approaches with higher sample efficiency and
episodic returns. https://sites.google.com/view/information-empowerment | [
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] |
Integration of multi-omics data provides opportunities for revealing
biological mechanisms related to certain phenotypes. We propose a novel method
of multi-omics integration called supervised deep generalized canonical
correlation analysis (SDGCCA) for modeling correlation structures between
nonlinear multi-omics manifolds, aiming for improving classification of
phenotypes and revealing biomarkers related to phenotypes. SDGCCA addresses the
limitations of other canonical correlation analysis (CCA)-based models (e.g.,
deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data
correlations and discriminating phenotype groups. Although there are a few
methods for nonlinear CCA projections for discriminant purposes of phenotypes,
they only consider two views. On the other hand, SDGCCA is the nonlinear
multiview CCA projection method for discrimination. When we applied SDGCCA to
prediction of patients of Alzheimer's disease (AD) and discrimination of early-
and late-stage cancers, it outperformed other CCA-based methods and other
supervised methods. In addition, we demonstrate that SDGCCA can be used for
feature selection to identify important multi-omics biomarkers. In the
application on AD data, SDGCCA identified clusters of genes in multi-omics
data, which are well known to be associated with AD. | [
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] |
Neural networks have had discernible achievements in a wide range of
applications. The wide-spread adoption also raises the concern of their
dependability and reliability. Similar to traditional decision-making programs,
neural networks can have defects that need to be repaired. The defects may
cause unsafe behaviors, raise security concerns or unjust societal impacts. In
this work, we address the problem of repairing a neural network for desirable
properties such as fairness and the absence of backdoor. The goal is to
construct a neural network that satisfies the property by (minimally) adjusting
the given neural network's parameters (i.e., weights). Specifically, we propose
CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural
network repair technique that 1) performs causality-based fault localization to
identify the `guilty' neurons and 2) optimizes the parameters of the identified
neurons to reduce the misbehavior. We have empirically evaluated CARE on
various tasks such as backdoor removal, neural network repair for fairness and
safety properties. Our experiment results show that CARE is able to repair all
neural networks efficiently and effectively. For fairness repair tasks, CARE
successfully improves fairness by $61.91\%$ on average. For backdoor removal
tasks, CARE reduces the attack success rate from over $98\%$ to less than
$1\%$. For safety property repair tasks, CARE reduces the property violation
rate to less than $1\%$. Results also show that thanks to the causality-based
fault localization, CARE's repair focuses on the misbehavior and preserves the
accuracy of the neural networks. | [
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] |
A fundamental problem in drug discovery is to design molecules that bind to
specific proteins. To tackle this problem using machine learning methods, here
we propose a novel and effective framework, known as GraphBP, to generate 3D
molecules that bind to given proteins by placing atoms of specific types and
locations to the given binding site one by one. In particular, at each step, we
first employ a 3D graph neural network to obtain geometry-aware and chemically
informative representations from the intermediate contextual information. Such
context includes the given binding site and atoms placed in the previous steps.
Second, to preserve the desirable equivariance property, we select a local
reference atom according to the designed auxiliary classifiers and then
construct a local spherical coordinate system. Finally, to place a new atom, we
generate its atom type and relative location w.r.t. the constructed local
coordinate system via a flow model. We also consider generating the variables
of interest sequentially to capture the underlying dependencies among them.
Experiments demonstrate that our GraphBP is effective to generate 3D molecules
with binding ability to target protein binding sites. Our implementation is
available at https://github.com/divelab/GraphBP. | [
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] |
Audio-based classification techniques on body sounds have long been studied
to aid in the diagnosis of respiratory diseases. While most research is
centered on the use of cough as the main biomarker, other body sounds also have
the potential to detect respiratory diseases. Recent studies on COVID-19 have
shown that breath and speech sounds, in addition to cough, correlate with the
disease. Our study proposes Fused Audio Instance and Representation (FAIR) as a
method for respiratory disease detection. FAIR relies on constructing a joint
feature vector from various body sounds represented in waveform and spectrogram
form. We conducted experiments on the use case of COVID-19 detection by
combining waveform and spectrogram representation of body sounds. Our findings
show that the use of self-attention to combine extracted features from cough,
breath, and speech sounds leads to the best performance with an Area Under the
Receiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of
0.8057, and a specificity of 0.7958. Compared to models trained solely on
spectrograms or waveforms, the use of both representations results in an
improved AUC score, demonstrating that combining spectrogram and waveform
representation helps to enrich the extracted features and outperforms the
models that use only one representation. | [
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] |
Full-precision deep learning models are typically too large or costly to
deploy on edge devices. To accommodate to the limited hardware resources,
models are adapted to the edge using various edge-adaptation techniques, such
as quantization and pruning. While such techniques may have a negligible impact
on top-line accuracy, the adapted models exhibit subtle differences in output
compared to the original model from which they are derived. In this paper, we
introduce a new evasive attack, DIVA, that exploits these differences in edge
adaptation, by adding adversarial noise to input data that maximizes the output
difference between the original and adapted model. Such an attack is
particularly dangerous, because the malicious input will trick the adapted
model running on the edge, but will be virtually undetectable by the original
model, which typically serves as the authoritative model version, used for
validation, debugging and retraining. We compare DIVA to a state-of-the-art
attack, PGD, and show that DIVA is only 1.7-3.6% worse on attacking the adapted
model but 1.9-4.2 times more likely not to be detected by the the original
model under a whitebox and semi-blackbox setting, compared to PGD. | [
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] |
Risk assessment is a substantial problem for financial institutions that has
been extensively studied both for its methodological richness and its various
practical applications. With the expansion of inclusive finance, recent
attentions are paid to micro and small-sized enterprises (MSEs). Compared with
large companies, MSEs present a higher exposure rate to default owing to their
insecure financial stability. Conventional efforts learn classifiers from
historical data with elaborate feature engineering. However, the main obstacle
for MSEs involves severe deficiency in credit-related information, which may
degrade the performance of prediction. Besides, financial activities have
diverse explicit and implicit relations, which have not been fully exploited
for risk judgement in commercial banks. In particular, the observations on real
data show that various relationships between company users have additional
power in financial risk analysis. In this paper, we consider a graph of banking
data, and propose a novel HIDAM model for the purpose. Specifically, we attempt
to incorporate heterogeneous information network with rich attributes on
multi-typed nodes and links for modeling the scenario of business banking
service. To enhance feature representation of MSEs, we extract interactive
information through meta-paths and fully exploit path information. Furthermore,
we devise a hierarchical attention mechanism respectively to learn the
importance of contents inside each meta-path and the importance of different
metapahs. Experimental results verify that HIDAM outperforms state-of-the-art
competitors on real-world banking data. | [
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] |
We describe a general approach for maximum a posteriori (MAP) inference in a
class of discrete-continuous factor graphs commonly encountered in robotics
applications. While there are openly available tools providing flexible and
easy-to-use interfaces for specifying and solving inference problems formulated
in terms of either discrete or continuous graphical models, at present, no
similarly general tools exist enabling the same functionality for hybrid
discrete-continuous problems. We aim to address this problem. In particular, we
provide a library, DC-SAM, extending existing tools for inference problems
defined in terms of factor graphs to the setting of discrete-continuous models.
A key contribution of our work is a novel solver for efficiently recovering
approximate solutions to discrete-continuous inference problems. The key
insight to our approach is that while joint inference over continuous and
discrete state spaces is often hard, many commonly encountered
discrete-continuous problems can naturally be split into a "discrete part" and
a "continuous part" that can individually be solved easily. Leveraging this
structure, we optimize discrete and continuous variables in an alternating
fashion. In consequence, our proposed work enables straightforward
representation of and approximate inference in discrete-continuous graphical
models. We also provide a method to approximate the uncertainty in estimates of
both discrete and continuous variables. We demonstrate the versatility of our
approach through its application to distinct robot perception applications,
including robust pose graph optimization, and object-based mapping and
localization. | [
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] |
The Reinforcement Learning (RL) paradigm has been an essential tool for
automating robotic tasks. Despite the advances in RL, it is still not widely
adopted in the industry due to the need for an expensive large amount of robot
interaction with its environment. Curriculum Learning (CL) has been proposed to
expedite learning. However, most research works have been only evaluated in
simulated environments, from video games to robotic toy tasks. This paper
presents a study for accelerating robot learning of contact-rich manipulation
tasks based on Curriculum Learning combined with Domain Randomization (DR). We
tackle complex industrial assembly tasks with position-controlled robots, such
as insertion tasks. We compare different curricula designs and sampling
approaches for DR. Based on this study, we propose a method that significantly
outperforms previous work, which uses DR only (No CL is used), with less than a
fifth of the training time (samples). Results also show that even when training
only in simulation with toy tasks, our method can learn policies that can be
transferred to the real-world robot. The learned policies achieved success
rates of up to 86\% on real-world complex industrial insertion tasks (with
tolerances of $\pm 0.01~mm$) not seen during the training. | [
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] |
To act safely and ethically in the real world, agents must be able to reason
about harm and avoid harmful actions. However, to date there is no statistical
method for measuring harm and factoring it into algorithmic decisions. In this
paper we propose the first formal definition of harm and benefit using causal
models. We show that any factual definition of harm must violate basic
intuitions in certain scenarios, and show that standard machine learning
algorithms that cannot perform counterfactual reasoning are guaranteed to
pursue harmful policies following distributional shifts. We use our definition
of harm to devise a framework for harm-averse decision making using
counterfactual objective functions. We demonstrate this framework on the
problem of identifying optimal drug doses using a dose-response model learned
from randomized control trial data. We find that the standard method of
selecting doses using treatment effects results in unnecessarily harmful doses,
while our counterfactual approach allows us to identify doses that are
significantly less harmful without sacrificing efficacy. | [
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In recommendation systems, the choice of loss function is critical since a
good loss may significantly improve the model performance. However, manually
designing a good loss is a big challenge due to the complexity of the problem.
A large fraction of previous work focuses on handcrafted loss functions, which
needs significant expertise and human effort. In this paper, inspired by the
recent development of automated machine learning, we propose an automatic loss
function generation framework, AutoLossGen, which is able to generate loss
functions directly constructed from basic mathematical operators without prior
knowledge on loss structure. More specifically, we develop a controller model
driven by reinforcement learning to generate loss functions, and develop
iterative and alternating optimization schedule to update the parameters of
both the controller model and the recommender model. One challenge for
automatic loss generation in recommender systems is the extreme sparsity of
recommendation datasets, which leads to the sparse reward problem for loss
generation and search. To solve the problem, we further develop a reward
filtering mechanism for efficient and effective loss generation. Experimental
results show that our framework manages to create tailored loss functions for
different recommendation models and datasets, and the generated loss gives
better recommendation performance than commonly used baseline losses. Besides,
most of the generated losses are transferable, i.e., the loss generated based
on one model and dataset also works well for another model or dataset. Source
code of the work is available at https://github.com/rutgerswiselab/AutoLossGen. | [
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] |
Many real-world multi-objective optimisation problems rely on computationally
expensive function evaluations. Multi-objective Bayesian optimisation (BO) can
be used to alleviate the computation time to find an approximated set of Pareto
optimal solutions. In many real-world problems, a decision-maker has some
preferences on the objective functions. One approach to incorporate the
preferences in multi-objective BO is to use a scalarising function and build a
single surrogate model (mono-surrogate approach) on it. This approach has two
major limitations. Firstly, the fitness landscape of the scalarising function
and the objective functions may not be similar. Secondly, the approach assumes
that the scalarising function distribution is Gaussian, and thus a closed-form
expression of an acquisition function e.g., expected improvement can be used.
We overcome these limitations by building independent surrogate models
(multi-surrogate approach) on each objective function and show that the
distribution of the scalarising function is not Gaussian. We approximate the
distribution using Generalised value distribution. We present an a-priori
multi-surrogate approach to incorporate the desirable objective function values
(or reference point) as the preferences of a decision-maker in multi-objective
BO. The results and comparison with the existing mono-surrogate approach on
benchmark and real-world optimisation problems show the potential of the
proposed approach. | [
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] |
High dimensional parameter space optimization is crucial in many
applications. The parameters affecting this performance can be both numerical
and categorical in their type. The existing techniques of black-box
optimization and visual analytics are good in dealing with numerical parameters
but analyzing categorical variables in context of the numerical variables are
not well studied. Hence, we propose a novel approach, to create an auto-tuning
framework for storage systems optimization combining both direct optimization
techniques and visual analytics research. While the optimization algorithm will
be the core of the system, visual analytics will provide a guideline with the
help of an external agent (expert) to provide crucial hints to narrow down the
large search space for the optimization engine. As part of the initial step
towards creating an auto-tuning engine for storage systems optimization, we
created an Interactive Configuration Explorer \textit{ICE}, which directly
addresses the need of analysts to learn how the dependent numerical variable is
affected by the parameter settings given multiple optimization objectives. No
information is lost as ICE shows the complete distribution and statistics of
the dependent variable in context with each categorical variable. Analysts can
interactively filter the variables to optimize for certain goals such as
achieving a system with maximum performance, low variance, etc. Our system was
developed in tight collaboration with a group of systems performance
researchers and its final effectiveness was evaluated with expert interviews, a
comparative user study, and two case studies. We also discuss our research plan
for creating an efficient auto-tuning framework combining black-box
optimization and visual analytics for storage systems performance optimization. | [
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Student assessment is one of the most fundamental tasks in the field of AI
Education (AIEd). One of the most common approach to student assessment is
Knowledge Tracing (KT), which evaluates a student's knowledge state by
predicting whether the student will answer a given question correctly or not.
However, in the context of multiple choice (polytomous) questions, conventional
KT approaches are limited in that they only consider the binary (dichotomous)
correctness label (i.e., correct or incorrect), and disregard the specific
option chosen by the student. Meanwhile, Option Tracing (OT) attempts to model
a student by predicting which option they will choose for a given question, but
overlooks the correctness information. In this paper, we propose
Dichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learning
framework that combines KT and OT for more precise student assessment. In
particular, we show that the KT objective acts as a regularization term for OT
in the DP-MTL framework, and propose an appropriate architecture for applying
our method on top of existing deep learning-based KT models. We experimentally
confirm that DP-MTL significantly improves both KT and OT performances, and
also benefits downstream tasks such as Score Prediction (SP). | [
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] |
Topology impacts important network performance metrics, including link
utilization, throughput and latency, and is of central importance to network
operators. However, due to the combinatorial nature of network topology, it is
extremely difficult to obtain an optimal solution, especially since topology
planning in networks also often comes with management-specific constraints. As
a result, local optimization with hand-tuned heuristic methods from human
experts are often adopted in practice. Yet, heuristic methods cannot cover the
global topology design space while taking into account constraints, and cannot
guarantee to find good solutions.
In this paper, we propose a novel deep reinforcement learning (DRL)
algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS), for network
topology optimization. A2C-GS consists of three novel components, including a
verifier to validate the correctness of a generated network topology, a graph
neural network (GNN) to efficiently approximate topology rating, and a DRL
actor layer to conduct a topology search. A2C-GS can efficiently search over
large topology space and output topology with satisfying performance. We
conduct a case study based on a real network scenario, and our experimental
results demonstrate the superior performance of A2C-GS in terms of both
efficiency and performance. | [
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] |
Automated decision systems (ADS) are increasingly used for consequential
decision-making. These systems often rely on sophisticated yet opaque machine
learning models, which do not allow for understanding how a given decision was
arrived at. This is not only problematic from a legal perspective, but
non-transparent systems are also prone to yield unfair outcomes because their
sanity is challenging to assess and calibrate in the first place -- which is
particularly worrisome for human decision-subjects. Based on this observation
and building upon existing work, I aim to make the following three main
contributions through my doctoral thesis: (a) understand how (potential)
decision-subjects perceive algorithmic decisions (with varying degrees of
transparency of the underlying ADS), as compared to similar decisions made by
humans; (b) evaluate different tools for transparent decision-making with
respect to their effectiveness in enabling people to appropriately assess the
quality and fairness of ADS; and (c) develop human-understandable technical
artifacts for fair automated decision-making. Over the course of the first half
of my PhD program, I have already addressed substantial pieces of (a) and (c),
whereas (b) will be the major focus of the second half. | [
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Prior research on exposure fairness in the context of recommender systems has
focused mostly on disparities in the exposure of individual or groups of items
to individual users of the system. The problem of how individual or groups of
items may be systemically under or over exposed to groups of users, or even all
users, has received relatively less attention. However, such systemic
disparities in information exposure can result in observable social harms, such
as withholding economic opportunities from historically marginalized groups
(allocative harm) or amplifying gendered and racialized stereotypes
(representational harm). Previously, Diaz et al. developed the expected
exposure metric -- that incorporates existing user browsing models that have
previously been developed for information retrieval -- to study fairness of
content exposure to individual users. We extend their proposed framework to
formalize a family of exposure fairness metrics that model the problem jointly
from the perspective of both the consumers and producers. Specifically, we
consider group attributes for both types of stakeholders to identify and
mitigate fairness concerns that go beyond individual users and items towards
more systemic biases in recommendation. Furthermore, we study and discuss the
relationships between the different exposure fairness dimensions proposed in
this paper, as well as demonstrate how stochastic ranking policies can be
optimized towards said fairness goals. | [
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] |
Identification of unknown physical processes and parameters of groundwater
contaminant sources is a challenging task due to their ill-posed and non-unique
nature. Numerous works have focused on determining nonlinear physical processes
through model selection methods. However, identifying corresponding nonlinear
systems for different physical phenomena using numerical methods can be
computationally prohibitive. With the advent of machine learning (ML)
algorithms, more efficient surrogate models based on neural networks (NNs) have
been developed in various disciplines. In this work, a theory-guided U-net
(TgU-net) framework is proposed for surrogate modeling of three-dimensional
(3D) groundwater contaminant problems in order to efficiently elucidate their
involved processes and unknown parameters. In TgU-net, the underlying governing
equations are embedded into the loss function of U-net as soft constraints. For
the considered groundwater contaminant problem, sorption is considered to be a
potential process of an uncertain type, and three equilibrium sorption isotherm
types (i.e., linear, Freundlich, and Langmuir) are considered. Different from
traditional approaches in which one model corresponds to one equation, these
three sorption types are modeled through only one TgU-net surrogate. The three
mentioned sorption terms are integrated into one equation by assigning
indicators. Accurate predictions illustrate the satisfactory generalizability
and extrapolability of the constructed TgU-net. Furthermore, based on the
constructed TgU-net surrogate, a data assimilation method is employed to
identify the physical process and parameters simultaneously. This work shows
the possibility of governing equation discovery of physical problems that
contain multiple and even uncertain processes by using deep learning and data
assimilation methods. | [
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] |
One of the most promising applications of quantum computing is the processing
of graphical data like images. Here, we investigate the possibility of
realizing a quantum pattern recognition protocol based on swap test, and use
the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We
find that with a two-qubit protocol, swap test can efficiently detect the
similarity between two patterns with good fidelity, though for three or more
qubits the noise in the real devices becomes detrimental. To mitigate this
noise effect, we resort to destructive swap test, which shows an improved
performance for three-qubit states. Due to limited cloud access to larger IBMQ
processors, we take a segment-wise approach to apply the destructive swap test
on higher dimensional images. In this case, we define an average overlap
measure which shows faithfulness to distinguish between two very different or
very similar patterns when simulated on real IBMQ processors. As test images,
we use binary images with simple patterns, greyscale MNIST numbers and MNIST
fashion images, as well as binary images of human blood vessel obtained from
magnetic resonance imaging (MRI). We also present an experimental set up for
applying destructive swap test using the nitrogen vacancy centre (NVs) in
diamond. Our experimental data show high fidelity for single qubit states.
Lastly, we propose a protocol inspired from quantum associative memory, which
works in an analogous way to supervised learning for performing quantum pattern
recognition using destructive swap test. | [
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In social robotics, endowing humanoid robots with the ability to generate
bodily expressions of affect can improve human-robot interaction and
collaboration, since humans attribute, and perhaps subconsciously anticipate,
such traces to perceive an agent as engaging, trustworthy, and socially
present. Robotic emotional body language needs to be believable, nuanced and
relevant to the context. We implemented a deep learning data-driven framework
that learns from a few hand-designed robotic bodily expressions and can
generate numerous new ones of similar believability and lifelikeness. The
framework uses the Conditional Variational Autoencoder model and a sampling
approach based on the geometric properties of the model's latent space to
condition the generative process on targeted levels of valence and arousal. The
evaluation study found that the anthropomorphism and animacy of the generated
expressions are not perceived differently from the hand-designed ones, and the
emotional conditioning was adequately differentiable between most levels except
the pairs of neutral-positive valence and low-medium arousal. Furthermore, an
exploratory analysis of the results reveals a possible impact of the
conditioning on the perceived dominance of the robot, as well as on the
participants' attention. | [
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] |
Previous electroencephalogram (EEG) emotion recognition relies on single-task
learning, which may lead to overfitting and learned emotion features lacking
generalization. In this paper, a graph-based multi-task self-supervised
learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the
ability to learn more general representations by integrating multiple
self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and
contrastive learning tasks. By learning from multiple tasks simultaneously,
GMSS can find a representation that captures all of the tasks thereby
decreasing the chance of overfitting on the original task, i.e., emotion
recognition task. In particular, the spatial jigsaw puzzle task aims to capture
the intrinsic spatial relationships of different brain regions. Considering the
importance of frequency information in EEG emotional signals, the goal of the
frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG
emotion recognition. To further regularize the learned features and encourage
the network to learn inherent representations, contrastive learning task is
adopted in this work by mapping the transformed data into a common feature
space. The performance of the proposed GMSS is compared with several popular
unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED
datasets show that the proposed model has remarkable advantages in learning
more discriminative and general features for EEG emotional signals. | [
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] |
Machine Learning (ML) decision-making algorithms are now widely used in
predictive decision-making, for example, to determine who to admit and give a
loan. Their wide usage and consequential effects on individuals led the ML
community to question and raise concerns on how the algorithms differently
affect different people and communities. In this paper, we study fairness
issues that arise when decision-makers use models (proxy models) that deviate
from the models that depict the physical and social environment in which the
decisions are situated (intended models). We also highlight the effect of
obstacles on individual access and utilization of the models. To this end, we
formulate an Equity Framework that considers equal access to the model, equal
outcomes from the model, and equal utilization of the model, and
consequentially achieves equity and higher social welfare than current fairness
notions that aim for equality. We show how the three main aspects of the
framework are connected and provide an equity scoring algorithm and questions
to guide decision-makers towards equitable decision-making. We show how failure
to consider access, outcome, and utilization would exacerbate proxy gaps
leading to an infinite inequity loop that reinforces structural inequities
through inaccurate and incomplete ground truth curation. We, therefore,
recommend a more critical look at the model design and its effect on equity and
a shift towards equity achieving predictive decision-making models. | [
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The incredible feats of athleticism demonstrated by humans are made possible
in part by a vast repertoire of general-purpose motor skills, acquired through
years of practice and experience. These skills not only enable humans to
perform complex tasks, but also provide powerful priors for guiding their
behaviors when learning new tasks. This is in stark contrast to what is common
practice in physics-based character animation, where control policies are most
typically trained from scratch for each task. In this work, we present a
large-scale data-driven framework for learning versatile and reusable skill
embeddings for physically simulated characters. Our approach combines
techniques from adversarial imitation learning and unsupervised reinforcement
learning to develop skill embeddings that produce life-like behaviors, while
also providing an easy to control representation for use on new downstream
tasks. Our models can be trained using large datasets of unstructured motion
clips, without requiring any task-specific annotation or segmentation of the
motion data. By leveraging a massively parallel GPU-based simulator, we are
able to train skill embeddings using over a decade of simulated experiences,
enabling our model to learn a rich and versatile repertoire of skills. We show
that a single pre-trained model can be effectively applied to perform a diverse
set of new tasks. Our system also allows users to specify tasks through simple
reward functions, and the skill embedding then enables the character to
automatically synthesize complex and naturalistic strategies in order to
achieve the task objectives. | [
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] |
Phase equilibrium calculations are an essential part of numerical simulations
of multi-component multi-phase flow in porous media, accounting for the largest
share of the computational time. In this work, we introduce a GPUenabled, fast,
and parallel framework, PTFlash, that vectorizes algorithms required for
isothermal two-phase flash calculations using PyTorch, and can facilitate a
wide range of downstream applications. In addition, to further accelerate
PTFlash, we design two task-specific neural networks, one for predicting the
stability of given mixtures and the other for providing estimates of the
distribution coefficients, which are trained offline and help shorten
computation time by sidestepping stability analysis and reducing the number of
iterations to reach convergence. The evaluation of PTFlash was conducted on
three case studies involving hydrocarbons, CO 2 and N 2 , for which the phase
equilibrium was tested over a large range of temperature, pressure and
composition conditions, using the Soave-Redlich-Kwong (SRK) equation of state.
We compare PTFlash with an in-house thermodynamic library, Carnot, written in
C++ and performing flash calculations one by one on CPU. Results show speed-ups
on large scale calculations up to two order of magnitudes, while maintaining
perfect precision with the reference solution provided by Carnot. | [
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] |
The advent of Artificial Intelligence (AI) has impacted all aspects of human
life. One of the concrete examples of AI impact is visible in radio
positioning. In this article, for the first time we utilize the power of AI by
training a Convolutional Neural Network (CNN) using 5G New Radio (NR)
fingerprints consisting of beamformed Channel State Information (CSI). By
observing CSI, it is possible to characterize the multipath channel between the
transmitter and the receiver, and thus provide a good source of spatiotemporal
data to find the position of a User Equipment (UE). We collect
ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals
from one Base Station (BS) is collected at the reference points with known
positions to train a CNN. We evaluate our work by testing: a) the robustness of
the trained network for estimating the positions for the new measurements on
the same reference points and b) the accuracy of the CNN-based position
estimation while the UE is on points other than the reference points. The
results prove that our trained network for a specific urban environment can
estimate the UE position with a minimum mean error of 0.98 m. | [
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] |
Ontology Matching (OM) plays an important role in many domains such as
bioinformatics and the Semantic Web, and its research is becoming increasingly
popular, especially with the application of machine learning (ML) techniques.
Although the Ontology Alignment Evaluation Initiative (OAEI) represents an
impressive effort for the systematic evaluation of OM systems, it still suffers
from several limitations including limited evaluation of subsumption mappings,
suboptimal reference mappings, and limited support for the evaluation of
ML-based systems. To tackle these limitations, we introduce five new biomedical
OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes
both equivalence and subsumption matching; the quality of reference mappings is
ensured by human curation, ontology pruning, etc.; and a comprehensive
evaluation framework is proposed to measure OM performance from various
perspectives for both ML-based and non-ML-based OM systems. We report
evaluation results for OM systems of different types to demonstrate the usage
of these resources, all of which are publicly available as part of the new
BioML track at OAEI 2022. | [
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] |
The progression of innovation and technology and ease of inter-connectivity
among networks has allowed us to evolve towards one of the promising areas, the
Internet of Vehicles. Nowadays, modern vehicles are connected to a range of
networks, including intra-vehicle networks and external networks. However, a
primary challenge in the automotive industry is to make the vehicle safe and
reliable; particularly with the loopholes in the existing traditional
protocols, cyber-attacks on the vehicle network are rising drastically.
Practically every vehicle uses the universal Controller Area Network (CAN) bus
protocol for the communication between electronic control units to transmit key
vehicle functionality and messages related to driver safety. The CAN bus
system, although its critical significance, lacks the key feature of any
protocol authentication and authorization. Resulting in compromises of CAN bus
security leads to serious issues to both car and driver safety. This paper
discusses the security issues of the CAN bus protocol and proposes an Intrusion
Detection System (IDS) that detects known attacks on in-vehicle networks.
Multiple Artificial Intelligence (AI) algorithms are employed to provide
recognition of known potential cyber-attacks based on messages, timestamps, and
data packets traveling through the CAN. The main objective of this paper is to
accurately detect cyberattacks by considering time-series features and attack
frequency. The majority of the evaluated AI algorithms, when considering attack
frequency, correctly identify known attacks with remarkable accuracy of more
than 99%. However, these models achieve approximately 92% to 97% accuracy when
timestamps are not taken into account. Long Short Term Memory (LSTM), Xgboost,
and SVC have proved to the well-performing classifiers. | [
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] |
Personalized recommendation models (RecSys) are one of the most popular
machine learning workload serviced by hyperscalers. A critical challenge of
training RecSys is its high memory capacity requirements, reaching hundreds of
GBs to TBs of model size. In RecSys, the so-called embedding layers account for
the majority of memory usage so current systems employ a hybrid CPU-GPU design
to have the large CPU memory store the memory hungry embedding layers.
Unfortunately, training embeddings involve several memory bandwidth intensive
operations which is at odds with the slow CPU memory, causing performance
overheads. Prior work proposed to cache frequently accessed embeddings inside
GPU memory as means to filter down the embedding layer traffic to CPU memory,
but this paper observes several limitations with such cache design. In this
work, we present a fundamentally different approach in designing embedding
caches for RecSys. Our proposed ScratchPipe architecture utilizes unique
properties of RecSys training to develop an embedding cache that not only sees
the past but also the "future" cache accesses. ScratchPipe exploits such
property to guarantee that the active working set of embedding layers can
"always" be captured inside our proposed cache design, enabling embedding layer
training to be conducted at GPU memory speed. | [
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Graph neural networks (GNNs) can extract features by learning both the
representation of each objects (i.e., graph nodes) and the relationship across
different objects (i.e., the edges that connect nodes), achieving
state-of-the-art performance in various graph-based tasks. Despite its
strengths, utilizing these algorithms in a production environment faces several
challenges as the number of graph nodes and edges amount to several billions to
hundreds of billions scale, requiring substantial storage space for training.
Unfortunately, state-of-the-art ML frameworks employ an in-memory processing
model which significantly hampers the productivity of ML practitioners as it
mandates the overall working set to fit within DRAM capacity. In this work, we
first conduct a detailed characterization on a state-of-the-art, large-scale
GNN training algorithm, GraphSAGE. Based on the characterization, we then
explore the feasibility of utilizing capacity-optimized NVM SSDs for storing
memory-hungry GNN data, which enables large-scale GNN training beyond the
limits of main memory size. Given the large performance gap between DRAM and
SSD, however, blindly utilizing SSDs as a direct substitute for DRAM leads to
significant performance loss. We therefore develop SmartSAGE, our
software/hardware co-design based on an in-storage processing (ISP)
architecture. Our work demonstrates that an ISP based large-scale GNN training
system can achieve both high capacity storage and high performance, opening up
opportunities for ML practitioners to train large GNN datasets without being
hampered by the physical limitations of main memory size. | [
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] |
Preventing poaching through ranger patrols protects endangered wildlife,
directly contributing to the UN Sustainable Development Goal 15 of life on
land. Combinatorial bandits have been used to allocate limited patrol
resources, but existing approaches overlook the fact that each location is home
to multiple species in varying proportions, so a patrol benefits each species
to differing degrees. When some species are more vulnerable, we ought to offer
more protection to these animals; unfortunately, existing combinatorial bandit
approaches do not offer a way to prioritize important species. To bridge this
gap, (1) We propose a novel combinatorial bandit objective that trades off
between reward maximization and also accounts for prioritization over species,
which we call ranked prioritization. We show this objective can be expressed as
a weighted linear sum of Lipschitz-continuous reward functions. (2) We provide
RankedCUCB, an algorithm to select combinatorial actions that optimize our
prioritization-based objective, and prove that it achieves asymptotic
no-regret. (3) We demonstrate empirically that RankedCUCB leads to up to 38%
improvement in outcomes for endangered species using real-world wildlife
conservation data. Along with adapting to other challenges such as preventing
illegal logging and overfishing, our no-regret algorithm addresses the general
combinatorial bandit problem with a weighted linear objective. | [
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] |
Variational quantum machine learning is an extensively studied application of
near-term quantum computers. The success of variational quantum learning models
crucially depends on finding a suitable parametrization of the model that
encodes an inductive bias relevant to the learning task. However, precious
little is known about guiding principles for the construction of suitable
parametrizations. In this work, we holistically explore when and how symmetries
of the learning problem can be exploited to construct quantum learning models
with outcomes invariant under the symmetry of the learning task. Building on
tools from representation theory, we show how a standard gateset can be
transformed into an equivariant gateset that respects the symmetries of the
problem at hand through a process of gate symmetrization. We benchmark the
proposed methods on two toy problems that feature a non-trivial symmetry and
observe a substantial increase in generalization performance. As our tools can
also be applied in a straightforward way to other variational problems with
symmetric structure, we show how equivariant gatesets can be used in
variational quantum eigensolvers. | [
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Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS)
platforms to allow users to access large-scale cloudbased models via APIs. In
addition to prediction outputs, these APIs can also provide other information
in a more human-understandable way, such as counterfactual explanations (CF).
However, such extra information inevitably causes the cloud models to be more
vulnerable to extraction attacks which aim to steal the internal functionality
of models in the cloud. Due to the black-box nature of cloud models, however, a
vast number of queries are inevitably required by existing attack strategies
before the substitute model achieves high fidelity. In this paper, we propose a
novel simple yet efficient querying strategy to greatly enhance the querying
efficiency to steal a classification model. This is motivated by our
observation that current querying strategies suffer from decision boundary
shift issue induced by taking far-distant queries and close-to-boundary CFs
into substitute model training. We then propose DualCF strategy to circumvent
the above issues, which is achieved by taking not only CF but also
counterfactual explanation of CF (CCF) as pairs of training samples for the
substitute model. Extensive and comprehensive experimental evaluations are
conducted on both synthetic and real-world datasets. The experimental results
favorably illustrate that DualCF can produce a high-fidelity model with fewer
queries efficiently and effectively. | [
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] |
Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL
problems that does not require value functions and uses only supervised
learning, where the targets for given inputs in a dataset do not change over
time. Ghosh et al. proved that Goal-Conditional Supervised Learning (GCSL) --
which can be viewed as a simplified version of UDRL -- optimizes a lower bound
on goal-reaching performance. This raises expectations that such algorithms may
enjoy guaranteed convergence to the optimal policy in arbitrary environments,
similar to certain well-known traditional RL algorithms. Here we show that for
a specific episodic UDRL algorithm (eUDRL, including GCSL), this is not the
case, and give the causes of this limitation. To do so, we first introduce a
helpful rewrite of eUDRL as a recursive policy update. This formulation helps
to disprove its convergence to the optimal policy for a wide class of
stochastic environments. Finally, we provide a concrete example of a very
simple environment where eUDRL diverges. Since the primary aim of this paper is
to present a negative result, and the best counterexamples are the simplest
ones, we restrict all discussions to finite (discrete) environments, ignoring
issues of function approximation and limited sample size. | [
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] |
In this paper, we present DendroMap, a novel approach to interactively
exploring large-scale image datasets for machine learning (ML). ML
practitioners often explore image datasets by generating a grid of images or
projecting high-dimensional representations of images into 2-D using
dimensionality reduction techniques (e.g., t-SNE). However, neither approach
effectively scales to large datasets because images are ineffectively organized
and interactions are insufficiently supported. To address these challenges, we
develop DendroMap by adapting Treemaps, a well-known visualization technique.
DendroMap effectively organizes images by extracting hierarchical cluster
structures from high-dimensional representations of images. It enables users to
make sense of the overall distributions of datasets and interactively zoom into
specific areas of interests at multiple levels of abstraction. Our case studies
with widely-used image datasets for deep learning demonstrate that users can
discover insights about datasets and trained models by examining the diversity
of images, identifying underperforming subgroups, and analyzing classification
errors. We conducted a user study that evaluates the effectiveness of DendroMap
in grouping and searching tasks by comparing it with a gridified version of
t-SNE and found that participants preferred DendroMap. DendroMap is available
at https://div-lab.github.io/dendromap/. | [
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In general, multiple domain cyberspace security assessments can be
implemented by reasoning user's permissions. However, while existing methods
include some information from the physical and social domains, they do not
provide a comprehensive representation of cyberspace. Existing reasoning
methods are also based on expert-given rules, resulting in inefficiency and a
low degree of intelligence. To address this challenge, we create a Knowledge
Graph (KG) of multiple domain cyberspace in order to provide a standard
semantic description of the multiple domain cyberspace. Following that, we
proposed a user's permissions reasoning method based on reinforcement learning.
All permissions in cyberspace are represented as nodes, and an agent is trained
to find all permissions that user can have according to user's initial
permissions and cyberspace KG. We set 10 reward setting rules based on the
features of cyberspace KG in the reinforcement learning of reward information
setting, so that the agent can better locate user's all permissions and avoid
blindly finding user's permissions. The results of the experiments showed that
the proposed method can successfully reason about user's permissions and
increase the intelligence level of the user's permissions reasoning method. At
the same time, the F1 value of the proposed method is 6% greater than that of
the Translating Embedding (TransE) method. | [
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] |
In recent years cybersecurity has become a major concern in adaptation of
smart applications. Specially, in smart homes where a large number of IoT
devices are used having a secure and trusted mechanisms can provide peace of
mind for users. Accurate detection of cyber attacks is crucial, however precise
identification of the type of attacks plays a huge role if devising the
countermeasure for protecting the system. Artificial Neural Networks (ANN) have
provided promising results for detecting any security attacks for smart
applications. However, due to complex nature of the model used for this
technique it is not easy for normal users to trust ANN based security
solutions. Also, selection of right hyperparameters for ANN architecture plays
a crucial role in the accurate detection of security attacks, especially when
it come to identifying the subcategories of attacks. In this paper, we propose
a model that considers both the issues of explainability of ANN model and the
hyperparameter selection for this approach to be easily trusted and adapted by
users of smart home applications. Also, our approach considers a subset of the
dataset for optimal selection of hyperparamters to reduce the overhead of the
process of ANN architecture design. Distinctively this paper focuses on
configuration, performance and evaluation of ANN architecture for
identification of five categorical attacks and nine subcategorical attacks.
Using a very recent IoT dataset our approach showed high performance for
intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for Binary, Category,
and Subcategory level classification of attacks. | [
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] |
Accurate ADMET (an abbreviation for "absorption, distribution, metabolism,
excretion, and toxicity") predictions can efficiently screen out undesirable
drug candidates in the early stage of drug discovery. In recent years, multiple
comprehensive ADMET systems that adopt advanced machine learning models have
been developed, providing services to estimate multiple endpoints. However,
those ADMET systems usually suffer from weak extrapolation ability. First, due
to the lack of labelled data for each endpoint, typical machine learning models
perform frail for the molecules with unobserved scaffolds. Second, most systems
only provide fixed built-in endpoints and cannot be customised to satisfy
various research requirements. To this end, we develop a robust and endpoint
extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept
of self-supervised learning to produce a robust pre-trained model. The model is
then fine-tuned with a multi-task and multi-stage framework to transfer
knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks.
Our results demonstrate that H-ADMET achieves an overall improvement of 4%,
compared with existing ADMET systems on comparable endpoints. Additionally, the
pre-trained model provided by H-ADMET can be fine-tuned to generate new and
customised ADMET endpoints, meeting various demands of drug research and
development requirements. | [
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Supervised learning techniques typically assume training data originates from
the target population. Yet, in reality, dataset shift frequently arises, which,
if not adequately taken into account, may decrease the performance of their
predictors. In this work, we propose a novel and flexible framework called
DetectShift that quantifies and tests for multiple dataset shifts, encompassing
shifts in the distributions of $(X, Y)$, $X$, $Y$, $X|Y$, and $Y|X$.
DetectShift equips practitioners with insights into data shifts, facilitating
the adaptation or retraining of predictors using both source and target data.
This proves extremely valuable when labeled samples in the target domain are
limited. The framework utilizes test statistics with the same nature to
quantify the magnitude of the various shifts, making results more
interpretable. It is versatile, suitable for regression and classification
tasks, and accommodates diverse data forms - tabular, text, or image.
Experimental results demonstrate the effectiveness of DetectShift in detecting
dataset shifts even in higher dimensions. | [
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] |
In human contact, emotion is very crucial. Attributes like words, voice
intonation, facial expressions, and kinesics can all be used to portray one's
feelings. However, brain-computer interface (BCI) devices have not yet reached
the level required for emotion interpretation. With the rapid development of
machine learning algorithms, dry electrode techniques, and different real-world
applications of the brain-computer interface for normal individuals, emotion
categorization from EEG data has recently gotten a lot of attention.
Electroencephalogram (EEG) signals are a critical resource for these systems.
The primary benefit of employing EEG signals is that they reflect true emotion
and are easily resolved by computer systems. In this work, EEG signals
associated with good, neutral, and negative emotions were identified using
channel selection preprocessing. However, researchers had a limited grasp of
the specifics of the link between various emotional states until now. To
identify EEG signals, we used discrete wavelet transform and machine learning
techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN)
algorithm. Initially, the classifier methods were utilized for channel
selection. As a result, final feature vectors were created by integrating the
features of EEG segments from these channels. Using the RNN and kNN algorithms,
the final feature vectors with connected positive, neutral, and negative
emotions were categorized independently. The classification performance of both
techniques is computed and compared. Using RNN and kNN, the average overall
accuracies were 94.844 % and 93.438 %, respectively. | [
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] |
Constrained random test generation is one of the most widely adopted methods
for generating stimuli for simulation-based verification. Randomness leads to
test diversity, but tests tend to repeatedly exercise the same design logic.
Constraints are written (typically manually) to bias random tests towards
interesting, hard-to-reach, and yet-untested logic. However, as verification
progresses, most constrained random tests yield little to no effect on
functional coverage. If stimuli generation consumes significantly less
resources than simulation, then a better approach involves randomly generating
a large number of tests, selecting the most effective subset, and only
simulating that subset. In this paper, we introduce a novel method for
automatic constraint extraction and test selection. This method, which we call
coverage-directed test selection, is based on supervised learning from coverage
feedback. Our method biases selection towards tests that have a high
probability of increasing functional coverage, and prioritises them for
simulation. We show how coverage-directed test selection can reduce manual
constraint writing, prioritise effective tests, reduce verification resource
consumption, and accelerate coverage closure on a large, real-life industrial
hardware design. | [
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] |
Graphical structures estimated by causal learning algorithms from time series
data can provide highly misleading causal information if the causal timescale
of the generating process fails to match the measurement timescale of the data.
Although this problem has been recently recognized, practitioners have limited
resources to respond to it, and so must continue using models that they know
are likely misleading. Existing methods either (a) require that the difference
between causal and measurement timescales is known; or (b) can handle only very
small number of random variables when the timescale difference is unknown; or
(c) apply to only pairs of variables, though with fewer assumptions about prior
knowledge; or (d) return impractically too many solutions. This paper addresses
all four challenges. We combine constraint programming with both theoretical
insights into the problem structure and prior information about admissible
causal interactions. The resulting system provides a practical approach that
scales to significantly larger sets (>100) of random variables, does not
require precise knowledge of the timescale difference, supports edge
misidentification and parametric connection strengths, and can provide the
optimum choice among many possible solutions. The cumulative impact of these
improvements is gain of multiple orders of magnitude in speed and
informativeness. | [
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] |
Graph embedding methods including traditional shallow models and deep Graph
Neural Networks (GNNs) have led to promising applications in recommendation.
Nevertheless, shallow models especially random-walk-based algorithms fail to
adequately exploit neighbor proximity in sampled subgraphs or sequences due to
their optimization paradigm. GNN-based algorithms suffer from the insufficient
utilization of high-order information and easily cause over-smoothing problems
when stacking too much layers, which may deteriorate the recommendations of
low-degree (long-tail) items, limiting the expressiveness and scalability. In
this paper, we propose a novel framework SAC, namely Spatial Autoregressive
Coding, to solve the above problems in a unified way. To adequately leverage
neighbor proximity and high-order information, we design a novel spatial
autoregressive paradigm. Specifically, we first randomly mask multi-hop
neighbors and embed the target node by integrating all other surrounding
neighbors with an explicit multi-hop attention. Then we reinforce the model to
learn a neighbor-predictive coding for the target node by contrasting the
coding and the masked neighbors' embedding, equipped with a new hard negative
sampling strategy. To learn the minimal sufficient representation for the
target-to-neighbor prediction task and remove the redundancy of neighbors, we
devise Neighbor Information Bottleneck by maximizing the mutual information
between target predictive coding and the masked neighbors' embedding, and
simultaneously constraining those between the coding and surrounding neighbors'
embedding. Experimental results on both public recommendation datasets and a
real scenario web-scale dataset Douyin-Friend-Recommendation demonstrate the
superiority of SAC compared with state-of-the-art methods. | [
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] |
IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in
Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a
color respectively. To communicate their digit, users press the button with the
same color that is assigned to their digit, which can thus be identified by
elimination after a few iterations. IFTT-PIN uses the same principle but does
not pre-assign colors to each button. Instead, users are free to choose which
button to use for each color. The button-to-color mapping only exists in the
user's mind and is never directly communicated to the interface. In other
words, IFTT-PIN infers both the user's PIN and their preferred button-to-color
mapping at the same time, a process called self-calibration. In this paper, we
present online interactive demonstrations of IFTT-PIN with and without
self-calibration and introduce the key concepts and assumptions making
self-calibration possible. IFTT-PIN can be tested at
https://jgrizou.github.io/IFTT-PIN/ with a video introduction available at
https://youtu.be/5I1ibPJdLHM. We review related work in the field of
brain-computer interface and further propose self-calibration as a novel
approach to protect users against shoulder surfing attacks. Finally, we
introduce a vault cracking challenge as a test of usability and security that
was informally tested at our institute. With IFTT-PIN, we wish to demonstrate a
new interactive experience where users can decide actively and on-the-fly how
to use an interface. The self-calibration paradigm might lead to novel
opportunities for interaction in other applications or domains. We hope this
work will inspire the community to invent them. | [
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] |
In a world increasingly dependent on road-based transportation, it is
essential to understand vehicles. We introduce the AI mechanic, an acoustic
vehicle characterization deep learning system, as an integrated approach using
sound captured from mobile devices to enhance transparency and understanding of
vehicles and their condition for non-expert users. We develop and implement
novel cascading architectures for vehicle understanding, which we define as
sequential, conditional, multi-level networks that process raw audio to extract
highly-granular insights. To showcase the viability of cascading architectures,
we build a multi-task convolutional neural network that predicts and cascades
vehicle attributes to enhance fault detection. We train and test these models
on a synthesized dataset reflecting more than 40 hours of augmented audio and
achieve >92% validation set accuracy on attributes (fuel type, engine
configuration, cylinder count and aspiration type). Our cascading architecture
additionally achieved 93.6% validation and 86.8% test set accuracy on misfire
fault prediction, demonstrating margins of 16.4% / 7.8% and 4.2% / 1.5%
improvement over na\"ive and parallel baselines. We explore experimental
studies focused on acoustic features, data augmentation, feature fusion, and
data reliability. Finally, we conclude with a discussion of broader
implications, future directions, and application areas for this work. | [
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] |
Multi-access edge computing (MEC) and network function virtualization (NFV)
are promising technologies to support emerging IoT applications, especially
those computation-intensive. In NFV-enabled MEC environment, service function
chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be
mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive
applications, which can be represented by SFCs, fully or partially to MEC
servers for remote execution. This paper studies the partial offloading and SFC
mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where
an incoming task can be partitioned into two parts, one for local execution and
the other for remote execution. The objective is to minimize the average cost
in the long term which is a combination of execution delay, MD's energy
consumption, and usage charge for edge computing. This problem consists of two
closely related decision-making steps, namely task partition and VNF placement,
which is highly complex and quite challenging. To address this, we propose a
cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where we
design a framework enabling interaction between two agents. Simulation results
show that the proposed algorithm outperforms three combinations of deep
reinforcement learning algorithms in terms of cumulative and average episodic
rewards and it overweighs a number of baseline algorithms with respect to
execution delay, energy consumption, and usage charge. | [
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] |
Dyson spheres are hypothetical megastructures encircling stars in order to
harvest most of their energy output. During the 11th edition of the GTOC
challenge, participants were tasked with a complex trajectory planning related
to the construction of a precursor Dyson structure, a heliocentric ring made of
twelve stations. To this purpose, we developed several new approaches that
synthesize techniques from machine learning, combinatorial optimization,
planning and scheduling, and evolutionary optimization effectively integrated
into a fully automated pipeline. These include a machine learned transfer time
estimator, improving the established Edelbaum approximation and thus better
informing a Lazy Race Tree Search to identify and collect asteroids with high
arrival mass for the stations; a series of optimally-phased low-thrust
transfers to all stations computed by indirect optimization techniques,
exploiting the synodic periodicity of the system; and a modified Hungarian
scheduling algorithm, which utilizes evolutionary techniques to arrange a
mass-balanced arrival schedule out of all transfer possibilities. We describe
the steps of our pipeline in detail with a special focus on how our approaches
mutually benefit from each other. Lastly, we outline and analyze the final
solution of our team, ACT&Friends, which ranked second at the GTOC 11
challenge. | [
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] |
Federated learning (FL) enables multiple clients to collaboratively train
models without sharing their local data, and becomes an important
privacy-preserving machine learning framework. However, classical FL faces
serious security and robustness problem, e.g., malicious clients can poison
model updates and at the same time claim large quantities to amplify the impact
of their model updates in the model aggregation. Existing defense methods for
FL, while all handling malicious model updates, either treat all quantities
benign or simply ignore/truncate the quantities of all clients. The former is
vulnerable to quantity-enhanced attack, while the latter leads to sub-optimal
performance since the local data on different clients is usually in
significantly different sizes. In this paper, we propose a robust
quantity-aware aggregation algorithm for federated learning, called FedRA, to
perform the aggregation with awareness of local data quantities while being
able to defend against quantity-enhanced attacks. More specifically, we propose
a method to filter malicious clients by jointly considering the uploaded model
updates and data quantities from different clients, and performing
quantity-aware weighted averaging on model updates from remaining clients.
Moreover, as the number of malicious clients participating in the federated
learning may dynamically change in different rounds, we also propose a
malicious client number estimator to predict how many suspicious clients should
be filtered in each round. Experiments on four public datasets demonstrate the
effectiveness of our FedRA method in defending FL against quantity-enhanced
attacks. | [
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] |
Sequential recommendation (SR) learns users' preferences by capturing the
sequential patterns from users' behaviors evolution. As discussed in many
works, user-item interactions of SR generally present the intrinsic power-law
distribution, which can be ascended to hierarchy-like structures. Previous
methods usually handle such hierarchical information by making user-item
sectionalization empirically under Euclidean space, which may cause distortion
of user-item representation in real online scenarios. In this paper, we propose
a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the
sequential pattern information as well as hierarchical information contained in
the data of SR scenarios simultaneously. Specifically, for the purpose of
explicitly capturing the hierarchical information, we first construct a
weighted user-item heterogeneous graph by aliening all the user-item
interactions to improve the perception domain of each user from a global view.
Then the output of the global representation would be used to complement the
local directed item-item homogeneous graph convolution. By defining a novel
hyperbolic inner product operator, the global and local graph representation
learning are directly conducted in Poincar\'{e} ball instead of commonly used
projection operation between Poincar\'{e} ball and Euclidean space, which could
alleviate the cumulative error issue of general bidirectional translation
process. Moreover, for the purpose of explicitly capturing the sequential
dependency information, we design two types of temporal attention operations
under Poincar\'{e} ball space. Empirical evaluations on datasets from the
public and financial industry show that PHGR outperforms several comparison
methods. | [
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Stochastic Programming is a powerful modeling framework for decision-making
under uncertainty. In this work, we tackle two-stage stochastic programs
(2SPs), the most widely used class of stochastic programming models. Solving
2SPs exactly requires optimizing over an expected value function that is
computationally intractable. Having a mixed-integer linear program (MIP) or a
nonlinear program (NLP) in the second stage further aggravates the
intractability, even when specialized algorithms that exploit problem structure
are employed. Finding high-quality (first-stage) solutions -- without
leveraging problem structure -- can be crucial in such settings. We develop
Neur2SP, a new method that approximates the expected value function via a
neural network to obtain a surrogate model that can be solved more efficiently
than the traditional extensive formulation approach. Neur2SP makes no
assumptions about the problem structure, in particular about the second-stage
problem, and can be implemented using an off-the-shelf MIP solver. Our
extensive computational experiments on four benchmark 2SP problem classes with
different structures (containing MIP and NLP second-stage problems) demonstrate
the efficiency (time) and efficacy (solution quality) of Neur2SP. In under 1.66
seconds, Neur2SP finds high-quality solutions across all problems even as the
number of scenarios increases, an ideal property that is difficult to have for
traditional 2SP solution techniques. Namely, the most generic baseline method
typically requires minutes to hours to find solutions of comparable quality. | [
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] |
Artificial Intelligence is now recognized as a general-purpose technology
with ample impact on human life. In this work, we aim to understand the
evolution of AI and Machine learning over the years by analyzing researchers'
impact, influence, and leadership over the last decades. This work also intends
to shed new light on the history and evolution of AI by exploring the dynamics
involved in the field's evolution through the lenses of the papers published on
AI conferences since the first International Joint Conference on Artificial
Intelligence (IJCAI) in 1969. AI development and evolution have led to
increasing research output, reflected in the number of articles published over
the last sixty years. We construct comprehensive citation-collaboration and
paper-author datasets and compute corresponding centrality measures to carry
out our analyses. These analyses allow a better understanding of how AI has
reached its current state of affairs in research. Throughout the process, we
correlate these datasets with the work of the ACM Turing Award winners and the
so-called two AI winters the field has gone through. We also look at
self-citation trends and new authors' behaviors. Finally, we present a novel
way to infer the country of affiliation of a paper from its organization.
Therefore, this work provides a deep analysis of Artificial Intelligence
history from information gathered and analyzed from large technical venues
datasets and suggests novel insights that can contribute to understanding and
measuring AI's evolution. | [
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] |
Embodied agents in continuous control domains have had limited exposure to
tasks allowing to explore musculoskeletal properties that enable agile and
nimble behaviors in biological beings. The sophistication behind
neuro-musculoskeletal control can pose new challenges for the motor learning
community. At the same time, agents solving complex neural control problems
allow impact in fields such as neuro-rehabilitation, as well as
collaborative-robotics. Human biomechanics underlies complex
multi-joint-multi-actuator musculoskeletal systems. The sensory-motor system
relies on a range of sensory-contact rich and proprioceptive inputs that define
and condition muscle actuation required to exhibit intelligent behaviors in the
physical world. Current frameworks for musculoskeletal control do not support
physiological sophistication of the musculoskeletal systems along with physical
world interaction capabilities. In addition, they are neither embedded in
complex and skillful motor tasks nor are computationally effective and scalable
to study large-scale learning paradigms. Here, we present MyoSuite -- a suite
of physiologically accurate biomechanical models of elbow, wrist, and hand,
with physical contact capabilities, which allow learning of complex and
skillful contact-rich real-world tasks. We provide diverse motor-control
challenges: from simple postural control to skilled hand-object interactions
such as turning a key, twirling a pen, rotating two balls in one hand, etc. By
supporting physiological alterations in musculoskeletal geometry (tendon
transfer), assistive devices (exoskeleton assistance), and muscle contraction
dynamics (muscle fatigue, sarcopenia), we present real-life tasks with temporal
changes, thereby exposing realistic non-stationary conditions in our tasks
which most continuous control benchmarks lack. | [
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The Streaming Engine (SE) is a Coarse-Grained Reconfigurable Array which
provides programming flexibility and high-performance with energy efficiency.
An application program to be executed on the SE is represented as a combination
of Synchronous Data Flow (SDF) graphs, where every instruction is represented
as a node. Each node needs to be mapped to the right slot and array in the SE
to ensure the correct execution of the program. This creates an optimization
problem with a vast and sparse search space for which finding a mapping
manually is impractical because it requires expertise and knowledge of the SE
micro-architecture. In this work we propose a Reinforcement Learning framework
with Global Graph Attention (GGA) module and output masking of invalid
placements to find and optimize instruction schedules. We use Proximal Policy
Optimization in order to train a model which places operations into the SE
tiles based on a reward function that models the SE device and its constraints.
The GGA module consists of a graph neural network and an attention module. The
graph neural network creates embeddings of the SDFs and the attention block is
used to model sequential operation placement. We show results on how certain
workloads are mapped to the SE and the factors affecting mapping quality. We
find that the addition of GGA, on average, finds 10% better instruction
schedules in terms of total clock cycles taken and masking improves reward
obtained by 20%. | [
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] |
We present a machine sound dataset to benchmark domain generalization
techniques for anomalous sound detection (ASD). Domain shifts are differences
in data distributions that can degrade the detection performance, and handling
them is a major issue for the application of ASD systems. While currently
available datasets for ASD tasks assume that occurrences of domain shifts are
known, in practice, they can be difficult to detect. To handle such domain
shifts, domain generalization techniques that perform well regardless of the
domains should be investigated. In this paper, we present the first ASD dataset
for the domain generalization techniques, called MIMII DG. The dataset consists
of five machine types and three domain shift scenarios for each machine type.
The dataset is dedicated to the domain generalization task with features such
as multiple different values for parameters that cause domain shifts and
introduction of domain shifts that can be difficult to detect, such as shifts
in the background noise. Experimental results using two baseline systems
indicate that the dataset reproduces domain shift scenarios and is useful for
benchmarking domain generalization techniques. | [
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] |
Though PINNs (physics-informed neural networks) are now deemed as a
complement to traditional CFD (computational fluid dynamics) solvers rather
than a replacement, their ability to solve the Navier-Stokes equations without
given data is still of great interest. This report presents our
not-so-successful experiments of solving the Navier-Stokes equations with PINN
as a replacement for traditional solvers. We aim to, with our experiments,
prepare readers for the challenges they may face if they are interested in
data-free PINN. In this work, we used two standard flow problems: 2D
Taylor-Green vortex at Re=100 and 2D cylinder flow at Re=200. The PINN method
solved the 2D Taylor-Green vortex problem with acceptable results, and we used
this flow as an accuracy and performance benchmark. About 32 hours of training
were required for the PINN method's accuracy to match the accuracy of a 16x16
finite-difference simulation, which took less than 20 seconds. The 2D cylinder
flow, on the other hand, did not produce a physical solution. The PINN method
behaved like a steady-flow solver and did not capture the vortex shedding
phenomenon. By sharing our experience, we would like to emphasize that the PINN
method is still a work-in-progress, especially in terms of solving flow
problems without any given data. More work is needed to make PINN feasible for
real-world problems in such applications. | [
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] |
Rational approximations are introduced and studied in granular graded rough
sets and generalizations thereof by the first author in recent research papers.
The concept of rationality is determined by related ontologies and coherence
between granularity, mereology and approximations in the context. In addition,
a framework for rational approximations is introduced by her in the mentioned
paper(s). Granular approximations constructed as per the procedures of variable
precision rough sets (VPRS) are likely to be more rational than those
constructed from a classical perspective under certain conditions. This may
continue to hold for some generalizations of the former. However, a formal
characterization of such conditions is not available in the previously
published literature. In this research, theoretical aspects of the problem are
critically examined, uniform generalizations of granular VPRS are introduced,
new connections with granular graded rough sets are proved, appropriate
concepts of substantial parthood are introduced, their extent of compatibility
with the framework is accessed, and the framework is extended. Basic
assumptions are explained in detail, and additional examples are constructed
for readability. Furthermore, meta applications to cluster validation, image
segmentation and dynamic sorting are invented. Extensions to direct
generalizations of VPRS such as probabilistic rough sets are a natural
consequence of the work. | [
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] |
Monumental advancements in artificial intelligence (AI) have lured the
interest of doctors, lenders, judges, and other professionals. While these
high-stakes decision-makers are optimistic about the technology, those familiar
with AI systems are wary about the lack of transparency of its decision-making
processes. Perturbation-based post hoc explainers offer a model agnostic means
of interpreting these systems while only requiring query-level access. However,
recent work demonstrates that these explainers can be fooled adversarially.
This discovery has adverse implications for auditors, regulators, and other
sentinels. With this in mind, several natural questions arise - how can we
audit these black box systems? And how can we ascertain that the auditee is
complying with the audit in good faith? In this work, we rigorously formalize
this problem and devise a defense against adversarial attacks on
perturbation-based explainers. We propose algorithms for the detection
(CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our
novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our
approach successfully detects whether a black box system adversarially conceals
its decision-making process and mitigates the adversarial attack on real-world
data for the prevalent explainers, LIME and SHAP. | [
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] |
With the growing popularity of artificial intelligence and machine learning,
a wide spectrum of attacks against deep learning models have been proposed in
the literature. Both the evasion attacks and the poisoning attacks attempt to
utilize adversarially altered samples to fool the victim model to misclassify
the adversarial sample. While such attacks claim to be or are expected to be
stealthy, i.e., imperceptible to human eyes, such claims are rarely evaluated.
In this paper, we present the first large-scale study on the stealthiness of
adversarial samples used in the attacks against deep learning. We have
implemented 20 representative adversarial ML attacks on six popular
benchmarking datasets. We evaluate the stealthiness of the attack samples using
two complementary approaches: (1) a numerical study that adopts 24 metrics for
image similarity or quality assessment; and (2) a user study of 3 sets of
questionnaires that has collected 20,000+ annotations from 1,000+ responses.
Our results show that the majority of the existing attacks introduce
nonnegligible perturbations that are not stealthy to human eyes. We further
analyze the factors that contribute to attack stealthiness. We further examine
the correlation between the numerical analysis and the user studies, and
demonstrate that some image quality metrics may provide useful guidance in
attack designs, while there is still a significant gap between assessed image
quality and visual stealthiness of attacks. | [
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] |
The problem of interpreting or aggregating multiple rankings is common to
many real-world applications. Perhaps the simplest and most common approach is
a weighted rank aggregation, wherein a (convex) weight is applied to each input
ranking and then ordered. This paper describes a new tool for visualizing and
displaying ranking information for the weighted rank aggregation method.
Traditionally, the aim of rank aggregation is to summarize the information from
the input rankings and provide one final ranking that hopefully represents a
more accurate or truthful result than any one input ranking. While such an
aggregated ranking is, and clearly has been, useful to many applications, it
also obscures information. In this paper, we show the wealth of information
that is available for the weighted rank aggregation problem due to its
structure. We apply weight set decomposition to the set of convex multipliers,
study the properties useful for understanding this decomposition, and visualize
the indifference regions. This methodology reveals information--that is
otherwise collapsed by the aggregated ranking--into a useful, interpretable,
and intuitive decision support tool. Included are multiple illustrative
examples, along with heuristic and exact algorithms for computing the weight
set decomposition. | [
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] |
Music can be represented in multiple forms, such as in the audio form as a
recording of a performance, in the symbolic form as a computer readable score,
or in the image form as a scan of the sheet music. Music synchronisation
provides a way to navigate among multiple representations of music in a unified
manner by generating an accurate mapping between them, lending itself
applicable to a myriad of domains like music education, performance analysis,
automatic accompaniment and music editing. Traditional synchronisation methods
compute alignment using knowledge-driven and stochastic approaches, typically
employing handcrafted features. These methods are often unable to generalise
well to different instruments, acoustic environments and recording conditions,
and normally assume complete structural agreement between the performances and
the scores. This PhD furthers the development of performance-score
synchronisation research by proposing data-driven, context-aware alignment
approaches, on three fronts: Firstly, I replace the handcrafted features by
employing a metric learning based approach that is adaptable to different
acoustic settings and performs well in data-scarce conditions. Secondly, I
address the handling of structural differences between the performances and
scores, which is a common limitation of standard alignment methods. Finally, I
eschew the reliance on both feature engineering and dynamic programming, and
propose a completely data-driven synchronisation method that computes
alignments using a neural framework, whilst also being robust to structural
differences between the performances and scores. | [
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] |
Applying reinforcement learning to autonomous driving entails particular
challenges, primarily due to dynamically changing traffic flows. To address
such challenges, it is necessary to quickly determine response strategies to
the changing intentions of surrounding vehicles. This paper proposes a new
policy optimization method for safe driving using graph-based interaction-aware
constraints. In this framework, the motion prediction and control modules are
trained simultaneously while sharing a latent representation that contains a
social context. To reflect social interactions, we illustrate the movements of
agents in graph form and filter the features with the graph convolution
networks. This helps preserve the spatiotemporal locality of adjacent nodes.
Furthermore, we create feedback loops to combine these two modules effectively.
As a result, this approach encourages the learned controller to be safe from
dynamic risks and renders the motion prediction robust to abnormal movements.
In the experiment, we set up a navigation scenario comprising various
situations with CARLA, an urban driving simulator. The experiments show
state-of-the-art performance on navigation strategy and motion prediction
compared to the baselines. | [
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A growing body of research runs human subject evaluations to study whether
providing users with explanations of machine learning models can help them with
practical real-world use cases. However, running user studies is challenging
and costly, and consequently each study typically only evaluates a limited
number of different settings, e.g., studies often only evaluate a few
arbitrarily selected explanation methods. To address these challenges and aid
user study design, we introduce Use-Case-Grounded Simulated Evaluations
(SimEvals). SimEvals involve training algorithmic agents that take as input the
information content (such as model explanations) that would be presented to
each participant in a human subject study, to predict answers to the use case
of interest. The algorithmic agent's test set accuracy provides a measure of
the predictiveness of the information content for the downstream use case. We
run a comprehensive evaluation on three real-world use cases (forward
simulation, model debugging, and counterfactual reasoning) to demonstrate that
Simevals can effectively identify which explanation methods will help humans
for each use case. These results provide evidence that SimEvals can be used to
efficiently screen an important set of user study design decisions, e.g.
selecting which explanations should be presented to the user, before running a
potentially costly user study. | [
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] |
Differentiable physics enables efficient gradient-based optimizations of
neural network (NN) controllers. However, existing work typically only delivers
NN controllers with limited capability and generalizability. We present a
practical learning framework that outputs unified NN controllers capable of
tasks with significantly improved complexity and diversity. To systematically
improve training robustness and efficiency, we investigated a suite of
improvements over the baseline approach, including periodic activation
functions, and tailored loss functions. In addition, we find our adoption of
batching and an Adam optimizer effective in training complex locomotion tasks.
We evaluate our framework on differentiable mass-spring and material point
method (MPM) simulations, with challenging locomotion tasks and multiple robot
designs. Experiments show that our learning framework, based on differentiable
physics, delivers better results than reinforcement learning and converges much
faster. We demonstrate that users can interactively control soft robot
locomotion and switch among multiple goals with specified velocity, height, and
direction instructions using a unified NN controller trained in our system.
Code is available at
https://github.com/erizmr/Complex-locomotion-skill-learning-via-differentiable-physics. | [
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] |
Variational autoencoders (VAEs) are a popular framework for modeling complex
data distributions; they can be efficiently trained via variational inference
by maximizing the evidence lower bound (ELBO), at the expense of a gap to the
exact (log-)marginal likelihood. While VAEs are commonly used for
representation learning, it is unclear why ELBO maximization would yield useful
representations, since unregularized maximum likelihood estimation cannot
invert the data-generating process. Yet, VAEs often succeed at this task. We
seek to elucidate this apparent paradox by studying nonlinear VAEs in the limit
of near-deterministic decoders. We first prove that, in this regime, the
optimal encoder approximately inverts the decoder -- a commonly used but
unproven conjecture -- which we refer to as {\em self-consistency}. Leveraging
self-consistency, we show that the ELBO converges to a regularized
log-likelihood. This allows VAEs to perform what has recently been termed
independent mechanism analysis (IMA): it adds an inductive bias towards
decoders with column-orthogonal Jacobians, which helps recovering the true
latent factors. The gap between ELBO and log-likelihood is therefore welcome,
since it bears unanticipated benefits for nonlinear representation learning. In
experiments on synthetic and image data, we show that VAEs uncover the true
latent factors when the data generating process satisfies the IMA assumption. | [
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] |
In order to enhance levels of engagement with conversational systems, our
long term research goal seeks to monitor the confusion state of a user and
adapt dialogue policies in response to such user confusion states. To this end,
in this paper, we present our initial research centred on a user-avatar
dialogue scenario that we have developed to study the manifestation of
confusion and in the long term its mitigation. We present a new definition of
confusion that is particularly tailored to the requirements of intelligent
conversational system development for task-oriented dialogue. We also present
the details of our Wizard-of-Oz based data collection scenario wherein users
interacted with a conversational avatar and were presented with stimuli that
were in some cases designed to invoke a confused state in the user. Post study
analysis of this data is also presented. Here, three pre-trained deep learning
models were deployed to estimate base emotion, head pose and eye gaze. Despite
a small pilot study group, our analysis demonstrates a significant relationship
between these indicators and confusion states. We understand this as a useful
step forward in the automated analysis of the pragmatics of dialogue. | [
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] |
Column Generation (CG) is an iterative algorithm for solving linear programs
(LPs) with an extremely large number of variables (columns). CG is the
workhorse for tackling large-scale \textit{integer} linear programs, which rely
on CG to solve LP relaxations within a branch and price algorithm. Two
canonical applications are the Cutting Stock Problem (CSP) and Vehicle Routing
Problem with Time Windows (VRPTW). In VRPTW, for example, each binary variable
represents the decision to include or exclude a \textit{route}, of which there
are exponentially many; CG incrementally grows the subset of columns being
used, ultimately converging to an optimal solution. We propose RLCG, the first
Reinforcement Learning (RL) approach for CG. Unlike typical column selection
rules which myopically select a column based on local information at each
iteration, we treat CG as a sequential decision-making problem: the column
selected in a given iteration affects subsequent column selections. This
perspective lends itself to a Deep Reinforcement Learning approach that uses
Graph Neural Networks (GNNs) to represent the variable-constraint structure in
the LP of interest. We perform an extensive set of experiments using the
publicly available BPPLIB benchmark for CSP and Solomon benchmark for VRPTW.
RLCG converges faster and reduces the number of CG iterations by 22.4\% for CSP
and 40.9\% for VRPTW on average compared to a commonly used greedy policy. Our
code is available at
https://github.com/chichengmessi/reinforcement-learning-for-column-generation.git. | [
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] |
Modeling time-evolving preferences of users with their sequential item
interactions, has attracted increasing attention in many online applications.
Hence, sequential recommender systems have been developed to learn the dynamic
user interests from the historical interactions for suggesting items. However,
the interaction pattern encoding functions in most existing sequential
recommender systems have focused on single type of user-item interactions. In
many real-life online platforms, user-item interactive behaviors are often
multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type
behavior inter-dependencies. Learning from informative representations of users
and items based on their multi-typed interaction data, is of great importance
to accurately characterize the time-evolving user preference. In this work, we
tackle the dynamic user-item relation learning with the awareness of
multi-behavior interactive patterns. Towards this end, we propose a new
Temporal Graph Transformer (TGT) recommendation framework to jointly capture
dynamic short-term and long-range user-item interactive patterns, by exploring
the evolving correlations across different types of behaviors. The new TGT
method endows the sequential recommendation architecture to distill dedicated
knowledge for type-specific behavior relational context and the implicit
behavior dependencies. Experiments on the real-world datasets indicate that our
method TGT consistently outperforms various state-of-the-art recommendation
methods. Our model implementation codes are available at
https://github.com/akaxlh/TGT. | [
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] |
In this paper, we introduce the first learning-based planner to drive a car
in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our
planner, DriveIRL, generates a diverse set of trajectory proposals, filters
these trajectories with a lightweight and interpretable safety filter, and then
uses a learned model to score each remaining trajectory. The best trajectory is
then tracked by the low-level controller of our self-driving vehicle. We train
our trajectory scoring model on a 500+ hour real-world dataset of expert
driving demonstrations in Las Vegas within the maximum entropy IRL framework.
DriveIRL's benefits include: a simple design due to only learning the
trajectory scoring function, relatively interpretable features, and strong
real-world performance. We validated DriveIRL on the Las Vegas Strip and
demonstrated fully autonomous driving in heavy traffic, including scenarios
involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff
zones. Our dataset will be made public to help further research in this area. | [
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] |
Designing accurate deep learning models for molecular property prediction
plays an increasingly essential role in drug and material discovery. Recently,
due to the scarcity of labeled molecules, self-supervised learning methods for
learning generalizable and transferable representations of molecular graphs
have attracted lots of attention. In this paper, we argue that there exist two
major issues hindering current self-supervised learning methods from obtaining
desired performance on molecular property prediction, that is, the ill-defined
pre-training tasks and the limited model capacity. To this end, we introduce
Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel
self-supervised learning framework for molecular graph representation learning,
to alleviate the aforementioned issues and improve the performance on the
downstream molecular property prediction tasks. More specifically, we first
introduce a high-capacity model, named Line Graph Transformer (LiGhT), which
emphasizes the importance of chemical bonds and is mainly designed to model the
structural information of molecular graphs. Then, a knowledge-guided
pre-training strategy is proposed to exploit the additional knowledge of
molecules to guide the model to capture the abundant structural and semantic
information from large-scale unlabeled molecular graphs. Extensive
computational tests demonstrated that KPGT can offer superior performance over
current state-of-the-art methods on several molecular property prediction
tasks. | [
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] |
Transfer learning improves the performance of the target task by leveraging
the data of a specific source task: the closer the relationship between the
source and the target tasks, the greater the performance improvement by
transfer learning. In neuroscience, the relationship between cognitive tasks is
usually represented by similarity of activated brain regions or neural
representation. However, no study has linked transfer learning and neuroscience
to reveal the relationship between cognitive tasks. In this study, we propose a
transfer learning framework to reflect the relationship between cognitive
tasks, and compare the task relations reflected by transfer learning and by the
overlaps of brain regions (e.g., neurosynth). Our results of transfer learning
create cognitive taskonomy to reflect the relationship between cognitive tasks
which is well in line with the task relations derived from neurosynth. Transfer
learning performs better in task decoding with fMRI data if the source and
target cognitive tasks activate similar brain regions. Our study uncovers the
relationship of multiple cognitive tasks and provides guidance for source task
selection in transfer learning for neural decoding based on small-sample data. | [
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Federated learning has been proposed as a privacy-preserving machine learning
framework that enables multiple clients to collaborate without sharing raw
data. However, client privacy protection is not guaranteed by design in this
framework. Prior work has shown that the gradient sharing strategies in
federated learning can be vulnerable to data reconstruction attacks. In
practice, though, clients may not transmit raw gradients considering the high
communication cost or due to privacy enhancement requirements. Empirical
studies have demonstrated that gradient obfuscation, including intentional
obfuscation via gradient noise injection and unintentional obfuscation via
gradient compression, can provide more privacy protection against
reconstruction attacks. In this work, we present a new data reconstruction
attack framework targeting the image classification task in federated learning.
We show that commonly adopted gradient postprocessing procedures, such as
gradient quantization, gradient sparsification, and gradient perturbation, may
give a false sense of security in federated learning. Contrary to prior
studies, we argue that privacy enhancement should not be treated as a byproduct
of gradient compression. Additionally, we design a new method under the
proposed framework to reconstruct the image at the semantic level. We quantify
the semantic privacy leakage and compare with conventional based on image
similarity scores. Our comparisons challenge the image data leakage evaluation
schemes in the literature. The results emphasize the importance of revisiting
and redesigning the privacy protection mechanisms for client data in existing
federated learning algorithms. | [
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] |
Intelligent agents need to select long sequences of actions to solve complex
tasks. While humans easily break down tasks into subgoals and reach them
through millions of muscle commands, current artificial intelligence is limited
to tasks with horizons of a few hundred decisions, despite large compute
budgets. Research on hierarchical reinforcement learning aims to overcome this
limitation but has proven to be challenging, current methods rely on manually
specified goal spaces or subtasks, and no general solution exists. We introduce
Director, a practical method for learning hierarchical behaviors directly from
pixels by planning inside the latent space of a learned world model. The
high-level policy maximizes task and exploration rewards by selecting latent
goals and the low-level policy learns to achieve the goals. Despite operating
in latent space, the decisions are interpretable because the world model can
decode goals into images for visualization. Director outperforms exploration
methods on tasks with sparse rewards, including 3D maze traversal with a
quadruped robot from an egocentric camera and proprioception, without access to
the global position or top-down view that was used by prior work. Director also
learns successful behaviors across a wide range of environments, including
visual control, Atari games, and DMLab levels. | [
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Recent techniques for approximating Nash equilibria in very large games
leverage neural networks to learn approximately optimal policies (strategies).
One promising line of research uses neural networks to approximate
counterfactual regret minimization (CFR) or its modern variants. DREAM, the
only current CFR-based neural method that is model free and therefore scalable
to very large games, trains a neural network on an estimated regret target that
can have extremely high variance due to an importance sampling term inherited
from Monte Carlo CFR (MCCFR). In this paper we propose an unbiased model-free
method that does not require any importance sampling. Our method, ESCHER, is
principled and is guaranteed to converge to an approximate Nash equilibrium
with high probability. We show that the variance of the estimated regret of
ESCHER is orders of magnitude lower than DREAM and other baselines. We then
show that ESCHER outperforms the prior state of the art -- DREAM and neural
fictitious self play (NFSP) -- on a number of games and the difference becomes
dramatic as game size increases. In the very large game of dark chess, ESCHER
is able to beat DREAM and NFSP in a head-to-head competition over $90\%$ of the
time. | [
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Collaborative filtering algorithms capture underlying consumption patterns,
including the ones specific to particular demographics or protected information
of users, e.g. gender, race, and location. These encoded biases can influence
the decision of a recommendation system (RS) towards further separation of the
contents provided to various demographic subgroups, and raise privacy concerns
regarding the disclosure of users' protected attributes. In this work, we
investigate the possibility and challenges of removing specific protected
information of users from the learned interaction representations of a RS
algorithm, while maintaining its effectiveness. Specifically, we incorporate
adversarial training into the state-of-the-art MultVAE architecture, resulting
in a novel model, Adversarial Variational Auto-Encoder with Multinomial
Likelihood (Adv-MultVAE), which aims at removing the implicit information of
protected attributes while preserving recommendation performance. We conduct
experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the
effectiveness of the bias mitigation method based on the inability of external
attackers in revealing the users' gender information from the model. Comparing
with baseline MultVAE, the results show that Adv-MultVAE, with marginal
deterioration in performance (w.r.t. NDCG and recall), largely mitigates
inherent biases in the model on both datasets. | [
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] |
Hardware-based acceleration is an extensive attempt to facilitate many
computationally-intensive mathematics operations. This paper proposes an
FPGA-based architecture to accelerate the convolution operation - a complex and
expensive computing step that appears in many Convolutional Neural Network
models. We target the design to the standard convolution operation, intending
to launch the product as an edge-AI solution. The project's purpose is to
produce an FPGA IP core that can process a convolutional layer at a time.
System developers can deploy the IP core with various FPGA families by using
Verilog HDL as the primary design language for the architecture. The
experimental results show that our single computing core synthesized on a
simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully
utilized, 4.48 GOPS can be achieved. | [
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Black-box nature of Artificial Intelligence (AI) models do not allow users to
comprehend and sometimes trust the output created by such model. In AI
applications, where not only the results but also the decision paths to the
results are critical, such black-box AI models are not sufficient. Explainable
Artificial Intelligence (XAI) addresses this problem and defines a set of AI
models that are interpretable by the users. Recently, several number of XAI
models have been to address the issues surrounding by lack of interpretability
and explainability of black-box models in various application areas such as
healthcare, military, energy, financial and industrial domains. Although the
concept of XAI has gained great deal of attention recently, its integration
into the IoT domain has not yet been fully defined. In this paper, we provide
an in-depth and systematic review of recent studies using XAI models in the
scope of IoT domain. We categorize the studies according to their methodology
and applications areas. In addition, we aim to focus on the challenging
problems and open issues and give future directions to guide the developers and
researchers for prospective future investigations. | [
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Node classification and graph classification are two graph learning problems
that predict the class label of a node and the class label of a graph
respectively. A node of a graph usually represents a real-world entity, e.g., a
user in a social network, or a document in a document citation network. In this
work, we consider a more challenging but practically useful setting, in which a
node itself is a graph instance. This leads to a hierarchical graph perspective
which arises in many domains such as social network, biological network and
document collection. We study the node classification problem in the
hierarchical graph where a 'node' is a graph instance. As labels are usually
limited, we design a novel semi-supervised solution named SEAL-CI. SEAL-CI
adopts an iterative framework that takes turns to update two modules, one
working at the graph instance level and the other at the hierarchical graph
level. To enforce a consistency among different levels of hierarchical graph,
we propose the Hierarchical Graph Mutual Information (HGMI) and further present
a way to compute HGMI with theoretical guarantee. We demonstrate the
effectiveness of this hierarchical graph modeling and the proposed SEAL-CI
method on text and social network data. | [
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] |
Membership inference attacks (MIAs) against machine learning models can lead
to serious privacy risks for the training dataset used in the model training.
In this paper, we propose a novel and effective Neuron-Guided Defense method
named NeuGuard against membership inference attacks (MIAs). We identify a key
weakness in existing defense mechanisms against MIAs wherein they cannot
simultaneously defend against two commonly used neural network based MIAs,
indicating that these two attacks should be separately evaluated to assure the
defense effectiveness. We propose NeuGuard, a new defense approach that jointly
controls the output and inner neurons' activation with the object to guide the
model output of training set and testing set to have close distributions.
NeuGuard consists of class-wise variance minimization targeting restricting the
final output neurons and layer-wise balanced output control aiming to constrain
the inner neurons in each layer. We evaluate NeuGuard and compare it with
state-of-the-art defenses against two neural network based MIAs, five strongest
metric based MIAs including the newly proposed label-only MIA on three
benchmark datasets. Results show that NeuGuard outperforms the state-of-the-art
defenses by offering much improved utility-privacy trade-off, generality, and
overhead. | [
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] |
An in-depth understanding of uncertainty is the first step to making
effective decisions under uncertainty. Deep/machine learning (ML/DL) has been
hugely leveraged to solve complex problems involved with processing
high-dimensional data. However, reasoning and quantifying different types of
uncertainties to achieve effective decision-making have been much less explored
in ML/DL than in other Artificial Intelligence (AI) domains. In particular,
belief/evidence theories have been studied in KRR since the 1960s to reason and
measure uncertainties to enhance decision-making effectiveness. We found that
only a few studies have leveraged the mature uncertainty research in
belief/evidence theories in ML/DL to tackle complex problems under different
types of uncertainty. In this survey paper, we discuss several popular belief
theories and their core ideas dealing with uncertainty causes and types and
quantifying them, along with the discussions of their applicability in ML/DL.
In addition, we discuss three main approaches that leverage belief theories in
Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough
DNNs, in terms of their uncertainty causes, types, and quantification methods
along with their applicability in diverse problem domains. Based on our
in-depth survey, we discuss insights, lessons learned, limitations of the
current state-of-the-art bridging belief theories and ML/DL, and finally,
future research directions. | [
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Artificial intelligence (AI) has the potential to greatly improve society,
but as with any powerful technology, it comes with heightened risks and
responsibilities. Current AI research lacks a systematic discussion of how to
manage long-tail risks from AI systems, including speculative long-term risks.
Keeping in mind the potential benefits of AI, there is some concern that
building ever more intelligent and powerful AI systems could eventually result
in systems that are more powerful than us; some say this is like playing with
fire and speculate that this could create existential risks (x-risks). To add
precision and ground these discussions, we provide a guide for how to analyze
AI x-risk, which consists of three parts: First, we review how systems can be
made safer today, drawing on time-tested concepts from hazard analysis and
systems safety that have been designed to steer large processes in safer
directions. Next, we discuss strategies for having long-term impacts on the
safety of future systems. Finally, we discuss a crucial concept in making AI
systems safer by improving the balance between safety and general capabilities.
We hope this document and the presented concepts and tools serve as a useful
guide for understanding how to analyze AI x-risk. | [
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] |
In order to develop effective sequential recommenders, a series of sequence
representation learning (SRL) methods are proposed to model historical user
behaviors. Most existing SRL methods rely on explicit item IDs for developing
the sequence models to better capture user preference. Though effective to some
extent, these methods are difficult to be transferred to new recommendation
scenarios, due to the limitation by explicitly modeling item IDs. To tackle
this issue, we present a novel universal sequence representation learning
approach, named UniSRec. The proposed approach utilizes the associated
description text of items to learn transferable representations across
different recommendation scenarios. For learning universal item
representations, we design a lightweight item encoding architecture based on
parametric whitening and mixture-of-experts enhanced adaptor. For learning
universal sequence representations, we introduce two contrastive pre-training
tasks by sampling multi-domain negatives. With the pre-trained universal
sequence representation model, our approach can be effectively transferred to
new recommendation domains or platforms in a parameter-efficient way, under
either inductive or transductive settings. Extensive experiments conducted on
real-world datasets demonstrate the effectiveness of the proposed approach.
Especially, our approach also leads to a performance improvement in a
cross-platform setting, showing the strong transferability of the proposed
universal SRL method. The code and pre-trained model are available at:
https://github.com/RUCAIBox/UniSRec. | [
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] |
To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G
wireless networks, conventional medium access control (MAC) procedures need to
evolve to enable base stations (BSs) and user equipments (UEs) to automatically
learn innovative MAC protocols catering to extremely diverse services. This
topic has received significant attention, and several reinforcement learning
(RL) algorithms, in which BSs and UEs are cast as agents, are available with
the aim of learning a communication policy based on agents' local observations.
However, current approaches are typically overfitted to the environment they
are trained in, and lack robustness against unseen conditions, failing to
generalize in different environments. To overcome this problem, in this work,
instead of learning a policy in the high dimensional and redundant observation
space, we leverage the concept of observation abstraction (OA) rooted in
extracting useful information from the environment. This in turn allows
learning communication protocols that are more robust and with much better
generalization capabilities than current baselines. To learn the abstracted
information from observations, we propose an architecture based on autoencoder
(AE) and imbue it into a multi-agent proximal policy optimization (MAPPO)
framework. Simulation results corroborate the effectiveness of leveraging
abstraction when learning protocols by generalizing across environments, in
terms of number of UEs, number of data packets to transmit, and channel
conditions. | [
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] |
It is crucial to consider the social and ethical consequences of AI and ML
based decisions for the safe and acceptable use of these emerging technologies.
Fairness, in particular, guarantees that the ML decisions do not result in
discrimination against individuals or minorities. Identifying and measuring
reliably fairness/discrimination is better achieved using causality which
considers the causal relation, beyond mere association, between the sensitive
attribute (e.g. gender, race, religion, etc.) and the decision (e.g. job
hiring, loan granting, etc.). The big impediment to the use of causality to
address fairness, however, is the unavailability of the causal model (typically
represented as a causal graph). Existing causal approaches to fairness in the
literature do not address this problem and assume that the causal model is
available. In this paper, we do not make such assumption and we review the
major algorithms to discover causal relations from observable data. This study
focuses on causal discovery and its impact on fairness. In particular, we show
how different causal discovery approaches may result in different causal models
and, most importantly, how even slight differences between causal models can
have significant impact on fairness/discrimination conclusions. These results
are consolidated by empirical analysis using synthetic and standard fairness
benchmark datasets. The main goal of this study is to highlight the importance
of the causal discovery step to appropriately address fairness using causality. | [
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] |
Data-driven models (DDM) based on machine learning and other AI techniques
play an important role in the perception of increasingly autonomous systems.
Due to the merely implicit definition of their behavior mainly based on the
data used for training, DDM outputs are subject to uncertainty. This poses a
challenge with respect to the realization of safety-critical perception tasks
by means of DDMs. A promising approach to tackling this challenge is to
estimate the uncertainty in the current situation during operation and adapt
the system behavior accordingly. In previous work, we focused on runtime
estimation of uncertainty and discussed approaches for handling uncertainty
estimations. In this paper, we present additional architectural patterns for
handling uncertainty. Furthermore, we evaluate the four patterns qualitatively
and quantitatively with respect to safety and performance gains. For the
quantitative evaluation, we consider a distance controller for vehicle
platooning where performance gains are measured by considering how much the
distance can be reduced in different operational situations. We conclude that
the consideration of context information of the driving situation makes it
possible to accept more or less uncertainty depending on the inherent risk of
the situation, which results in performance gains. | [
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] |
Recently, a considerable amount of works have been made to tackle the
communication burden in federated learning (FL) (e.g., model quantization, data
sparsification, and model compression). However, the existing methods, that
boost the communication efficiency in FL, result in a considerable trade-off
between communication efficiency and global convergence rate. We formulate an
optimization problem for compression-aided FL, which captures the relationship
between the distortion rate, number of participating IoT devices, and
convergence rate. Following that, the objective function is to minimize the
total transmission time for FL convergence. Because the problem is non-convex,
we propose to decompose it into sub-problems. Based on the property of a FL
model, we first determine the number of IoT devices participating in the FL
process. Then, the communication between IoT devices and the server is
optimized by efficiently allocating wireless resources based on a coalition
game. Our theoretical analysis shows that, by actively controlling the number
of participating IoT devices, we can avoid the training divergence of
compression-aided FL while maintaining the communication efficiency. | [
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] |
Deep Reinforcement Learning (DRL) is regarded as a potential method for
car-following control and has been mostly studied to support a single following
vehicle. However, it is more challenging to learn a stable and efficient
car-following policy when there are multiple following vehicles in a platoon,
especially with unpredictable leading vehicle behavior. In this context, we
adopt an integrated DRL and Dynamic Programming (DP) approach to learn
autonomous platoon control policies, which embeds the Deep Deterministic Policy
Gradient (DDPG) algorithm into a finite-horizon value iteration framework.
Although the DP framework can improve the stability and performance of DDPG, it
has the limitations of lower sampling and training efficiency. In this paper,
we propose an algorithm, namely Finite-Horizon-DDPG with Sweeping through
reduced state space using Stationary approximation (FH-DDPG-SS), which uses
three key ideas to overcome the above limitations, i.e., transferring network
weights backward in time, stationary policy approximation for earlier time
steps, and sweeping through reduced state space. In order to verify the
effectiveness of FH-DDPG-SS, simulation using real driving data is performed,
where the performance of FH-DDPG-SS is compared with those of the benchmark
algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are
demonstrated. | [
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Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for
source code yielded strong results on several tasks in the past few years,
including code generation and translation. These models have adopted varying
pre-training objectives to learn statistics of code construction from very
large-scale corpora in a self-supervised fashion; the success of pre-trained
models largely hinges on these pre-training objectives. This paper proposes a
new pre-training objective, "Naturalizing" of source code, exploiting code's
bimodal, dual-channel (formal & natural channels) nature. Unlike natural
language, code's bimodal, dual-channel nature allows us to generate
semantically equivalent code at scale. We introduce six classes of semantic
preserving transformations to introduce un-natural forms of code, and then
force our model to produce more natural original programs written by
developers. Learning to generate equivalent, but more natural code, at scale,
over large corpora of open-source code, without explicit manual supervision,
helps the model learn to both ingest & generate code. We fine-tune our model in
three generative Software Engineering tasks: code generation, code translation,
and code refinement with limited human-curated labeled data and achieve
state-of-the-art performance rivaling CodeT5. We show that our pre-trained
model is especially competitive at zero-shot and few-shot learning, and better
at learning code properties (e.g., syntax, data flow). | [
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] |
This paper systematically investigates the effectiveness of various
augmentations for contrastive self-supervised learning of electrocardiogram
(ECG) signals and identifies the best parameters. The baseline of our proposed
self-supervised framework consists of two main parts: the contrastive learning
and the downstream task. In the first stage, we train an encoder using a number
of augmentations to extract generalizable ECG signal representations. We then
freeze the encoder and finetune a few linear layers with different amounts of
labelled data for downstream arrhythmia detection. We then experiment with
various augmentations techniques and explore a range of parameters. Our
experiments are done on PTB-XL, a large and publicly available 12-lead ECG
dataset. The results show that applying augmentations in a specific range of
complexities works better for self-supervised contrastive learning. For
instance, when adding Gaussian noise, a sigma in the range of 0.1 to 0.2
achieves better results, while poor training occurs when the added noise is too
small or too large (outside of the specified range). A similar trend is
observed with other augmentations, demonstrating the importance of selecting
the optimum level of difficulty for the added augmentations, as augmentations
that are too simple will not result in effective training, while augmentations
that are too difficult will also prevent the model from effective learning of
generalized representations. Our work can influence future research on
self-supervised contrastive learning on bio-signals and aid in selecting
optimum parameters for different augmentations. | [
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] |
Machine learning (ML) models are increasingly being used in application
domains that often involve working together with human experts. In this
context, it can be advantageous to defer certain instances to a single human
expert when they are difficult to predict for the ML model. While previous work
has focused on scenarios with one distinct human expert, in many real-world
situations several human experts with varying capabilities may be available. In
this work, we propose an approach that trains a classification model to
complement the capabilities of multiple human experts. By jointly training the
classifier together with an allocation system, the classifier learns to
accurately predict those instances that are difficult for the human experts,
while the allocation system learns to pass each instance to the most suitable
team member -- either the classifier or one of the human experts. We evaluate
our proposed approach in multiple experiments on public datasets with
"synthetic" experts and a real-world medical dataset annotated by multiple
radiologists. Our approach outperforms prior work and is more accurate than the
best human expert or a classifier. Furthermore, it is flexibly adaptable to
teams of varying sizes and different levels of expert diversity. | [
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] |
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects. | [
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] |
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging
yet challenging task that simultaneously considers the shared-account and
cross-domain characteristics in the sequential recommendation. Existing works
on SCSR are mainly based on Recurrent Neural Network (RNN) and Graph Neural
Network (GNN) but they ignore the fact that although multiple users share a
single account, it is mainly occupied by one user at a time. This observation
motivates us to learn a more accurate user-specific account representation by
attentively focusing on its recent behaviors. Furthermore, though existing
works endow lower weights to irrelevant interactions, they may still dilute the
domain information and impede the cross-domain recommendation. To address the
above issues, we propose a reinforcement learning-based solution, namely
RL-ISN, which consists of a basic cross-domain recommender and a reinforcement
learning-based domain filter. Specifically, to model the account representation
in the shared-account scenario, the basic recommender first clusters users'
mixed behaviors as latent users, and then leverages an attention model over
them to conduct user identification. To reduce the impact of irrelevant domain
information, we formulate the domain filter as a hierarchical reinforcement
learning task, where a high-level task is utilized to decide whether to revise
the whole transferred sequence or not, and if it does, a low-level task is
further performed to determine whether to remove each interaction within it or
not. To evaluate the performance of our solution, we conduct extensive
experiments on two real-world datasets, and the experimental results
demonstrate the superiority of our RL-ISN method compared with the
state-of-the-art recommendation methods. | [
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] |
Many neural network-based out-of-distribution (OoD) detection methods have
been proposed. However, they require many training data for each target task.
We propose a simple yet effective meta-learning method to detect OoD with small
in-distribution data in a target task. With the proposed method, the OoD
detection is performed by density estimation in a latent space. A neural
network shared among all tasks is used to flexibly map instances in the
original space to the latent space. The neural network is meta-learned such
that the expected OoD detection performance is improved by using various tasks
that are different from the target tasks. This meta-learning procedure enables
us to obtain appropriate representations in the latent space for OoD detection.
For density estimation, we use a Gaussian mixture model (GMM) with full
covariance for each class. We can adapt the GMM parameters to in-distribution
data in each task in a closed form by maximizing the likelihood. Since the
closed form solution is differentiable, we can meta-learn the neural network
efficiently with a stochastic gradient descent method by incorporating the
solution into the meta-learning objective function. In experiments using six
datasets, we demonstrate that the proposed method achieves better performance
than existing meta-learning and OoD detection methods. | [
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] |
The rise of AI methods to make predictions and decisions has led to a
pressing need for more explainable artificial intelligence (XAI) methods. One
common approach for XAI is to produce a post-hoc explanation, explaining why a
black box ML model made a certain prediction. Formal approaches to post-hoc
explanations provide succinct reasons for why a prediction was made, as well as
why not another prediction was made. But these approaches assume that features
are independent and uniformly distributed. While this means that "why"
explanations are correct, they may be longer than required. It also means the
"why not" explanations may be suspect as the counterexamples they rely on may
not be meaningful. In this paper, we show how one can apply background
knowledge to give more succinct "why" formal explanations, that are presumably
easier to interpret by humans, and give more accurate "why not" explanations.
In addition, we show how to use existing rule induction techniques to
efficiently extract background information from a dataset, and also how to
report which background information was used to make an explanation, allowing a
human to examine it if they doubt the correctness of the explanation. | [
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] |
In the last decade, reinforcement learning successfully solved complex
control tasks and decision-making problems, like the Go board game. Yet, there
are few success stories when it comes to deploying those algorithms to
real-world scenarios. One of the reasons is the lack of guarantees when dealing
with and avoiding unsafe states, a fundamental requirement in critical control
engineering systems. In this paper, we introduce Guided Safe Shooting (GuSS), a
model-based RL approach that can learn to control systems with minimal
violations of the safety constraints. The model is learned on the data
collected during the operation of the system in an iterated batch fashion, and
is then used to plan for the best action to perform at each time step. We
propose three different safe planners, one based on a simple random shooting
strategy and two based on MAP-Elites, a more advanced divergent-search
algorithm. Experiments show that these planners help the learning agent avoid
unsafe situations while maximally exploring the state space, a necessary aspect
when learning an accurate model of the system. Furthermore, compared to
model-free approaches, learning a model allows GuSS reducing the number of
interactions with the real-system while still reaching high rewards, a
fundamental requirement when handling engineering systems. | [
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