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Convolutional Neural Networks (CNNs) are widely used in fault diagnosis of
mechanical systems due to their powerful feature extraction and classification
capabilities. However, the CNN is a typical black-box model, and the mechanism
of CNN's decision-making are not clear, which limits its application in
high-reliability-required fault diagnosis scenarios. To tackle this issue, we
propose a novel interpretable neural network termed as Time-Frequency Network
(TFN), where the physically meaningful time-frequency transform (TFT) method is
embedded into the traditional convolutional layer as an adaptive preprocessing
layer. This preprocessing layer named as time-frequency convolutional (TFconv)
layer, is constrained by a well-designed kernel function to extract
fault-related time-frequency information. It not only improves the diagnostic
performance but also reveals the logical foundation of the CNN prediction in
the frequency domain. Different TFT methods correspond to different kernel
functions of the TFconv layer. In this study, four typical TFT methods are
considered to formulate the TFNs and their effectiveness and interpretability
are proved through three mechanical fault diagnosis experiments. Experimental
results also show that the proposed TFconv layer can be easily generalized to
other CNNs with different depths. The code of TFN is available on
https://github.com/ChenQian0618/TFN. | [
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] |
In this work, we introduce a learning model designed to meet the needs of
applications in which computational resources are limited, and robustness and
interpretability are prioritized. Learning problems can be formulated as
constrained stochastic optimization problems, with the constraints originating
mainly from model assumptions that define a trade-off between complexity and
performance. This trade-off is closely related to over-fitting, generalization
capacity, and robustness to noise and adversarial attacks, and depends on both
the structure and complexity of the model, as well as the properties of the
optimization methods used. We develop an online prototype-based learning
algorithm based on annealing optimization that is formulated as an online
gradient-free stochastic approximation algorithm. The learning model can be
viewed as an interpretable and progressively growing competitive-learning
neural network model to be used for supervised, unsupervised, and reinforcement
learning. The annealing nature of the algorithm contributes to minimal
hyper-parameter tuning requirements, poor local minima prevention, and
robustness with respect to the initial conditions. At the same time, it
provides online control over the performance-complexity trade-off by
progressively increasing the complexity of the learning model as needed,
through an intuitive bifurcation phenomenon. Finally, the use of stochastic
approximation enables the study of the convergence of the learning algorithm
through mathematical tools from dynamical systems and control, and allows for
its integration with reinforcement learning algorithms, constructing an
adaptive state-action aggregation scheme. | [
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] |
The spread of COVID-19 leads to the global shutdown of many corporate
offices, and encourages companies to open more opportunities that allow
employees to work from a remote location. As the workplace type expands from
onsite offices to remote areas, an emerging challenge for an online hiring
marketplace is how these remote opportunities and user intentions to work
remotely can be modeled and matched without prior information. Despite the
unprecedented amount of remote jobs posted amid COVID-19, there is no existing
approach that can be directly applied.
Introducing a brand new workplace type naturally leads to the cold-start
problem, which is particularly more common for less active job seekers. It is
challenging, if not impossible, to onboard a new workplace type for any
predictive model if existing information sources can provide little information
related to a new category of jobs, including data from resumes and job
descriptions. Hence, in this work, we aim to propose a principled approach that
jointly models the remoteness of job seekers and job opportunities with limited
information, which also suffices the needs of web-scale applications. Existing
research on the emerging type of remote workplace mainly focuses on qualitative
studies, and classic predictive modeling approaches are inapplicable
considering the problem of cold-start and information scarcity. We precisely
try to close this gap with a novel graph neural architecture. Extensive
experiments on large-scale data from real-world applications have been
conducted to validate the superiority of the proposed approach over competitive
baselines. The improvement may translate to more rapid onboarding of the new
workplace type that can benefit job seekers who are interested in working
remotely. | [
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] |
Predict+Optimize is a recently proposed framework which combines machine
learning and constrained optimization, tackling optimization problems that
contain parameters that are unknown at solving time. The goal is to predict the
unknown parameters and use the estimates to solve for an estimated optimal
solution to the optimization problem. However, all prior works have focused on
the case where unknown parameters appear only in the optimization objective and
not the constraints, for the simple reason that if the constraints were not
known exactly, the estimated optimal solution might not even be feasible under
the true parameters. The contributions of this paper are two-fold. First, we
propose a novel and practically relevant framework for the Predict+Optimize
setting, but with unknown parameters in both the objective and the constraints.
We introduce the notion of a correction function, and an additional penalty
term in the loss function, modelling practical scenarios where an estimated
optimal solution can be modified into a feasible solution after the true
parameters are revealed, but at an additional cost. Second, we propose a
corresponding algorithmic approach for our framework, which handles all packing
and covering linear programs. Our approach is inspired by the prior work of
Mandi and Guns, though with crucial modifications and re-derivations for our
very different setting. Experimentation demonstrates the superior empirical
performance of our method over classical approaches. | [
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] |
Non-intrusive load monitoring (NILM) aims at energy consumption and appliance
state information retrieval from aggregated consumption measurements, with the
help of signal processing and machine learning algorithms. Representation
learning with deep neural networks is successfully applied to several related
disciplines. The main advantage of representation learning lies in replacing an
expert-driven, hand-crafted feature extraction with hierarchical learning from
many representations in raw data format. In this paper, we show how the NILM
processing-chain can be improved, reduced in complexity and alternatively
designed with recent deep learning algorithms. On the basis of an event-based
appliance recognition approach, we evaluate seven different classification
models: a classical machine learning approach that is based on a hand-crafted
feature extraction, three different deep neural network architectures for
automated feature extraction on raw waveform data, as well as three baseline
approaches for raw data processing. We evaluate all approaches on two
large-scale energy consumption datasets with more than 50,000 events of 44
appliances. We show that with the use of deep learning, we are able to reach
and surpass the performance of the state-of-the-art classical machine learning
approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to
0.69 and 0.87 of the classical approach. | [
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] |
Although beam emittance is critical for the performance of high-brightness
accelerators, optimization is often time limited as emittance calculations,
commonly done via quadrupole scans, are typically slow. Such calculations are a
type of $\textit{multi-point query}$, i.e. each query requires multiple
secondary measurements. Traditional black-box optimizers such as Bayesian
optimization are slow and inefficient when dealing with such objectives as they
must acquire the full series of measurements, but return only the emittance,
with each query. We propose applying Bayesian Algorithm Execution (BAX) to
instead query and model individual beam-size measurements. BAX avoids the slow
multi-point query on the accelerator by acquiring points through a
$\textit{virtual objective}$, i.e. calculating the emittance objective from a
fast learned model rather than directly from the accelerator. Here, we use BAX
to minimize emittance at the Linac Coherent Light Source (LCLS) and the
Facility for Advanced Accelerator Experimental Tests II (FACET-II). In
simulation, BAX is 20$\times$ faster and more robust to noise compared to
existing methods. In live LCLS and FACET-II tests, BAX performed the first
automated emittance tuning, matching the hand-tuned emittance at FACET-II and
achieving a 24% lower emittance at LCLS. Our method represents a conceptual
shift for optimizing multi-point queries, and we anticipate that it can be
readily adapted to similar problems in particle accelerators and other
scientific instruments. | [
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] |
Designing profitable and reliable trading strategies is challenging in the
highly volatile cryptocurrency market. Existing works applied deep
reinforcement learning methods and optimistically reported increased profits in
backtesting, which may suffer from the false positive issue due to overfitting.
In this paper, we propose a practical approach to address backtest overfitting
for cryptocurrency trading using deep reinforcement learning. First, we
formulate the detection of backtest overfitting as a hypothesis test. Then, we
train the DRL agents, estimate the probability of overfitting, and reject the
overfitted agents, increasing the chance of good trading performance. Finally,
on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022
(during which the crypto market crashed two times), we show that the less
overfitted deep reinforcement learning agents have a higher return than that of
more overfitted agents, an equal weight strategy, and the S&P DBM Index (market
benchmark), offering confidence in possible deployment to a real market. | [
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] |
With the growing rates of cyber-attacks and cyber espionage, the need for
better and more powerful intrusion detection systems (IDS) is even more
warranted nowadays. The basic task of an IDS is to act as the first line of
defense, in detecting attacks on the internet. As intrusion tactics from
intruders become more sophisticated and difficult to detect, researchers have
started to apply novel Machine Learning (ML) techniques to effectively detect
intruders and hence preserve internet users' information and overall trust in
the entire internet network security. Over the last decade, there has been an
explosion of research on intrusion detection techniques based on ML and Deep
Learning (DL) architectures on various cyber security-based datasets such as
the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we
review contemporary literature and provide a comprehensive survey of different
types of intrusion detection technique that applies Support Vector Machines
(SVMs) algorithms as a classifier. We focus only on studies that have been
evaluated on the two most widely used datasets in cybersecurity namely: the
KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method,
identifying the role of the SVMs classifier, and all other algorithms involved
in the studies. Furthermore, we present a critical review of each method, in
tabular form, highlighting the performance measures, strengths, and limitations
of each of the methods surveyed. | [
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] |
Cross-dataset emotion recognition as an extremely challenging task in the
field of EEG-based affective computing is influenced by many factors, which
makes the universal models yield unsatisfactory results. Facing the situation
that lacks EEG information decoding research, we first analyzed the impact of
different EEG information(individual, session, emotion and trial) for emotion
recognition by sample space visualization, sample aggregation phenomena
quantification, and energy pattern analysis on five public datasets. Based on
these phenomena and patterns, we provided the processing methods and
interpretable work of various EEG differences. Through the analysis of
emotional feature distribution patterns, the Individual Emotional Feature
Distribution Difference(IEFDD) was found, which was also considered as the main
factor of the stability for emotion recognition. After analyzing the
limitations of traditional modeling approach suffering from IEFDD, the
Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably
characterize emotional feature distribution patterns, four weight extraction
methods were designed, and the optimal was the correction T-test(CT) weight
extraction method. Finally, the performance of WCMF was validated on
cross-dataset tasks in two kinds of experiments that simulated different
practical scenarios, and the results showed that WCMF had more stable and
better emotion recognition ability. | [
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] |
Deep learning techniques have been applied widely in industrial
recommendation systems. However, far less attention has been paid to the
overfitting problem of models in recommendation systems, which, on the
contrary, is recognized as a critical issue for deep neural networks. In the
context of Click-Through Rate (CTR) prediction, we observe an interesting
one-epoch overfitting problem: the model performance exhibits a dramatic
degradation at the beginning of the second epoch. Such a phenomenon has been
witnessed widely in real-world applications of CTR models. Thereby, the best
performance is usually achieved by training with only one epoch. To understand
the underlying factors behind the one-epoch phenomenon, we conduct extensive
experiments on the production data set collected from the display advertising
system of Alibaba. The results show that the model structure, the optimization
algorithm with a fast convergence rate, and the feature sparsity are closely
related to the one-epoch phenomenon. We also provide a likely hypothesis for
explaining such a phenomenon and conduct a set of proof-of-concept experiments.
We hope this work can shed light on future research on training more epochs for
better performance. | [
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] |
Research articles are being shared in increasing numbers on multiple online
platforms. Although the scholarly impact of these articles has been widely
studied, the online interest determined by how long the research articles are
shared online remains unclear. Being cognizant of how long a research article
is mentioned online could be valuable information to the researchers. In this
paper, we analyzed multiple social media platforms on which users share and/or
discuss scholarly articles. We built three clusters for papers, based on the
number of yearly online mentions having publication dates ranging from the year
1920 to 2016. Using the online social media metrics for each of these three
clusters, we built machine learning models to predict the long-term online
interest in research articles. We addressed the prediction task with two
different approaches: regression and classification. For the regression
approach, the Multi-Layer Perceptron model performed best, and for the
classification approach, the tree-based models performed better than other
models. We found that old articles are most evident in the contexts of
economics and industry (i.e., patents). In contrast, recently published
articles are most evident in research platforms (i.e., Mendeley) followed by
social media platforms (i.e., Twitter). | [
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] |
In recent years, reinforcement learning and its multi-agent analogue have
achieved great success in solving various complex control problems. However,
multi-agent reinforcement learning remains challenging both in its theoretical
analysis and empirical design of algorithms, especially for large swarms of
embodied robotic agents where a definitive toolchain remains part of active
research. We use emerging state-of-the-art mean-field control techniques in
order to convert many-agent swarm control into more classical single-agent
control of distributions. This allows profiting from advances in single-agent
reinforcement learning at the cost of assuming weak interaction between agents.
However, the mean-field model is violated by the nature of real systems with
embodied, physically colliding agents. Thus, we combine collision avoidance and
learning of mean-field control into a unified framework for tractably designing
intelligent robotic swarm behavior. On the theoretical side, we provide novel
approximation guarantees for general mean-field control both in continuous
spaces and with collision avoidance. On the practical side, we show that our
approach outperforms multi-agent reinforcement learning and allows for
decentralized open-loop application while avoiding collisions, both in
simulation and real UAV swarms. Overall, we propose a framework for the design
of swarm behavior that is both mathematically well-founded and practically
useful, enabling the solution of otherwise intractable swarm problems. | [
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] |
The robustness of signal temporal logic not only assesses whether a signal
adheres to a specification but also provides a measure of how much a formula is
fulfilled or violated. The calculation of robustness is based on evaluating the
robustness of underlying predicates. However, the robustness of predicates is
usually defined in a model-free way, i.e., without including the system
dynamics. Moreover, it is often nontrivial to define the robustness of
complicated predicates precisely. To address these issues, we propose a notion
of model predictive robustness, which provides a more systematic way of
evaluating robustness compared to previous approaches by considering
model-based predictions. In particular, we use Gaussian process regression to
learn the robustness based on precomputed predictions so that robustness values
can be efficiently computed online. We evaluate our approach for the use case
of autonomous driving with predicates used in formalized traffic rules on a
recorded dataset, which highlights the advantage of our approach compared to
traditional approaches in terms of precision. By incorporating our robustness
definitions into a trajectory planner, autonomous vehicles obey traffic rules
more robustly than human drivers in the dataset. | [
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] |
Hybrid modeling combining data-driven techniques and numerical methods is an
emerging and promising research direction for efficient climate simulation.
However, previous works lack practical platforms, making developing hybrid
modeling a challenging programming problem. Furthermore, the lack of standard
data sets and evaluation metrics may hamper researchers from comprehensively
comparing various algorithms under a uniform condition. To address these
problems, we propose a framework called NeuroClim for hybrid modeling under the
real-world scenario, a basic setting to simulate the real climate that we live
in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly
platform NeuroGCM for efficiently developing hybrid modeling in climate
simulation. (2) Dataset. We provide an open-source dataset for data-driven
methods in hybrid modeling. We investigate the characteristics of the data,
i.e., heterogeneity and stiffness, which reveals the difficulty of regressing
climate simulation data; (3) Metrics. We propose a methodology for
quantitatively evaluating hybrid modeling, including the approximation ability
of machine learning models and the stability during simulation. We believe that
NeuroClim allows researchers to work without high level of climate-related
expertise and focus only on machine learning algorithm design, which will
accelerate hybrid modeling research in the AI-Climate intersection. The codes
and data are released at https://github.com/x-w19/NeuroClim. | [
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] |
Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating
which form of KT extracts possesses AD properties similar to the standard AD
fluoxetine (flu) remained challenging. Here, we adopted an autoencoder
(AE)-based anomaly detector called ANet to measure the similarity of mice's
local field potential (LFP) features that responded to KT leave extracts and AD
flu. The features that responded to KT syrup had the highest similarity to
those that responded to the AD flu at 85.62 $\pm$ 0.29%. This finding presents
the higher feasibility of using KT syrup as an alternative substance for
depressant therapy than KT alkaloids and KT aqueous, which are the other
candidates in this study. Apart from the similarity measurement, we utilized
ANet as a multi-task AE and evaluated the performance in discriminating
multi-class LFP responses corresponding to the effect of different KT extracts
and AD flu simultaneously. Furthermore, we visualized learned latent features
among LFP responses qualitatively and quantitatively as t-SNE projection and
maximum mean discrepancy distance, respectively. The classification results
reported the accuracy and F1-score of 79.78 $\pm$ 0.39% and 79.53 $\pm$ 0.00%.
In summary, the outcomes of this research might help therapeutic design devices
for an alternative substance profile evaluation, such as Kratom-based form in
real-world applications. | [
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] |
In this paper, we provide a characterization of the expected value of
submodular set functions with pairwise independent random input. The set of
pairwise independent (uncorrelated) probability distributions contains the
mutually independent distribution and is contained within the set of
arbitrarily dependent (correlated) distributions. We study the ratio of the
maximum expected value of a function with arbitrary dependence among the random
input with given marginal probabilities to the maximum expected value of the
function with pairwise independent random input with the same marginal
probabilities. The definition of the ratio is inspired from the correlation gap
ratio of Agrawal et al. (2012) and Calinescu et al. (2007). Our results show
that for any monotone submodular set function defined on n variables, the ratio
is bounded from above by 4/3 in the following cases: (a) for small n
(specifically n = 2, 3) with general marginal probabilities, and (b) for
general n with small marginal probabilities. The bound is tight in cases (a)
and (b). This contrasts with the e/(e-1) bound on the correlation gap ratio for
monotone submodular set functions with mutually independent random input which
is known to be tight in case (b). Our results illustrate a fundamental
difference in the behavior of submodular functions with weaker notions of
independence. We discuss an application in distributionally robust optimization
and end the paper with a conjecture. | [
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] |
Both in academic and industry-based research, online evaluation methods are
seen as the golden standard for interactive applications like recommendation
systems. Naturally, the reason for this is that we can directly measure utility
metrics that rely on interventions, being the recommendations that are being
shown to users. Nevertheless, online evaluation methods are costly for a number
of reasons, and a clear need remains for reliable offline evaluation
procedures. In industry, offline metrics are often used as a first-line
evaluation to generate promising candidate models to evaluate online. In
academic work, limited access to online systems makes offline metrics the de
facto approach to validating novel methods. Two classes of offline metrics
exist: proxy-based methods, and counterfactual methods. The first class is
often poorly correlated with the online metrics we care about, and the latter
class only provides theoretical guarantees under assumptions that cannot be
fulfilled in real-world environments. Here, we make the case that
simulation-based comparisons provide ways forward beyond offline metrics, and
argue that they are a preferable means of evaluation. | [
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High-quality medical systematic reviews require comprehensive literature
searches to ensure the recommendations and outcomes are sufficiently reliable.
Indeed, searching for relevant medical literature is a key phase in
constructing systematic reviews and often involves domain (medical researchers)
and search (information specialists) experts in developing the search queries.
Queries in this context are highly complex, based on Boolean logic, include
free-text terms and index terms from standardised terminologies (e.g., the
Medical Subject Headings (MeSH) thesaurus), and are difficult and
time-consuming to build. The use of MeSH terms, in particular, has been shown
to improve the quality of the search results. However, identifying the correct
MeSH terms to include in a query is difficult: information experts are often
unfamiliar with the MeSH database and unsure about the appropriateness of MeSH
terms for a query. Naturally, the full value of the MeSH terminology is often
not fully exploited. This article investigates methods to suggest MeSH terms
based on an initial Boolean query that includes only free-text terms. In this
context, we devise lexical and pre-trained language models based methods. These
methods promise to automatically identify highly effective MeSH terms for
inclusion in a systematic review query. Our study contributes an empirical
evaluation of several MeSH term suggestion methods. We further contribute an
extensive analysis of MeSH term suggestions for each method and how these
suggestions impact the effectiveness of Boolean queries. | [
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] |
Most knowledge graphs (KGs) are incomplete, which motivates one important
research topic on automatically complementing knowledge graphs. However,
evaluation of knowledge graph completion (KGC) models often ignores the
incompleteness -- facts in the test set are ranked against all unknown triplets
which may contain a large number of missing facts not included in the KG yet.
Treating all unknown triplets as false is called the closed-world assumption.
This closed-world assumption might negatively affect the fairness and
consistency of the evaluation metrics. In this paper, we study KGC evaluation
under a more realistic setting, namely the open-world assumption, where unknown
triplets are considered to include many missing facts not included in the
training or test sets. For the currently most used metrics such as mean
reciprocal rank (MRR) and Hits@K, we point out that their behavior may be
unexpected under the open-world assumption. Specifically, with not many missing
facts, their numbers show a logarithmic trend with respect to the true strength
of the model, and thus, the metric increase could be insignificant in terms of
reflecting the true model improvement. Further, considering the variance, we
show that the degradation in the reported numbers may result in incorrect
comparisons between different models, where stronger models may have lower
metric numbers. We validate the phenomenon both theoretically and
experimentally. Finally, we suggest possible causes and solutions for this
problem. Our code and data are available at
https://github.com/GraphPKU/Open-World-KG . | [
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] |
Federated Learning (FL) is a machine learning paradigm that allows
decentralized clients to learn collaboratively without sharing their private
data. However, excessive computation and communication demands pose challenges
to current FL frameworks, especially when training large-scale models. To
prevent these issues from hindering the deployment of FL systems, we propose a
lightweight framework where clients jointly learn to fuse the representations
generated by multiple fixed pre-trained models rather than training a
large-scale model from scratch. This leads us to a more practical FL problem by
considering how to capture more client-specific and class-relevant information
from the pre-trained models and jointly improve each client's ability to
exploit those off-the-shelf models. In this work, we design a Federated
Prototype-wise Contrastive Learning (FedPCL) approach which shares knowledge
across clients through their class prototypes and builds client-specific
representations in a prototype-wise contrastive manner. Sharing prototypes
rather than learnable model parameters allows each client to fuse the
representations in a personalized way while keeping the shared knowledge in a
compact form for efficient communication. We perform a thorough evaluation of
the proposed FedPCL in the lightweight framework, measuring and visualizing its
ability to fuse various pre-trained models on popular FL datasets. | [
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] |
Link prediction (LP) has been recognized as an important task in graph
learning with its broad practical applications. A typical application of LP is
to retrieve the top scoring neighbors for a given source node, such as the
friend recommendation. These services desire the high inference scalability to
find the top scoring neighbors from many candidate nodes at low latencies.
There are two popular decoders that the recent LP models mainly use to compute
the edge scores from node embeddings: the HadamardMLP and Dot Product decoders.
After theoretical and empirical analysis, we find that the HadamardMLP decoders
are generally more effective for LP. However, HadamardMLP lacks the scalability
for retrieving top scoring neighbors on large graphs, since to the best of our
knowledge, there does not exist an algorithm to retrieve the top scoring
neighbors for HadamardMLP decoders in sublinear complexity. To make HadamardMLP
scalable, we propose the Flashlight algorithm to accelerate the top scoring
neighbor retrievals for HadamardMLP: a sublinear algorithm that progressively
applies approximate maximum inner product search (MIPS) techniques with
adaptively adjusted query embeddings. Empirical results show that Flashlight
improves the inference speed of LP by more than 100 times on the large
OGBL-CITATION2 dataset without sacrificing effectiveness. Our work paves the
way for large-scale LP applications with the effective HadamardMLP decoders by
greatly accelerating their inference. | [
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] |
As one of the most successful AI-powered applications, recommender systems
aim to help people make appropriate decisions in an effective and efficient
way, by providing personalized suggestions in many aspects of our lives,
especially for various human-oriented online services such as e-commerce
platforms and social media sites. In the past few decades, the rapid
developments of recommender systems have significantly benefited human by
creating economic value, saving time and effort, and promoting social good.
However, recent studies have found that data-driven recommender systems can
pose serious threats to users and society, such as spreading fake news to
manipulate public opinion in social media sites, amplifying unfairness toward
under-represented groups or individuals in job matching services, or inferring
privacy information from recommendation results. Therefore, systems'
trustworthiness has been attracting increasing attention from various aspects
for mitigating negative impacts caused by recommender systems, so as to enhance
the public's trust towards recommender systems techniques. In this survey, we
provide a comprehensive overview of Trustworthy Recommender systems (TRec) with
a specific focus on six of the most important aspects; namely, Safety &
Robustness, Nondiscrimination & Fairness, Explainability, Privacy,
Environmental Well-being, and Accountability & Auditability. For each aspect,
we summarize the recent related technologies and discuss potential research
directions to help achieve trustworthy recommender systems in the future. | [
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] |
Online social networks are actively involved in the removal of malicious
social bots due to their role in the spread of low quality information.
However, most of the existing bot detectors are supervised classifiers
incapable of capturing the evolving behavior of sophisticated bots. Here we
propose MulBot, an unsupervised bot detector based on multivariate time series
(MTS). For the first time, we exploit multidimensional temporal features
extracted from user timelines. We manage the multidimensionality with an LSTM
autoencoder, which projects the MTS in a suitable latent space. Then, we
perform a clustering step on this encoded representation to identify dense
groups of very similar users -- a known sign of automation. Finally, we perform
a binary classification task achieving f1-score $= 0.99$, outperforming
state-of-the-art methods (f1-score $\le 0.97$). Not only does MulBot achieve
excellent results in the binary classification task, but we also demonstrate
its strengths in a novel and practically-relevant task: detecting and
separating different botnets. In this multi-class classification task we
achieve f1-score $= 0.96$. We conclude by estimating the importance of the
different features used in our model and by evaluating MulBot's capability to
generalize to new unseen bots, thus proposing a solution to the generalization
deficiencies of supervised bot detectors. | [
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Software vulnerabilities (SVs) have become a common, serious and crucial
concern due to the ubiquity of computer software. Many machine learning-based
approaches have been proposed to solve the software vulnerability detection
(SVD) problem. However, there are still two open and significant issues for SVD
in terms of i) learning automatic representations to improve the predictive
performance of SVD, and ii) tackling the scarcity of labeled vulnerabilities
datasets that conventionally need laborious labeling effort by experts. In this
paper, we propose a novel end-to-end approach to tackle these two crucial
issues. We first exploit the automatic representation learning with deep domain
adaptation for software vulnerability detection. We then propose a novel
cross-domain kernel classifier leveraging the max-margin principle to
significantly improve the transfer learning process of software vulnerabilities
from labeled projects into unlabeled ones. The experimental results on
real-world software datasets show the superiority of our proposed method over
state-of-the-art baselines. In short, our method obtains a higher performance
on F1-measure, the most important measure in SVD, from 1.83% to 6.25% compared
to the second highest method in the used datasets. Our released source code
samples are publicly available at https://github.com/vannguyennd/dam2p | [
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] |
Software vulnerabilities existing in a program or function of computer
systems are a serious and crucial concern. Typically, in a program or function
consisting of hundreds or thousands of source code statements, there are only
few statements causing the corresponding vulnerabilities. Vulnerability
labeling is currently done on a function or program level by experts with the
assistance of machine learning tools. Extending this approach to the code
statement level is much more costly and time-consuming and remains an open
problem. In this paper we propose a novel end-to-end deep learning-based
approach to identify the vulnerability-relevant code statements of a specific
function. Inspired by the specific structures observed in real world vulnerable
code, we first leverage mutual information for learning a set of latent
variables representing the relevance of the source code statements to the
corresponding function's vulnerability. We then propose novel clustered spatial
contrastive learning in order to further improve the representation learning
and the robust selection process of vulnerability-relevant code statements.
Experimental results on real-world datasets of 200k+ C/C++ functions show the
superiority of our method over other state-of-the-art baselines. In general,
our method obtains a higher performance in VCP, VCA, and Top-10 ACC measures of
between 3\% to 14\% over the baselines when running on real-world datasets in
an unsupervised setting. Our released source code samples are publicly
available at
\href{https://github.com/vannguyennd/livuitcl}{https://github.com/vannguyennd/livuitcl.} | [
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] |
Despite decades of research, existing navigation systems still face
real-world challenges when deployed in the wild, e.g., in cluttered home
environments or in human-occupied public spaces. To address this, we present a
new class of implicit control policies combining the benefits of imitation
learning with the robust handling of system constraints from Model Predictive
Control (MPC). Our approach, called Performer-MPC, uses a learned cost function
parameterized by vision context embeddings provided by Performers -- a low-rank
implicit-attention Transformer. We jointly train the cost function and
construct the controller relying on it, effectively solving end-to-end the
corresponding bi-level optimization problem. We show that the resulting policy
improves standard MPC performance by leveraging a few expert demonstrations of
the desired navigation behavior in different challenging real-world scenarios.
Compared with a standard MPC policy, Performer-MPC achieves >40% better goal
reached in cluttered environments and >65% better on social metrics when
navigating around humans. | [
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] |
Convolution-augmented transformers (Conformers) are recently proposed in
various speech-domain applications, such as automatic speech recognition (ASR)
and speech separation, as they can capture both local and global dependencies.
In this paper, we propose a conformer-based metric generative adversarial
network (CMGAN) for speech enhancement (SE) in the time-frequency (TF) domain.
The generator encodes the magnitude and complex spectrogram information using
two-stage conformer blocks to model both time and frequency dependencies. The
decoder then decouples the estimation into a magnitude mask decoder branch to
filter out unwanted distortions and a complex refinement branch to further
improve the magnitude estimation and implicitly enhance the phase information.
Additionally, we include a metric discriminator to alleviate metric mismatch by
optimizing the generator with respect to a corresponding evaluation score.
Objective and subjective evaluations illustrate that CMGAN is able to show
superior performance compared to state-of-the-art methods in three speech
enhancement tasks (denoising, dereverberation and super-resolution). For
instance, quantitative denoising analysis on Voice Bank+DEMAND dataset
indicates that CMGAN outperforms various previous models with a margin, i.e.,
PESQ of 3.41 and SSNR of 11.10 dB. | [
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] |
Epilepsy is one of the most common neurological diseases, characterized by
transient and unprovoked events called epileptic seizures. Electroencephalogram
(EEG) is an auxiliary method used to perform both the diagnosis and the
monitoring of epilepsy. Given the unexpected nature of an epileptic seizure,
its prediction would improve patient care, optimizing the quality of life and
the treatment of epilepsy. Predicting an epileptic seizure implies the
identification of two distinct states of EEG in a patient with epilepsy: the
preictal and the interictal. In this paper, we developed two deep learning
models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer
(TMC-ViT), adaptations of Transformer-based architectures for multi-channel
temporal signals. Moreover, we accessed the impact of choosing different
preictal duration, since its length is not a consensus among experts, and also
evaluated how the sample size benefits each model. Our models are compared with
fully connected, convolutional, and recurrent networks. The algorithms were
patient-specific trained and evaluated on raw EEG signals from the CHB-MIT
database. Experimental results and statistical validation demonstrated that our
TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure
prediction. | [
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] |
The accuracy of recent deep learning based clinical decision support systems
is promising. However, lack of model interpretability remains an obstacle to
widespread adoption of artificial intelligence in healthcare. Using sleep as a
case study, we propose a generalizable method to combine clinical
interpretability with high accuracy derived from black-box deep learning.
Clinician-determined sleep stages from polysomnogram (PSG) remain the gold
standard for evaluating sleep quality. However, PSG manual annotation by
experts is expensive and time-prohibitive. We propose SERF, interpretable Sleep
staging using Embeddings, Rules, and Features to read PSG. SERF provides
interpretation of classified sleep stages through meaningful features derived
from the AASM Manual for the Scoring of Sleep and Associated Events. In SERF,
the embeddings obtained from a hybrid of convolutional and recurrent neural
networks are transposed to the interpretable feature space. These
representative interpretable features are used to train simple models like a
shallow decision tree for classification. Model results are validated on two
publicly available datasets. SERF surpasses the current state-of-the-art for
interpretable sleep staging by 2%. Using Gradient Boosted Trees as the
classifier, SERF obtains 0.766 $\kappa$ and 0.870 AUC-ROC, within 2% of the
current state-of-the-art black-box models. | [
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Session-based recommender systems (SBRSs) have shown superior performance
over conventional methods. However, they show limited scalability on
large-scale industrial datasets since most models learn one embedding per item.
This leads to a large memory requirement (of storing one vector per item) and
poor performance on sparse sessions with cold-start or unpopular items. Using
one public and one large industrial dataset, we experimentally show that
state-of-the-art SBRSs have low performance on sparse sessions with sparse
items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for
session-based recommendations. Our proposed method learns a transformation
function from item metadata to embeddings, and is thus, item-ID free (i.e.,
does not need to learn one embedding per item). It integrates item metadata to
learn shared representations of diverse item attributes. During inference, new
or unpopular items will be assigned identical representations for the
attributes they share with items previously observed during training, and thus
will have similar representations with those items, enabling recommendations of
even cold-start and sparse items. Additionally, M2TRec is trained in a
multi-task setting to predict the next item in the session along with its
primary category and subcategories. Our multi-task strategy makes the model
converge faster and significantly improves the overall performance.
Experimental results show significant performance gains using our proposed
approach on sparse items on the two datasets. | [
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] |
Machine programming (MP) is an emerging field at the intersection of
deterministic and probabilistic computing, and it aims to assist software and
hardware engineers, among other applications. Along with powerful compute
resources, MP systems often rely on vast amount of open-source code to learn
interesting properties about code and programming and solve problems in the
areas of debugging, code recommendation, auto-completion, etc. Unfortunately,
several of the existing MP systems either do not consider quality of code
repositories or use atypical quality measures than those typically used in
software engineering community to select them. As such, impact of quality of
code repositories on the performance of these systems needs to be studied.
In this preliminary paper, we evaluate impact of different quality
repositories on the performance of a candidate MP system. Towards that
objective, we develop a framework, named GitRank, to rank open-source
repositories on quality, maintainability, and popularity by leveraging existing
research on this topic. We then apply GitRank to evaluate correlation between
the quality measures used by the candidate MP system and the quality measures
used by our framework. Our preliminary results reveal some correlation between
the quality measures used in GitRank and ControlFlag's performance, suggesting
that some of the measures used in GitRank are applicable to ControlFlag. But it
also raises questions around right quality measures for code repositories used
in MP systems. We believe that our findings also generate interesting insights
towards code quality measures that affect performance of MP systems. | [
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] |
EEG signals are complex and low-frequency signals. Therefore, they are easily
influenced by external factors. EEG artifact removal is crucial in neuroscience
because artifacts have a significant impact on the results of EEG analysis. The
removal of ocular artifacts is the most challenging among these artifacts. In
this study, a novel ocular artifact removal method is presented by developing
bidirectional long-short term memory (BiLSTM)-based deep learning (DL) models.
We created a benchmarking dataset to train and test proposed DL models by
combining the EEGdenoiseNet and DEAP datasets. We also augmented the data by
contaminating ground-truth clean EEG signals with EOG at various SNR levels.
The BiLSTM network is then fed to features extracted from augmented signals
using highly-localized time-frequency (TF) coefficients obtained by wavelet
synchrosqueezed transform (WSST). We also compare the WSST-based DL model
results with traditional TF analysis (TFA) methods namely short-time Fourier
transformation (STFT) and continuous wavelet transform (CWT) as well as
augmented raw signals. The best average MSE value of 0.3066 was obtained by the
first time-proposed BiLSTM-based WSST-Net model. Our results demonstrated the
WSST-Net model significantly improves artifact removal performance compared to
traditional TF and raw signal methods. Also, the proposed EOG removal approach
reveals that it outperforms many conventional and DL-based ocular artifact
removal methods in the literature. | [
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In the past few years, Convolutional Neural Networks (CNN) have demonstrated
promising performance in various real-world cybersecurity applications, such as
network and multimedia security. However, the underlying fragility of CNN
structures poses major security problems, making them inappropriate for use in
security-oriented applications including such computer networks. Protecting
these architectures from adversarial attacks necessitates using security-wise
architectures that are challenging to attack.
In this study, we present a novel architecture based on an ensemble
classifier that combines the enhanced security of 1-Class classification (known
as 1C) with the high performance of conventional 2-Class classification (known
as 2C) in the absence of attacks.Our architecture is referred to as the
1.5-Class (SPRITZ-1.5C) classifier and constructed using a final dense
classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers
(i.e., auto-encoders). In our experiments, we evaluated the robustness of our
proposed architecture by considering eight possible adversarial attacks in
various scenarios. We performed these attacks on the 2C and SPRITZ-1.5C
architectures separately. The experimental results of our study showed that the
Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained
with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the
SPRITZ-1.5C classifier. | [
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] |
As recommender systems become increasingly central for sorting and
prioritizing the content available online, they have a growing impact on the
opportunities or revenue of their items producers. For instance, they influence
which recruiter a resume is recommended to, or to whom and how much a music
track, video or news article is being exposed. This calls for recommendation
approaches that not only maximize (a proxy of) user satisfaction, but also
consider some notion of fairness in the exposure of items or groups of items.
Formally, such recommendations are usually obtained by maximizing a concave
objective function in the space of randomized rankings. When the total exposure
of an item is defined as the sum of its exposure over users, the optimal
rankings of every users become coupled, which makes the optimization process
challenging. Existing approaches to find these rankings either solve the global
optimization problem in a batch setting, i.e., for all users at once, which
makes them inapplicable at scale, or are based on heuristics that have weak
theoretical guarantees. In this paper, we propose the first efficient online
algorithm to optimize concave objective functions in the space of rankings
which applies to every concave and smooth objective function, such as the ones
found for fairness of exposure. Based on online variants of the Frank-Wolfe
algorithm, we show that our algorithm is computationally fast, generating
rankings on-the-fly with computation cost dominated by the sort operation,
memory efficient, and has strong theoretical guarantees. Compared to baseline
policies that only maximize user-side performance, our algorithm allows to
incorporate complex fairness of exposure criteria in the recommendations with
negligible computational overhead. | [
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] |
In recent years, with the large-scale deployment of space spacecraft entities
and the increase of satellite onboard capabilities, delay/disruption tolerant
network (DTN) emerged as a more robust communication protocol than TCP/IP in
the case of excessive network dynamics. DTN node buffer management is still an
active area of research, as the current implementation of the DTN core protocol
still relies on the assumption that there is always enough memory available in
different network nodes to store and forward bundles. In addition, the
classical queuing theory does not apply to the dynamic management of DTN node
buffers. Therefore, this paper proposes a centralized approach to automatically
manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation
scenarios based on the advanced reinforcement learning (RL) strategy advantage
actor-critic (A2C). The method aims to explore training a geosynchronous earth
orbit intelligent agent to manage all DTN nodes in an LEO satellite
constellation scenario. The goal of the A2C agent is to maximize delivery
success rate and minimize network resource consumption cost while considering
node memory utilization. The intelligent agent can dynamically adjust the radio
data rate and perform drop operations based on bundle priority. In order to
measure the effectiveness of applying A2C technology to DTN node management
issues in LEO satellite constellation scenarios, this paper compares the
trained intelligent agent strategy with the other two non-RL policies,
including random and standard policies. Experiments show that the A2C strategy
balances delivery success rate and cost, and provides the highest reward and
the lowest node memory utilization. | [
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] |
Artificial intelligence (AI) has been widely applied in drug discovery with a
major task as molecular property prediction. Despite booming techniques in
molecular representation learning, key elements underlying molecular property
prediction remain largely unexplored, which impedes further advancements in
this field. Herein, we conduct an extensive evaluation of representative models
using various representations on the MoleculeNet datasets, a suite of
opioids-related datasets and two additional activity datasets from the
literature. To investigate the predictive power in low-data and high-data
space, a series of descriptors datasets of varying sizes are also assembled to
evaluate the models. In total, we have trained 62,820 models, including 50,220
models on fixed representations, 4,200 models on SMILES sequences and 8,400
models on molecular graphs. Based on extensive experimentation and rigorous
comparison, we show that representation learning models exhibit limited
performance in molecular property prediction in most datasets. Besides,
multiple key elements underlying molecular property prediction can affect the
evaluation results. Furthermore, we show that activity cliffs can significantly
impact model prediction. Finally, we explore into potential causes why
representation learning models can fail and show that dataset size is essential
for representation learning models to excel. | [
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] |
Deep reinforcement learning (DRL) algorithms have recently gained wide
attention in the wireless networks domain. They are considered promising
approaches for solving dynamic radio resource management (RRM) problems in
next-generation networks. Given their capabilities to build an approximate and
continuously updated model of the wireless network environments, DRL algorithms
can deal with the multifaceted complexity of such environments. Nevertheless,
several challenges hinder the practical adoption of DRL in commercial networks.
In this article, we first discuss two key practical challenges that are faced
but rarely tackled when developing DRL-based RRM solutions. We argue that it is
inevitable to address these DRL-related challenges for DRL to find its way to
RRM commercial solutions. In particular, we discuss the need to have safe and
accelerated DRL-based RRM solutions that mitigate the slow convergence and
performance instability exhibited by DRL algorithms. We then review and
categorize the main approaches used in the RRM domain to develop safe and
accelerated DRL-based solutions. Finally, a case study is conducted to
demonstrate the importance of having safe and accelerated DRL-based RRM
solutions. We employ multiple variants of transfer learning (TL) techniques to
accelerate the convergence of intelligent radio access network (RAN) slicing
DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid
function-based rewards as examples of safe exploration in DRL-based RAN
slicing. | [
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] |
Operating under real world conditions is challenging due to the possibility
of a wide range of failures induced by execution errors and state uncertainty.
In relatively benign settings, such failures can be overcome by retrying or
executing one of a small number of hand-engineered recovery strategies. By
contrast, contact-rich sequential manipulation tasks, like opening doors and
assembling furniture, are not amenable to exhaustive hand-engineering. To
address this issue, we present a general approach for robustifying manipulation
strategies in a sample-efficient manner. Our approach incrementally improves
robustness by first discovering the failure modes of the current strategy via
exploration in simulation and then learning additional recovery skills to
handle these failures. To ensure efficient learning, we propose an online
algorithm called Meta-Reasoning for Skill Learning (MetaReSkill) that monitors
the progress of all recovery policies during training and allocates training
resources to recoveries that are likely to improve the task performance the
most. We use our approach to learn recovery skills for door-opening and
evaluate them both in simulation and on a real robot with little fine-tuning.
Compared to open-loop execution, our experiments show that even a limited
amount of recovery learning improves task success substantially from 71% to
92.4% in simulation and from 75% to 90% on a real robot. | [
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With the advances of the Internet of Things (IoT) and 5G/6G wireless
communications, the paradigms of mobile computing have developed dramatically
in recent years, from centralized mobile cloud computing to distributed fog
computing and mobile edge computing (MEC). MEC pushes compute-intensive
assignments to the edge of the network and brings resources as close to the
endpoints as possible, addressing the shortcomings of mobile devices with
regard to storage space, resource optimisation, computational performance and
efficiency. Compared to cloud computing, as the distributed and closer
infrastructure, the convergence of MEC with other emerging technologies,
including the Metaverse, 6G wireless communications, artificial intelligence
(AI), and blockchain, also solves the problems of network resource allocation,
more network load as well as latency requirements. Accordingly, this paper
investigates the computational paradigms used to meet the stringent
requirements of modern applications. The application scenarios of MEC in mobile
augmented reality (MAR) are provided. Furthermore, this survey presents the
motivation of MEC-based Metaverse and introduces the applications of MEC to the
Metaverse. Particular emphasis is given on a set of technical fusions mentioned
above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc. | [
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] |
Smart meter measurements, though critical for accurate demand forecasting,
face several drawbacks including consumers' privacy, data breach issues, to
name a few. Recent literature has explored Federated Learning (FL) as a
promising privacy-preserving machine learning alternative which enables
collaborative learning of a model without exposing private raw data for short
term load forecasting. Despite its virtue, standard FL is still vulnerable to
an intractable cyber threat known as Byzantine attack carried out by faulty
and/or malicious clients. Therefore, to improve the robustness of federated
short-term load forecasting against Byzantine threats, we develop a
state-of-the-art differentially private secured FL-based framework that ensures
the privacy of the individual smart meter's data while protect the security of
FL models and architecture. Our proposed framework leverages the idea of
gradient quantization through the Sign Stochastic Gradient Descent (SignSGD)
algorithm, where the clients only transmit the `sign' of the gradient to the
control centre after local model training. As we highlight through our
experiments involving benchmark neural networks with a set of Byzantine attack
models, our proposed approach mitigates such threats quite effectively and thus
outperforms conventional Fed-SGD models. | [
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] |
How might we design Reinforcement Learning (RL)-based recommenders that
encourage aligning user trajectories with the underlying user satisfaction?
Three research questions are key: (1) measuring user satisfaction, (2)
combatting sparsity of satisfaction signals, and (3) adapting the training of
the recommender agent to maximize satisfaction. For measurement, it has been
found that surveys explicitly asking users to rate their experience with
consumed items can provide valuable orthogonal information to the
engagement/interaction data, acting as a proxy to the underlying user
satisfaction. For sparsity, i.e, only being able to observe how satisfied users
are with a tiny fraction of user-item interactions, imputation models can be
useful in predicting satisfaction level for all items users have consumed. For
learning satisfying recommender policies, we postulate that reward shaping in
RL recommender agents is powerful for driving satisfying user experiences.
Putting everything together, we propose to jointly learn a policy network and a
satisfaction imputation network: The role of the imputation network is to learn
which actions are satisfying to the user; while the policy network, built on
top of REINFORCE, decides which items to recommend, with the reward utilizing
the imputed satisfaction. We use both offline analysis and live experiments in
an industrial large-scale recommendation platform to demonstrate the promise of
our approach for satisfying user experiences. | [
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] |
In this paper three customised Artificial Intelligence (AI) frameworks,
considering Deep Learning (convolutional neural networks), Machine Learning
algorithms and data reduction techniques are proposed, for a problem of
long-term summer air temperature prediction. Specifically, the prediction of
average air temperature in the first and second August fortnights, using input
data from previous months, at two different locations, Paris (France) and
C\'ordoba (Spain), is considered. The target variable, mainly in the first
August fortnight, can contain signals of extreme events such as heatwaves, like
the mega-heatwave of 2003, which affected France and the Iberian Peninsula.
Thus, an accurate prediction of long-term air temperature may be valuable also
for different problems related to climate change, such as attribution of
extreme events, and in other problems related to renewable energy. The analysis
carried out this work is based on Reanalysis data, which are first processed by
a correlation analysis among different prediction variables and the target
(average air temperature in August first and second fortnights). An area with
the largest correlation is located, and the variables within, after a feature
selection process, are the input of different deep learning and ML algorithms.
The experiments carried out show a very good prediction skill in the three
proposed AI frameworks, both in Paris and C\'ordoba regions. | [
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] |
Polar codes are widely used state-of-the-art codes for reliable communication
that have recently been included in the 5th generation wireless standards (5G).
However, there remains room for the design of polar decoders that are both
efficient and reliable in the short blocklength regime. Motivated by recent
successes of data-driven channel decoders, we introduce a novel
$\textbf{C}$ur$\textbf{RI}$culum based $\textbf{S}$equential neural decoder for
$\textbf{P}$olar codes (CRISP). We design a principled curriculum, guided by
information-theoretic insights, to train CRISP and show that it outperforms the
successive-cancellation (SC) decoder and attains near-optimal reliability
performance on the Polar(32,16) and Polar(64,22) codes. The choice of the
proposed curriculum is critical in achieving the accuracy gains of CRISP, as we
show by comparing against other curricula. More notably, CRISP can be readily
extended to Polarization-Adjusted-Convolutional (PAC) codes, where existing SC
decoders are significantly less reliable. To the best of our knowledge, CRISP
constructs the first data-driven decoder for PAC codes and attains near-optimal
performance on the PAC(32,16) code. | [
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] |
Due to its distributed nature, federated learning is vulnerable to poisoning
attacks, in which malicious clients poison the training process via
manipulating their local training data and/or local model updates sent to the
cloud server, such that the poisoned global model misclassifies many
indiscriminate test inputs or attacker-chosen ones. Existing defenses mainly
leverage Byzantine-robust federated learning methods or detect malicious
clients. However, these defenses do not have provable security guarantees
against poisoning attacks and may be vulnerable to more advanced attacks. In
this work, we aim to bridge the gap by proposing FLCert, an ensemble federated
learning framework, that is provably secure against poisoning attacks with a
bounded number of malicious clients. Our key idea is to divide the clients into
groups, learn a global model for each group of clients using any existing
federated learning method, and take a majority vote among the global models to
classify a test input. Specifically, we consider two methods to group the
clients and propose two variants of FLCert correspondingly, i.e., FLCert-P that
randomly samples clients in each group, and FLCert-D that divides clients to
disjoint groups deterministically. Our extensive experiments on multiple
datasets show that the label predicted by our FLCert for a test input is
provably unaffected by a bounded number of malicious clients, no matter what
poisoning attacks they use. | [
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Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to
interact in a shared dynamic environment -- permeates across a wide range of
critical applications. While there has been substantial progress on
understanding the global convergence of policy optimization methods in
single-agent RL, designing and analysis of efficient policy optimization
algorithms in the MARL setting present significant challenges, which
unfortunately, remain highly inadequately addressed by existing theory. In this
paper, we focus on the most basic setting of competitive multi-agent RL, namely
two-player zero-sum Markov games, and study equilibrium finding algorithms in
both the infinite-horizon discounted setting and the finite-horizon episodic
setting. We propose a single-loop policy optimization method with symmetric
updates from both agents, where the policy is updated via the
entropy-regularized optimistic multiplicative weights update (OMWU) method and
the value is updated on a slower timescale. We show that, in the
full-information tabular setting, the proposed method achieves a finite-time
last-iterate linear convergence to the quantal response equilibrium of the
regularized problem, which translates to a sublinear last-iterate convergence
to the Nash equilibrium by controlling the amount of regularization. Our
convergence results improve upon the best known iteration complexities, and
lead to a better understanding of policy optimization in competitive Markov
games. | [
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] |
Bayesian optimization (BO) is a popular method for efficiently inferring
optima of an expensive black-box function via a sequence of queries. Existing
information-theoretic BO procedures aim to make queries that most reduce the
uncertainty about optima, where the uncertainty is captured by Shannon entropy.
However, an optimal measure of uncertainty would, ideally, factor in how we
intend to use the inferred quantity in some downstream procedure. In this
paper, we instead consider a generalization of Shannon entropy from work in
statistical decision theory (DeGroot 1962, Rao 1984), which contains a broad
class of uncertainty measures parameterized by a problem-specific loss function
corresponding to a downstream task. We first show that special cases of this
entropy lead to popular acquisition functions used in BO procedures such as
knowledge gradient, expected improvement, and entropy search. We then show how
alternative choices for the loss yield a flexible family of acquisition
functions that can be customized for use in novel optimization settings.
Additionally, we develop gradient-based methods to efficiently optimize our
proposed family of acquisition functions, and demonstrate strong empirical
performance on a diverse set of sequential decision making tasks, including
variants of top-$k$ optimization, multi-level set estimation, and sequence
search. | [
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] |
In many complex sequential decision-making tasks, online planning is crucial
for high performance. For efficient online planning, Monte Carlo Tree Search
(MCTS) employs a principled mechanism for trading off exploration for
exploitation. MCTS outperforms comparison methods in many discrete
decision-making domains such as Go, Chess, and Shogi. Following, extensions of
MCTS to continuous domains have been proposed. However, the inherent high
branching factor and the resulting explosion of search tree size are limiting
existing methods. To address this problem, we propose Continuous Monte Carlo
Graph Search (CMCGS), a novel extension of MCTS to online planning in
environments with continuous state and action spaces. CMCGS takes advantage of
the insight that, during planning, sharing the same action policy between
several states can yield high performance. To implement this idea, at each time
step, CMCGS clusters similar states into a limited number of stochastic action
bandit nodes, which produce a layered directed graph instead of an MCTS search
tree. Experimental evaluation shows that CMCGS outperforms comparable planning
methods in several complex continuous DeepMind Control Suite benchmarks and a
2D navigation task with limited sample budgets. Furthermore, CMCGS can be
parallelized to scale up and it outperforms the Cross-Entropy Method (CEM) in
continuous control with learned dynamics models. | [
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] |
News recommendation is one of the most challenging tasks in recommender
systems, mainly due to the ephemeral relevance of news to users. As social
media, and particularly microblogging applications like Twitter or Weibo, gains
popularity as platforms for news dissemination, personalized news
recommendation in this context becomes a significant challenge. We revisit news
recommendation in the microblogging scenario, by taking into consideration
social interactions and observations tracing how the information that is up for
recommendation spreads in an underlying network. We propose a deep-learning
based approach that is diffusion and influence-aware, called Influence-Graph
News Recommender (IGNiteR). It is a content-based deep recommendation model
that jointly exploits all the data facets that may impact adoption decisions,
namely semantics, diffusion-related features pertaining to local and global
influence among users, temporal attractiveness, and timeliness, as well as
dynamic user preferences. To represent the news, a multi-level attention-based
encoder is used to reveal the different interests of users. This news encoder
relies on a CNN for the news content and on an attentive LSTM for the diffusion
traces. For the latter, by exploiting previously observed news diffusions
(cascades) in the microblogging medium, users are mapped to a latent space that
captures potential influence on others or susceptibility of being influenced
for news adoptions. Similarly, a time-sensitive user encoder enables us to
capture the dynamic preferences of users with an attention-based bidirectional
LSTM. We perform extensive experiments on two real-world datasets, showing that
IGNiteR outperforms the state-of-the-art deep-learning based news
recommendation methods. | [
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] |
A scalable and computationally efficient framework is designed to fingerprint
real-world Bluetooth devices. We propose an embedding-assisted attentional
framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its
generalization capability is analyzed in different settings and the effect of
sample length and anti-aliasing decimation is demonstrated. The embedding
module serves as a dimensionality reduction unit that maps the high dimensional
3D input tensor to a 1D feature vector for further processing by the ATN
module. Furthermore, unlike the prior research in this field, we closely
evaluate the complexity of the model and test its fingerprinting capability
with real-world Bluetooth dataset collected under a different time frame and
experimental setting while being trained on another. Our study reveals a 9.17x
and 65.2x lesser memory usage at a sample length of 100 kS when compared to the
benchmark - GRU and Oracle models respectively. Further, the proposed Mbed-ATN
showcases 16.9x fewer FLOPs and 7.5x lesser trainable parameters when compared
to Oracle. Finally, we show that when subject to anti-aliasing decimation and
at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework
results in a 5.32x higher TPR, 37.9% fewer false alarms, and 6.74x higher
accuracy under the challenging real-world setting. | [
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] |
In recent years, much work has been done on processing of wireless spectrum
data involving machine learning techniques in domain-related problems for
cognitive radio networks, such as anomaly detection, modulation classification,
technology classification and device fingerprinting. Most of the solutions are
based on labeled data, created in a controlled manner and processed with
supervised learning approaches. However, spectrum data measured in real-world
environment is highly nondeterministic, making its labeling a laborious and
expensive process, requiring domain expertise, thus being one of the main
drawbacks of using supervised learning approaches in this domain. In this
paper, we investigate the use of self-supervised learning (SSL) for exploring
spectrum activities in a real-world unlabeled data. In particular, we compare
the performance of two SSL models, one based on a reference DeepCluster
architecture and one adapted for spectrum activity identification and
clustering, and a baseline model based on K-means clustering algorithm. We show
that SSL models achieve superior performance regarding the quality of extracted
features and clustering performance. With SSL models we achieve reduction of
the feature vectors size by two orders of magnitude, while improving the
performance by a factor of 2 to 2.5 across the evaluation metrics, supported by
visual assessment. Additionally we show that adaptation of the reference SSL
architecture to the domain data provides reduction of model complexity by one
order of magnitude, while preserving or even improving the clustering
performance. | [
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] |
Prompt-based learning has emerged as a successful paradigm in natural
language processing, where a single general-purpose language model can be
instructed to perform any task specified by input prompts. Yet task
specification in robotics comes in various forms, such as imitating one-shot
demonstrations, following language instructions, and reaching visual goals.
They are often considered different tasks and tackled by specialized models. We
show that a wide spectrum of robot manipulation tasks can be expressed with
multimodal prompts, interleaving textual and visual tokens. Accordingly, we
develop a new simulation benchmark that consists of thousands of
procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert
trajectories for imitation learning, and a four-level evaluation protocol for
systematic generalization. We design a transformer-based robot agent, VIMA,
that processes these prompts and outputs motor actions autoregressively. VIMA
features a recipe that achieves strong model scalability and data efficiency.
It outperforms alternative designs in the hardest zero-shot generalization
setting by up to $2.9\times$ task success rate given the same training data.
With $10\times$ less training data, VIMA still performs $2.7\times$ better than
the best competing variant. Code and video demos are available at
https://vimalabs.github.io/ | [
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] |
Determining the state of a mobile robot is an essential building block of
robot navigation systems. In this paper, we address the problem of estimating
the robots pose in an indoor environment using 2D LiDAR data and investigate
how modern environment models can improve gold standard Monte-Carlo
localization (MCL) systems. We propose a neural occupancy field to implicitly
represent the scene using a neural network. With the pretrained network, we can
synthesize 2D LiDAR scans for an arbitrary robot pose through volume rendering.
Based on the implicit representation, we can obtain the similarity between a
synthesized and actual scan as an observation model and integrate it into an
MCL system to perform accurate localization. We evaluate our approach on
self-recorded datasets and three publicly available ones. We show that we can
accurately and efficiently localize a robot using our approach surpassing the
localization performance of state-of-the-art methods. The experiments suggest
that the presented implicit representation is able to predict more accurate 2D
LiDAR scans leading to an improved observation model for our particle
filter-based localization. The code of our approach will be available at:
https://github.com/PRBonn/ir-mcl. | [
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] |
Learning provides a powerful tool for vision-based navigation, but the
capabilities of learning-based policies are constrained by limited training
data. If we could combine data from all available sources, including multiple
kinds of robots, we could train more powerful navigation models. In this paper,
we study how a general goal-conditioned model for vision-based navigation can
be trained on data obtained from many distinct but structurally similar robots,
and enable broad generalization across environments and embodiments. We analyze
the necessary design decisions for effective data sharing across robots,
including the use of temporal context and standardized action spaces, and
demonstrate that an omnipolicy trained from heterogeneous datasets outperforms
policies trained on any single dataset. We curate 60 hours of navigation
trajectories from 6 distinct robots, and deploy the trained GNM on a range of
new robots, including an underactuated quadrotor. We find that training on
diverse data leads to robustness against degradation in sensing and actuation.
Using a pre-trained navigation model with broad generalization capabilities can
bootstrap applications on novel robots going forward, and we hope that the GNM
represents a step in that direction. For more information on the datasets,
code, and videos, please check out our project page
https://sites.google.com/view/drive-any-robot. | [
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] |
Recent years have seen growing interest among both researchers and
practitioners in user-engaged approaches to algorithm auditing, which directly
engage users in detecting problematic behaviors in algorithmic systems.
However, we know little about industry practitioners' current practices and
challenges around user-engaged auditing, nor what opportunities exist for them
to better leverage such approaches in practice. To investigate, we conducted a
series of interviews and iterative co-design activities with practitioners who
employ user-engaged auditing approaches in their work. Our findings reveal
several challenges practitioners face in appropriately recruiting and
incentivizing user auditors, scaffolding user audits, and deriving actionable
insights from user-engaged audit reports. Furthermore, practitioners shared
organizational obstacles to user-engaged auditing, surfacing a complex
relationship between practitioners and user auditors. Based on these findings,
we discuss opportunities for future HCI research to help realize the potential
(and the mitigate risks) of user-engaged auditing in industry practice. | [
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] |
We consider quantitative analyses of spectral data using laser-induced
breakdown spectroscopy. We address the small size of training data available,
and the validation of the predictions during inference on unknown data. For the
purpose, we build robust calibration models using deep convolutional multitask
learning architectures to predict the concentration of the analyte, alongside
additional spectral information as auxiliary outputs. These secondary
predictions can be used to validate the trustworthiness of the model by taking
advantage of the mutual dependencies of the parameters of the multitask neural
networks. Due to the experimental lack of training samples, we introduce a
simulation-based data augmentation process to synthesise an arbitrary number of
spectra, statistically representative of the experimental data. Given the
nature of the deep learning model, no dimensionality reduction or data
selection processes are required. The procedure is an end-to-end pipeline
including the process of synthetic data augmentation, the construction of a
suitable robust, homoscedastic, deep learning model, and the validation of its
predictions. In the article, we compare the performance of the multitask model
with traditional univariate and multivariate analyses, to highlight the
separate contributions of each element introduced in the process. | [
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] |
Neural networks (NNs) are increasingly applied in safety-critical systems
such as autonomous vehicles. However, they are fragile and are often
ill-behaved. Consequently, their behaviors should undergo rigorous guarantees
before deployment in practice. In this paper we propose a set-boundary
reachability method to investigate the safety verification problem of NNs from
a topological perspective. Given an NN with an input set and a safe set, the
safety verification problem is to determine whether all outputs of the NN
resulting from the input set fall within the safe set. In our method, the
homeomorphism property of NNs is mainly exploited, which establishes a
relationship mapping boundaries to boundaries. The exploitation of this
property facilitates reachability computations via extracting subsets of the
input set rather than the entire input set, thus controlling the wrapping
effect in reachability analysis and facilitating the reduction of computation
burdens for safety verification. The homeomorphism property exists in some
widely used NNs such as invertible NNs. Notable representations are invertible
residual networks (i-ResNets) and Neural ordinary differential equations
(Neural ODEs). For these NNs, our set-boundary reachability method only needs
to perform reachability analysis on the boundary of the input set. For NNs
which do not feature this property with respect to the input set, we explore
subsets of the input set for establishing the local homeomorphism property, and
then abandon these subsets for reachability computations. Finally, some
examples demonstrate the performance of the proposed method. | [
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] |
Graphs emerge in almost every real-world application domain, ranging from
online social networks all the way to health data and movie viewership
patterns. Typically, such real-world graphs are big and dynamic, in the sense
that they evolve over time. Furthermore, graphs usually contain multi-aspect
information i.e. in a social network, we can have the "means of communication"
between nodes, such as who messages whom, who calls whom, and who comments on
whose timeline and so on.
How can we model and mine useful patterns, such as communities of nodes in
that graph, from such multi-aspect graphs? How can we identify dynamic patterns
in those graphs, and how can we deal with streaming data, when the volume of
data to be processed is very large? In order to answer those questions, in this
thesis, we propose novel tensor-based methods for mining static and dynamic
multi-aspect graphs. In general, a tensor is a higher-order generalization of a
matrix that can represent high-dimensional multi-aspect data such as
time-evolving networks, collaboration networks, and spatio-temporal data like
Electroencephalography (EEG) brain measurements.
The thesis is organized in two synergistic thrusts: First, we focus on static
multi-aspect graphs, where the goal is to identify coherent communities and
patterns between nodes by leveraging the tensor structure in the data. Second,
as our graphs evolve dynamically, we focus on handling such streaming updates
in the data without having to re-compute the decomposition, but incrementally
update the existing results. | [
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] |
Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, when
the number of participating clients is large, the overhead of managing the
clients slows down the learning. Thus, client selection has been introduced as
a strategy to limit the number of communicating parties at every step of the
process. Since the early na\"{i}ve random selection of clients, several client
selection methods have been proposed in the literature. Unfortunately, given
that this is an emergent field, there is a lack of a taxonomy of client
selection methods, making it hard to compare approaches. In this paper, we
propose a taxonomy of client selection in Federated Learning that enables us to
shed light on current progress in the field and identify potential areas of
future research in this promising area of machine learning. | [
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] |
Code pre-trained models (CodePTMs) have recently demonstrated significant
success in code intelligence. To interpret these models, some probing methods
have been applied. However, these methods fail to consider the inherent
characteristics of codes. In this paper, to address the problem, we propose a
novel probing method CAT-probing to quantitatively interpret how CodePTMs
attend code structure. We first denoise the input code sequences based on the
token types pre-defined by the compilers to filter those tokens whose attention
scores are too small. After that, we define a new metric CAT-score to measure
the commonality between the token-level attention scores generated in CodePTMs
and the pair-wise distances between corresponding AST nodes. The higher the
CAT-score, the stronger the ability of CodePTMs to capture code structure. We
conduct extensive experiments to integrate CAT-probing with representative
CodePTMs for different programming languages. Experimental results show the
effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are
publicly available at https://github.com/nchen909/CodeAttention. | [
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] |
The high effectiveness of neural models of code, such as OpenAI Codex and
AlphaCode, suggests coding capabilities of models that are at least comparable
to those of humans. However, previous work has only used these models for their
raw completion, ignoring how the model reasoning, in the form of attention
weights, can be used for other downstream tasks. Disregarding the attention
weights means discarding a considerable portion of what those models compute
when queried. To profit more from the knowledge embedded in these large
pre-trained models, this work compares multiple approaches to post-process
these valuable attention weights for supporting code exploration. Specifically,
we compare to which extent the transformed attention signal of CodeGen, a large
and publicly available pretrained neural model, agrees with how developers look
at and explore code when each answering the same sense-making questions about
code. At the core of our experimental evaluation, we collect, manually
annotate, and open-source a novel eye-tracking dataset comprising 25 developers
answering sense-making questions on code over 92 sessions. We empirically
evaluate five attention-agnostic heuristics and ten attention-based post
processing approaches of the attention signal against our ground truth of
developers exploring code, including the novel concept of follow-up attention
which exhibits the highest agreement. Beyond the dataset contribution and the
empirical study, we also introduce a novel practical application of the
attention signal of pre-trained models with completely analytical solutions,
going beyond how neural models' attention mechanisms have traditionally been
used. | [
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] |
This paper proposes a model that generates a drum track in the audio domain
to play along to a user-provided drum-free recording. Specifically, using
paired data of drumless tracks and the corresponding human-made drum tracks, we
train a Transformer model to improvise the drum part of an unseen drumless
recording. We combine two approaches to encode the input audio. First, we train
a vector-quantized variational autoencoder (VQ-VAE) to represent the input
audio with discrete codes, which can then be readily used in a Transformer.
Second, using an audio-domain beat tracking model, we compute beat-related
features of the input audio and use them as embeddings in the Transformer.
Instead of generating the drum track directly as waveforms, we use a separate
VQ-VAE to encode the mel-spectrogram of a drum track into another set of
discrete codes, and train the Transformer to predict the sequence of
drum-related discrete codes. The output codes are then converted to a
mel-spectrogram with a decoder, and then to the waveform with a vocoder. We
report both objective and subjective evaluations of variants of the proposed
model, demonstrating that the model with beat information generates drum
accompaniment that is rhythmically and stylistically consistent with the input
audio. | [
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Machine learning on electromyography (EMG) has recently achieved remarkable
success on a variety of tasks, while such success relies heavily on the
assumption that the training and future data must be of the same data
distribution. However, this assumption may not hold in many real-world
applications. Model calibration is required via data re-collection and label
annotation, which is generally very expensive and time-consuming. To address
this problem, transfer learning (TL), which aims to improve target learners'
performance by transferring the knowledge from related source domains, is
emerging as a new paradigm to reduce the amount of calibration effort. In this
survey, we assess the eligibility of more than fifty published peer-reviewed
representative transfer learning approaches for EMG applications. Unlike
previous surveys on purely transfer learning or EMG-based machine learning,
this survey aims to provide an insight into the biological foundations of
existing transfer learning methods on EMG-related analysis. In specific, we
first introduce the physiological structure of the muscles and the EMG
generating mechanism, and the recording of EMG to provide biological insights
behind existing transfer learning approaches. Further, we categorize existing
research endeavors into data based, model based, training scheme based, and
adversarial based. This survey systematically summarizes and categorizes
existing transfer learning approaches for EMG related machine learning
applications. In addition, we discuss possible drawbacks of existing works and
point out the future direction of better EMG transfer learning algorithms to
enhance practicality for real-world applications. | [
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] |
Electroencephalograms (EEGs) are brain dynamics measured outside the brain,
which have been widely utilized in non-invasive brain-computer interface
applications. Recently, various neural network approaches have been proposed to
improve the accuracy of EEG signal recognition. However, these approaches
severely rely on manually designed network structures for different tasks which
generally are not sharing the same empirical design cross-task-wise. In this
paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework
for EEG signal recognition, which can automatically design the network
structure across tasks and improve the recognition accuracy of EEG signals.
Specifically, a compatible search space for cross-task searching and an
efficient constrained searching method is proposed to overcome challenges
brought by EEG signals. By unifying structure search on different EEG tasks,
this work is the first to explore and analyze the searched structure difference
cross-task-wise. Moreover, by introducing architecture search, this work is the
first to analyze model performance by customizing model structure for each
human subject. Detailed experimental results suggest that the proposed
CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such
as Motor Imagery (MI) and Emotion recognition. Extensive experiments and
detailed analysis are provided as a good reference for follow-up researchers. | [
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] |
To operate safely, an automated vehicle (AV) must anticipate how the
environment around it will evolve. For that purpose, it is important to know
which prediction models are most appropriate for every situation. Currently,
assessment of prediction models is often performed over a set of trajectories
without distinction of the type of movement they capture, resulting in the
inability to determine the suitability of each model for different situations.
In this work we illustrate how standardized evaluation methods result in wrong
conclusions regarding a model's predictive capabilities, preventing a clear
assessment of prediction models and potentially leading to dangerous on-road
situations. We argue that following evaluation practices in safety assessment
for AVs, assessment of prediction models should be performed in a
scenario-based fashion. To encourage scenario-based assessment of prediction
models and illustrate the dangers of improper assessment, we categorize
trajectories of the Waymo Open Motion dataset according to the type of movement
they capture. Next, three different models are thoroughly evaluated for
different trajectory types and prediction horizons. Results show that common
evaluation methods are insufficient and the assessment should be performed
depending on the application in which the model will operate. | [
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] |
The utilization of broad datasets has proven to be crucial for generalization
for a wide range of fields. However, how to effectively make use of diverse
multi-task data for novel downstream tasks still remains a grand challenge in
robotics. To tackle this challenge, we introduce a framework that acquires
goal-conditioned policies for unseen temporally extended tasks via offline
reinforcement learning on broad data, in combination with online fine-tuning
guided by subgoals in learned lossy representation space. When faced with a
novel task goal, the framework uses an affordance model to plan a sequence of
lossy representations as subgoals that decomposes the original task into easier
problems. Learned from the broad data, the lossy representation emphasizes
task-relevant information about states and goals while abstracting away
redundant contexts that hinder generalization. It thus enables subgoal planning
for unseen tasks, provides a compact input to the policy, and facilitates
reward shaping during fine-tuning. We show that our framework can be
pre-trained on large-scale datasets of robot experiences from prior work and
efficiently fine-tuned for novel tasks, entirely from visual inputs without any
manual reward engineering. | [
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] |
In drug discovery, aqueous solubility is an important pharmacokinetic
property which affects absorption and assay availability of drug. Thus, in
silico prediction of solubility has been studied for its utility in virtual
screening and lead optimization. Recently, machine learning (ML) methods using
experimental data has been popular because physics-based methods like quantum
mechanics and molecular dynamics are not suitable for high-throughput tasks due
to its computational costs. However, ML method can exhibit over-fitting problem
in a data-deficient condition, and this is the case for most chemical property
datasets. In addition, ML methods are regarded as a black box function in that
it is difficult to interpret contribution of hidden features to outputs,
hindering analysis and modification of structure-activity relationship. To deal
with mentioned issues, we developed Bayesian graph neural networks (GNNs) with
the self-attention readout layer. Unlike most GNNs using self-attention in node
updates, self-attention applied at readout layer enabled a model to improve
prediction performance as well as to identify atom-wise importance, which can
help lead optimization as exemplified for three FDA-approved drugs. Also,
Bayesian inference enables us to separate more or less accurate results
according to uncertainty in solubility prediction task We expect that our
accurate, reliable and interpretable model can be used for more careful
decision-making and various applications in the development of drugs. | [
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] |
Reference-model-assisted deep reinforcement learning (DRL) for inverter-based
Volt-Var Control (IB-VVC) in active distribution networks is proposed. We
investigate that a large action space increases the learning difficulties of
DRL and degrades the optimization performance in the process of generating data
and training neural networks. To reduce the action space of DRL, we design a
reference-model-assisted DRL approach. We introduce definitions of the
reference model, reference-model-based optimization, and reference actions. The
reference-model-assisted DRL learns the residual actions between the reference
actions and optimal actions, rather than learning the optimal actions directly.
Since the residual actions are considerably smaller than the optimal actions
for a reference model, we can design a smaller action space for the
reference-model-assisted DRL. It reduces the learning difficulties of DRL and
optimises the performance of the reference-model-assisted DRL approach. It is
noteworthy that the reference-model-assisted DRL approach is compatible with
any policy gradient DRL algorithms for continuous action problems. This work
takes the soft actor-critic algorithm as an example and designs a
reference-model-assisted soft actor-critic algorithm. Simulations show that 1)
large action space degrades the performance of DRL in the whole training stage,
and 2) reference-model-assisted DRL requires fewer iteration times and returns
a better optimization performance. | [
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] |
Affective computing strives to unveil the unknown relationship between affect
elicitation, manifestation of affect and affect annotations. The ground truth
of affect, however, is predominately attributed to the affect labels which
inadvertently include biases inherent to the subjective nature of emotion and
its labeling. The response to such limitations is usually augmenting the
dataset with more annotations per data point; however, this is not possible
when we are interested in self-reports via first-person annotation. Moreover,
outlier detection methods based on inter-annotator agreement only consider the
annotations themselves and ignore the context and the corresponding affect
manifestation. This paper reframes the ways one may obtain a reliable ground
truth of affect by transferring aspects of causation theory to affective
computing. In particular, we assume that the ground truth of affect can be
found in the causal relationships between elicitation, manifestation and
annotation that remain \emph{invariant} across tasks and participants. To test
our assumption we employ causation inspired methods for detecting outliers in
affective corpora and building affect models that are robust across
participants and tasks. We validate our methodology within the domain of
digital games, with experimental results showing that it can successfully
detect outliers and boost the accuracy of affect models. To the best of our
knowledge, this study presents the first attempt to integrate causation tools
in affective computing, making a crucial and decisive step towards general
affect modeling. | [
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] |
There has been a flurry of research in recent years on notions of fairness in
ranking and recommender systems, particularly on how to evaluate if a
recommender allocates exposure equally across groups of relevant items (also
known as provider fairness). While this research has laid an important
foundation, it gave rise to different approaches depending on whether relevant
items are compared per-user/per-query or aggregated across users. Despite both
being established and intuitive, we discover that these two notions can lead to
opposite conclusions, a form of Simpson's Paradox. We reconcile these notions
and show that the tension is due to differences in distributions of users where
items are relevant, and break down the important factors of the user's
recommendations. Based on this new understanding, practitioners might be
interested in either notions, but might face challenges with the per-user
metric due to partial observability of the relevance and user satisfaction,
typical in real-world recommenders. We describe a technique based on
distribution matching to estimate it in such a scenario. We demonstrate on
simulated and real-world recommender data the effectiveness and usefulness of
such an approach. | [
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] |
Airbnb is a two-sided marketplace, bringing together hosts who own listings
for rent, with prospective guests from around the globe. Applying neural
network-based learning to rank techniques has led to significant improvements
in matching guests with hosts. These improvements in ranking were driven by a
core strategy: order the listings by their estimated booking probabilities,
then iterate on techniques to make these booking probability estimates more and
more accurate. Embedded implicitly in this strategy was an assumption that the
booking probability of a listing could be determined independently of other
listings in search results. In this paper we discuss how this assumption,
pervasive throughout the commonly-used learning to rank frameworks, is false.
We provide a theoretical foundation correcting this assumption, followed by
efficient neural network architectures based on the theory. Explicitly
accounting for possible similarities between listings, and reducing them to
diversify the search results generated strong positive impact. We discuss these
metric wins as part of the online A/B tests of the theory. Our method provides
a practical way to diversify search results for large-scale production ranking
systems. | [
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] |
In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source,
GPU-accelerated integration framework for click-through rate estimation. It
optimizes both training and inference, whilst enabling model training at scale
with model-parallel embeddings and data-parallel neural networks. In
particular, Merlin HugeCTR combines a high-performance GPU embedding cache with
an hierarchical storage architecture, to realize low-latency retrieval of
embeddings for online model inference tasks. In the MLPerf v1.0 DLRM model
training benchmark, Merlin HugeCTR achieves a speedup of up to 24.6x on a
single DGX A100 (8x A100) over PyTorch on 4x4-socket CPU nodes (4x4x28 cores).
Merlin HugeCTR can also take advantage of multi-node environments to accelerate
training even further. Since late 2021, Merlin HugeCTR additionally features a
hierarchical parameter server (HPS) and supports deployment via the NVIDIA
Triton server framework, to leverage the computational capabilities of GPUs for
high-speed recommendation model inference. Using this HPS, Merlin HugeCTR users
can achieve a 5~62x speedup (batch size dependent) for popular recommendation
models over CPU baseline implementations, and dramatically reduce their
end-to-end inference latency. | [
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] |
Recommendation systems are of crucial importance for a variety of modern apps
and web services, such as news feeds, social networks, e-commerce, search, etc.
To achieve peak prediction accuracy, modern recommendation models combine deep
learning with terabyte-scale embedding tables to obtain a fine-grained
representation of the underlying data. Traditional inference serving
architectures require deploying the whole model to standalone servers, which is
infeasible at such massive scale.
In this paper, we provide insights into the intriguing and challenging
inference domain of online recommendation systems. We propose the HugeCTR
Hierarchical Parameter Server (HPS), an industry-leading distributed
recommendation inference framework, that combines a high-performance GPU
embedding cache with an hierarchical storage architecture, to realize
low-latency retrieval of embeddings for online model inference tasks. Among
other things, HPS features (1) a redundant hierarchical storage system, (2) a
novel high-bandwidth cache to accelerate parallel embedding lookup on NVIDIA
GPUs, (3) online training support and (4) light-weight APIs for easy
integration into existing large-scale recommendation workflows. To demonstrate
its capabilities, we conduct extensive studies using both synthetically
engineered and public datasets. We show that our HPS can dramatically reduce
end-to-end inference latency, achieving 5~62x speedup (depending on the batch
size) over CPU baseline implementations for popular recommendation models.
Through multi-GPU concurrent deployment, the HPS can also greatly increase the
inference QPS. | [
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] |
Knowledge tracing and dropout prediction are crucial for online education to
estimate students' knowledge states or to prevent dropout rates. While
traditional systems interacting with students suffered from data sparsity and
overfitting, recent sample-level contrastive learning helps to alleviate this
issue. One major limitation of sample-level approaches is that they regard
students' behavior interaction sequences as a bundle, so they often fail to
encode temporal contexts and track their dynamic changes, making it hard to
find optimal representations for knowledge tracing and dropout prediction. To
apply temporal context within the sequence, this study introduces a novel
student modeling framework, SAICL: \textbf{s}tudent modeling with
\textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive
\textbf{l}earning. In detail, SAICL can utilize both proposed
self-supervised/supervised interaction-level contrastive objectives: MilCPC
(\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive
\textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised
\textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous
sample-level contrastive methods for student modeling are highly dependent on
data augmentation methods, the SAICL is free of data augmentation while showing
better performance in both self-supervised and supervised settings. By
combining cross-entropy with contrastive objectives, the proposed SAICL
achieved comparable knowledge tracing and dropout prediction performance with
other state-of-art models without compromising inference costs. | [
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Knowledge Tracing (KT), which aims to model student knowledge level and
predict their performance, is one of the most important applications of user
modeling. Modern KT approaches model and maintain an up-to-date state of
student knowledge over a set of course concepts according to students'
historical performance in attempting the problems. However, KT approaches were
designed to model knowledge by observing relatively small problem-solving steps
in Intelligent Tutoring Systems. While these approaches were applied
successfully to model student knowledge by observing student solutions for
simple problems, they do not perform well for modeling complex problem solving
in students.M ost importantly, current models assume that all problem attempts
are equally valuable in quantifying current student knowledge.However, for
complex problems that involve many concepts at the same time, this assumption
is deficient. In this paper, we argue that not all attempts are equivalently
important in discovering students' knowledge state, and some attempts can be
summarized together to better represent student performance. We propose a novel
student knowledge tracing approach, Granular RAnk based TEnsor factorization
(GRATE), that dynamically selects student attempts that can be aggregated while
predicting students' performance in problems and discovering the concepts
presented in them. Our experiments on three real-world datasets demonstrate the
improved performance of GRATE, compared to the state-of-the-art baselines, in
the task of student performance prediction. Our further analysis shows that
attempt aggregation eliminates the unnecessary fluctuations from students'
discovered knowledge states and helps in discovering complex latent concepts in
the problems. | [
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] |
Machine learning (ML) enabled classification models are becoming increasingly
popular for tackling the sheer volume and speed of online misinformation and
other content that could be identified as harmful. In building these models,
data scientists need to take a stance on the legitimacy, authoritativeness and
objectivity of the sources of ``truth" used for model training and testing.
This has political, ethical and epistemic implications which are rarely
addressed in technical papers. Despite (and due to) their reported high
accuracy and performance, ML-driven moderation systems have the potential to
shape online public debate and create downstream negative impacts such as undue
censorship and the reinforcing of false beliefs. Using collaborative
ethnography and theoretical insights from social studies of science and
expertise, we offer a critical analysis of the process of building ML models
for (mis)information classification: we identify a series of algorithmic
contingencies--key moments during model development that could lead to
different future outcomes, uncertainty and harmful effects as these tools are
deployed by social media platforms. We conclude by offering a tentative path
toward reflexive and responsible development of ML tools for moderating
misinformation and other harmful content online. | [
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] |
In complex environments, where the human sensory system reaches its limits,
our behaviour is strongly driven by our beliefs about the state of the world
around us. Accessing others' beliefs, intentions, or mental states in general,
could thus allow for more effective social interactions in natural contexts.
Yet these variables are not directly observable. Theory of Mind (TOM), the
ability to attribute to other agents' beliefs, intentions, or mental states in
general,
is a crucial feature of human social interaction and has become of interest
to the robotics community. Recently, new models that are able to learn TOM have
been introduced. In this paper, we show the synergy between learning to predict
low-level mental states, such as intentions and goals, and attributing
high-level ones, such as beliefs. Assuming that learning of beliefs can take
place by observing own decision and beliefs estimation processes in partially
observable environments and using a simple feed-forward deep learning model, we
show that when learning to predict others' intentions and actions, faster and
more accurate predictions can be acquired if beliefs attribution is learnt
simultaneously with action and intentions prediction. We show that the learning
performance improves even when observing agents with a different decision
process and is higher when observing beliefs-driven chunks of behaviour. We
propose that our architectural approach can be relevant for the design of
future adaptive social robots that should be able to autonomously understand
and assist human partners in novel natural environments and tasks. | [
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] |
We demonstrate the first large-scale application of model-based generative
adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
We augment standard MGAIL using a hierarchical model to enable generalization
to arbitrary goal routes, and measure performance using a closed-loop
evaluation framework with simulated interactive agents. We train policies from
expert trajectories collected from real vehicles driving over 100,000 miles in
San Francisco, and demonstrate a steerable policy that can navigate robustly
even in a zero-shot setting, generalizing to synthetic scenarios with novel
goals that never occurred in real-world driving. We also demonstrate the
importance of mixing closed-loop MGAIL losses with open-loop behavior cloning
losses, and show our best policy approaches the performance of the expert. We
evaluate our imitative model in both average and challenging scenarios, and
show how it can serve as a useful prior to plan successful trajectories. | [
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] |
Unsupervised representation learning for dynamic graphs has attracted a lot
of research attention in recent years. Compared with static graph, the dynamic
graph is a comprehensive embodiment of both the intrinsic stable
characteristics of nodes and the time-related dynamic preference. However,
existing methods generally mix these two types of information into a single
representation space, which may lead to poor explanation, less robustness, and
a limited ability when applied to different downstream tasks. To solve the
above problems, in this paper, we propose a novel disenTangled representation
learning framework for discrete-time Dynamic graphs, namely DyTed. We specially
design a temporal-clips contrastive learning task together with a structure
contrastive learning to effectively identify the time-invariant and
time-varying representations respectively. To further enhance the
disentanglement of these two types of representation, we propose a
disentanglement-aware discriminator under an adversarial learning framework
from the perspective of information theory. Extensive experiments on Tencent
and five commonly used public datasets demonstrate that DyTed, as a general
framework that can be applied to existing methods, achieves state-of-the-art
performance on various downstream tasks, as well as be more robust against
noise. | [
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] |
Digital human recommendation system has been developed to help customers find
their favorite products and is playing an active role in various recommendation
contexts. How to timely catch and learn the dynamics of the preferences of the
customers, while meeting their exact requirements, becomes crucial in the
digital human recommendation domain. We design a novel practical digital human
interactive recommendation agent framework based on Reinforcement Learning(RL)
to improve the efficiency of the interactive recommendation decision-making by
leveraging both the digital human features and the superior flexibility of RL.
Our proposed framework learns through real-time interactions between the
digital human and customers dynamically through the state-of-art RL algorithms,
combined with multimodal embedding and graph embedding, to improve the accuracy
of personalization and thus enable the digital human agent to timely catch the
attention of the customer. Experiments on real business data demonstrate that
our framework can provide better personalized customer engagement and better
customer experiences. | [
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Understanding which student support strategies mitigate dropout and improve
student retention is an important part of modern higher educational research.
One of the largest challenges institutions of higher learning currently face is
the scalability of student support. Part of this is due to the shortage of
staff addressing the needs of students, and the subsequent referral pathways
associated to provide timeous student support strategies. This is further
complicated by the difficulty of these referrals, especially as students are
often faced with a combination of administrative, academic, social, and
socio-economic challenges. A possible solution to this problem can be a
combination of student outcome predictions and applying algorithmic recommender
systems within the context of higher education. While much effort and detail
has gone into the expansion of explaining algorithmic decision making in this
context, there is still a need to develop data collection strategies Therefore,
the purpose of this paper is to outline a data collection framework specific to
recommender systems within this context in order to reduce collection biases,
understand student characteristics, and find an ideal way to infer optimal
influences on the student journey. If confirmation biases, challenges in data
sparsity and the type of information to collect from students are not
addressed, it will have detrimental effects on attempts to assess and evaluate
the effects of these systems within higher education. | [
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] |
When robots enter everyday human environments, they need to understand their
tasks and how they should perform those tasks. To encode these, reward
functions, which specify the objective of a robot, are employed. However,
designing reward functions can be extremely challenging for complex tasks and
environments. A promising approach is to learn reward functions from humans.
Recently, several robot learning works embrace this approach and leverage human
demonstrations to learn the reward functions. Known as inverse reinforcement
learning, this approach relies on a fundamental assumption: humans can provide
near-optimal demonstrations to the robot. Unfortunately, this is rarely the
case: human demonstrations to the robot are often suboptimal due to various
reasons, e.g., difficulty of teleoperation, robot having high degrees of
freedom, or humans' cognitive limitations.
This thesis is an attempt towards learning reward functions from human users
by using other, more reliable data modalities. Specifically, we study how
reward functions can be learned using comparative feedback, in which the human
user compares multiple robot trajectories instead of (or in addition to)
providing demonstrations. To this end, we first propose various forms of
comparative feedback, e.g., pairwise comparisons, best-of-many choices,
rankings, scaled comparisons; and describe how a robot can use these various
forms of human feedback to infer a reward function, which may be parametric or
non-parametric. Next, we propose active learning techniques to enable the robot
to ask for comparison feedback that optimizes for the expected information that
will be gained from that user feedback. Finally, we demonstrate the
applicability of our methods in a wide variety of domains, ranging from
autonomous driving simulations to home robotics, from standard reinforcement
learning benchmarks to lower-body exoskeletons. | [
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] |
There has been a recent surge in the field of Explainable AI (XAI) which
tackles the problem of providing insights into the behavior of black-box
machine learning models. Within this field, \textit{feature attribution}
encompasses methods which assign relevance scores to input features and
visualize them as a heatmap. Designing flexible accelerators for multiple such
algorithms is challenging since the hardware mapping of these algorithms has
not been studied yet. In this work, we first analyze the dataflow of gradient
backpropagation based feature attribution algorithms to determine the resource
overhead required over inference. The gradient computation is optimized to
minimize the memory overhead. Second, we develop a High-Level Synthesis (HLS)
based configurable FPGA design that is targeted for edge devices and supports
three feature attribution algorithms. Tile based computation is employed to
maximally use on-chip resources while adhering to the resource constraints.
Representative CNNs are trained on CIFAR-10 dataset and implemented on multiple
Xilinx FPGAs using 16-bit fixed-point precision demonstrating flexibility of
our library. Finally, through efficient reuse of allocated hardware resources,
our design methodology demonstrates a pathway to repurpose inference
accelerators to support feature attribution with minimal overhead, thereby
enabling real-time XAI on the edge. | [
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As a booming research area in the past decade, deep learning technologies
have been driven by big data collected and processed on an unprecedented scale.
However, privacy concerns arise due to the potential leakage of sensitive
information from the training data. Recent research has revealed that deep
learning models are vulnerable to various privacy attacks, including membership
inference attacks, attribute inference attacks, and gradient inversion attacks.
Notably, the efficacy of these attacks varies from model to model. In this
paper, we answer a fundamental question: Does model architecture affect model
privacy? By investigating representative model architectures from CNNs to
Transformers, we demonstrate that Transformers generally exhibit higher
vulnerability to privacy attacks compared to CNNs. Additionally, We identify
the micro design of activation layers, stem layers, and LN layers, as major
factors contributing to the resilience of CNNs against privacy attacks, while
the presence of attention modules is another main factor that exacerbates the
privacy vulnerability of Transformers. Our discovery reveals valuable insights
for deep learning models to defend against privacy attacks and inspires the
research community to develop privacy-friendly model architectures. | [
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] |
Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent
years, with the application of new sensors and deep learning technologies
extending to the autonomous field, the development of autonomous driving
technology has continued to make breakthroughs. Thus, many carmakers and
high-tech giants dedicated to research and system development of autonomous
driving. However, as the foundation of autonomous driving, the deep learning
technology faces many new security risks. The academic community has proposed
deep learning countermeasures against the adversarial examples and AI backdoor,
and has introduced them into the autonomous driving field for verification.
Deep learning security matters to autonomous driving system security, and then
matters to personal safety, which is an issue that deserves attention and
research.This paper provides an summary of the concepts, developments and
recent research in deep learning security technologies in autonomous driving.
Firstly, we briefly introduce the deep learning framework and pipeline in the
autonomous driving system, which mainly include the deep learning technologies
and algorithms commonly used in this field. Moreover, we focus on the potential
security threats of the deep learning based autonomous driving system in each
functional layer in turn. We reviews the development of deep learning attack
technologies to autonomous driving, investigates the State-of-the-Art
algorithms, and reveals the potential risks. At last, we provides an outlook on
deep learning security in the autonomous driving field and proposes
recommendations for building a safe and trustworthy autonomous driving system. | [
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] |
Modeling interaction dynamics to generate robot trajectories that enable a
robot to adapt and react to a human's actions and intentions is critical for
efficient and effective collaborative Human-Robot Interactions (HRI). Learning
from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown
promising results, especially when coupled with representation learning
techniques. However, such methods for learning HRI either do not scale well to
high dimensional data or cannot accurately adapt to changing via-poses of the
interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD),
a method that couples deep representation learning and probabilistic machine
learning to address the problem of two-party physical HRIs. We learn the
interaction dynamics from demonstrations, using Hidden Semi-Markov Models
(HSMMs) to model the joint distribution of the interacting agents in the latent
space of a Variational Autoencoder (VAE). Our experimental evaluations for
learning HRI from HHI demonstrations show that MILD effectively captures the
multimodality in the latent representations of HRI tasks, allowing us to decode
the varying dynamics occurring in such tasks. Compared to related work, MILD
generates more accurate trajectories for the controlled agent (robot) when
conditioned on the observed agent's (human) trajectory. Notably, MILD can learn
directly from camera-based pose estimations to generate trajectories, which we
then map to a humanoid robot without the need for any additional training. | [
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] |
Federated Learning (FL) is a distributed learning paradigm that enables
different parties to train a model together for high quality and strong privacy
protection. In this scenario, individual participants may get compromised and
perform backdoor attacks by poisoning the data (or gradients). Existing work on
robust aggregation and certified FL robustness does not study how hardening
benign clients can affect the global model (and the malicious clients). In this
work, we theoretically analyze the connection among cross-entropy loss, attack
success rate, and clean accuracy in this setting. Moreover, we propose a
trigger reverse engineering based defense and show that our method can achieve
robustness improvement with guarantee (i.e., reducing the attack success rate)
without affecting benign accuracy. We conduct comprehensive experiments across
different datasets and attack settings. Our results on eight competing SOTA
defense methods show the empirical superiority of our method on both
single-shot and continuous FL backdoor attacks. Code is available at
https://github.com/KaiyuanZh/FLIP. | [
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] |
Behaviour biometrics are being explored as a viable alternative to overcome
the limitations of traditional authentication methods such as passwords and
static biometrics. Also, they are being considered as a viable authentication
method for IoT devices such as smart headsets with AR/VR capabilities,
wearables, and erables, that do not have a large form factor or the ability to
seamlessly interact with the user. Recent behavioural biometric solutions use
deep learning models that require large amounts of annotated training data.
Collecting such volumes of behaviour biometrics data raises privacy and
usability concerns. To this end, we propose using SimSiam-based non-contrastive
self-supervised learning to improve the label efficiency of behavioural
biometric systems. The key idea is to use large volumes of unlabelled (and
anonymised) data to build good feature extractors that can be subsequently used
in supervised settings. Using two EEG datasets, we show that at lower amounts
of labelled data, non-contrastive learning performs 4%-11% more than
conventional methods such as supervised learning and data augmentation. We also
show that, in general, self-supervised learning methods perform better than
other baselines. Finally, through careful experimentation, we show various
modifications that can be incorporated into the non-contrastive learning
process to archive high performance. | [
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] |
Traffic signal control has the potential to reduce congestion in dynamic
networks. Recent studies show that traffic signal control with reinforcement
learning (RL) methods can significantly reduce the average waiting time.
However, a shortcoming of existing methods is that they require model
retraining for new intersections with different structures. In this paper, we
propose a novel reinforcement learning approach with augmented data (ADLight)
to train a universal model for intersections with different structures. We
propose a new agent design incorporating features on movements and actions with
set current phase duration to allow the generalized model to have the same
structure for different intersections. A new data augmentation method named
\textit{movement shuffle} is developed to improve the generalization
performance. We also test the universal model with new intersections in
Simulation of Urban MObility (SUMO). The results show that the performance of
our approach is close to the models trained in a single environment directly
(only a 5% loss of average waiting time), and we can reduce more than 80% of
training time, which saves a lot of computational resources in scalable
operations of traffic lights. | [
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] |
Sequential Recommendation (SR) models user dynamics and predicts the next
preferred items based on the user history. Existing SR methods model the 'was
interacted before' item-item transitions observed in sequences, which can be
viewed as an item relationship. However, there are multiple auxiliary item
relationships, e.g., items from similar brands and with similar contents in
real-world scenarios. Auxiliary item relationships describe item-item
affinities in multiple different semantics and alleviate the long-lasting cold
start problem in the recommendation. However, it remains a significant
challenge to model auxiliary item relationships in SR.
To simultaneously model high-order item-item transitions in sequences and
auxiliary item relationships, we propose a Multi-relational Transformer capable
of modeling auxiliary item relationships for SR (MT4SR). Specifically, we
propose a novel self-attention module, which incorporates arbitrary item
relationships and weights item relationships accordingly. Second, we regularize
intra-sequence item relationships with a novel regularization module to
supervise attentions computations. Third, for inter-sequence item relationship
pairs, we introduce a novel inter-sequence related items modeling module.
Finally, we conduct experiments on four benchmark datasets and demonstrate the
effectiveness of MT4SR over state-of-the-art methods and the improvements on
the cold start problem. The code is available at
https://github.com/zfan20/MT4SR. | [
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] |
Proportional-integral-derivative (PID) control underlies more than $97\%$ of
automated industrial processes. Controlling these processes effectively with
respect to some specified set of performance goals requires finding an optimal
set of PID parameters to moderate the PID loop. Tuning these parameters is a
long and exhaustive process. A method (patent pending) based on deep
reinforcement learning is presented that learns a relationship between generic
system properties (e.g. resonance frequency), a multi-objective performance
goal and optimal PID parameter values. Performance is demonstrated in the
context of a real optical switching product of the foremost manufacturer of
such devices globally. Switching is handled by piezoelectric actuators where
switching time and optical loss are derived from the speed and stability of
actuator-control processes respectively. The method achieves a $5\times$
improvement in the number of actuators that fall within the most challenging
target switching speed, $\geq 20\%$ improvement in mean switching speed at the
same optical loss and $\geq 75\%$ reduction in performance inconsistency when
temperature varies between 5 and 73 degrees celcius. Furthermore, once trained
(which takes $\mathcal{O}(hours)$), the model generates actuator-unique PID
parameters in a one-shot inference process that takes $\mathcal{O}(ms)$ in
comparison to up to $\mathcal{O}(week)$ required for conventional tuning
methods, therefore accomplishing these performance improvements whilst
achieving up to a $10^6\times$ speed-up. After training, the method can be
applied entirely offline, incurring effectively zero optimisation-overhead in
production. | [
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] |
The behavior of malware threats is gradually increasing, heightened the need
for malware detection. However, existing malware detection methods only target
at the existing malicious samples, the detection of fresh malicious code and
variants of malicious code is limited. In this paper, we propose a novel scheme
that detects malware and its variants efficiently. Based on the idea of the
generative adversarial networks (GANs), we obtain the `true' sample
distribution that satisfies the characteristics of the real malware, use them
to deceive the discriminator, thus achieve the defense against malicious code
attacks and improve malware detection. Firstly, a new Android malware APK to
image texture feature extraction segmentation method is proposed, which is
called segment self-growing texture segmentation algorithm. Secondly, tensor
singular value decomposition (tSVD) based on the low-tubal rank transforms
malicious features with different sizes into a fixed third-order tensor
uniformly, which is entered into the neural network for training and learning.
Finally, a flexible Android malware detection model based on GANs with code
tensor (MTFD-GANs) is proposed. Experiments show that the proposed model can
generally surpass the traditional malware detection model, with a maximum
improvement efficiency of 41.6\%. At the same time, the newly generated samples
of the GANs generator greatly enrich the sample diversity. And retraining
malware detector can effectively improve the detection efficiency and
robustness of traditional models. | [
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] |
We consider the problem of simultaneous learning in stochastic games with
many players in the finite-horizon setting. While the typical target solution
for a stochastic game is a Nash equilibrium, this is intractable with many
players. We instead focus on variants of {\it correlated equilibria}, such as
those studied for extensive-form games. We begin with a hardness result for the
adversarial MDP problem: even for a horizon of 3, obtaining sublinear regret
against the best non-stationary policy is \textsf{NP}-hard when both rewards
and transitions are adversarial. This implies that convergence to even the
weakest natural solution concept -- normal-form coarse correlated equilbrium --
is not possible via black-box reduction to a no-regret algorithm even in
stochastic games with constant horizon (unless
$\textsf{NP}\subseteq\textsf{BPP}$). Instead, we turn to a different target:
algorithms which {\it generate} an equilibrium when they are used by all
players. Our main result is algorithm which generates an {\it extensive-form}
correlated equilibrium, whose runtime is exponential in the horizon but
polynomial in all other parameters. We give a similar algorithm which is
polynomial in all parameters for "fast-mixing" stochastic games. We also show a
method for efficiently reaching normal-form coarse correlated equilibria in
"single-controller" stochastic games which follows the traditional no-regret
approach. When shared randomness is available, the two generative algorithms
can be extended to give simultaneous regret bounds and converge in the
traditional sense. | [
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] |
Radio based positioning of a user equipment (UE) based on deep learning (DL)
methods using channel state information (CSI) fingerprints have shown promising
results. DL models are able to capture complex properties embedded in the CSI
about a particular environment and map UE's CSI to the UE's position. However,
the CSI fingerprints and the DL models trained on such fingerprints are highly
dependent on a particular propagation environment, which generally limits the
transfer of knowledge of the DL models from one environment to another. In this
paper, we propose a DL model consisting of two parts: the first part aims to
learn environment independent features while the second part combines those
features depending on the particular environment. To improve transfer learning,
we propose a meta learning scheme for training the first part over multiple
environments. We show that for positioning in a new environment, initializing a
DL model with the meta learned environment independent function achieves higher
UE positioning accuracy compared to regular transfer learning from one
environment to the new environment, or compared to training the DL model from
scratch with only fingerprints from the new environment. Our proposed scheme is
able to create an environment independent function which can embed knowledge
from multiple environments and more effectively learn from a new environment. | [
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Despite recent advances in Graph Neural Networks (GNNs), their training
strategies remain largely under-explored. The conventional training strategy
learns over all nodes in the original graph(s) equally, which can be
sub-optimal as certain nodes are often more difficult to learn than others.
Here we present TuneUp, a simple curriculum-based training strategy for
improving the predictive performance of GNNs. TuneUp trains a GNN in two
stages. In the first stage, TuneUp applies conventional training to obtain a
strong base GNN. The base GNN tends to perform well on head nodes (nodes with
large degrees) but less so on tail nodes (nodes with small degrees). Therefore,
the second stage of TuneUp focuses on improving prediction on the difficult
tail nodes by further training the base GNN on synthetically generated tail
node data. We theoretically analyze TuneUp and show it provably improves
generalization performance on tail nodes. TuneUp is simple to implement and
applicable to a broad range of GNN architectures and prediction tasks.
Extensive evaluation of TuneUp on five diverse GNN architectures, three types
of prediction tasks, and both transductive and inductive settings shows that
TuneUp significantly improves the performance of the base GNN on tail nodes,
while often even improving the performance on head nodes. Altogether, TuneUp
produces up to 57.6% and 92.2% relative predictive performance improvement in
the transductive and the challenging inductive settings, respectively. | [
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] |
Deep learning-based adversarial malware detectors have yielded promising
results in detecting never-before-seen malware executables without relying on
expensive dynamic behavior analysis and sandbox. Despite their abilities, these
detectors have been shown to be vulnerable to adversarial malware variants -
meticulously modified, functionality-preserving versions of original malware
executables generated by machine learning. Due to the nature of these
adversarial modifications, these adversarial methods often use a \textit{single
view} of malware executables (i.e., the binary/hexadecimal view) to generate
adversarial malware variants. This provides an opportunity for the defenders
(i.e., malware detectors) to detect the adversarial variants by utilizing more
than one view of a malware file (e.g., source code view in addition to the
binary view). The rationale behind this idea is that while the adversary
focuses on the binary view, certain characteristics of the malware file in the
source code view remain untouched which leads to the detection of the
adversarial malware variants. To capitalize on this opportunity, we propose
Adversarially Robust Multiview Malware Defense (ARMD), a novel multi-view
learning framework to improve the robustness of DL-based malware detectors
against adversarial variants. Our experiments on three renowned open-source
deep learning-based malware detectors across six common malware categories show
that ARMD is able to improve the adversarial robustness by up to seven times on
these malware detectors. | [
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] |
How can we recommend existing bundles to users accurately? How can we
generate new tailored bundles for users? Recommending a bundle, or a group of
various items, has attracted widespread attention in e-commerce owing to the
increased satisfaction of both users and providers. Bundle matching and bundle
generation are two representative tasks in bundle recommendation. The bundle
matching task is to correctly match existing bundles to users while the bundle
generation is to generate new bundles that users would prefer. Although many
recent works have developed bundle recommendation models, they fail to achieve
high accuracy since they do not handle heterogeneous data effectively and do
not learn a method for customized bundle generation. In this paper, we propose
BundleMage, an accurate approach for bundle matching and generation. BundleMage
effectively mixes user preferences of items and bundles using an adaptive gate
technique to achieve high accuracy for the bundle matching. BundleMage also
generates a personalized bundle by learning a generation module that exploits a
user preference and the characteristic of a given incomplete bundle to be
completed. BundleMage further improves its performance using multi-task
learning with partially shared parameters. Through extensive experiments, we
show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and
6.3x higher nDCG in bundle generation than the best competitors. We also
provide qualitative analysis that BundleMage effectively generates bundles
considering both the tastes of users and the characteristics of target bundles. | [
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] |
In this paper, we propose a Joint Semantic Transfer Network (JSTN) towards
effective intrusion detection for large-scale scarcely labelled IoT domain. As
a multi-source heterogeneous domain adaptation (MS-HDA) method, the JSTN
integrates a knowledge rich network intrusion (NI) domain and another
small-scale IoT intrusion (II) domain as source domains, and preserves
intrinsic semantic properties to assist target II domain intrusion detection.
The JSTN jointly transfers the following three semantics to learn a
domain-invariant and discriminative feature representation. The scenario
semantic endows source NI and II domain with characteristics from each other to
ease the knowledge transfer process via a confused domain discriminator and
categorical distribution knowledge preservation. It also reduces the
source-target discrepancy to make the shared feature space domain-invariant.
Meanwhile, the weighted implicit semantic transfer boosts discriminability via
a fine-grained knowledge preservation, which transfers the source categorical
distribution to the target domain. The source-target divergence guides the
importance weighting during knowledge preservation to reflect the degree of
knowledge learning. Additionally, the hierarchical explicit semantic alignment
performs centroid-level and representative-level alignment with the help of a
geometric similarity-aware pseudo-label refiner, which exploits the value of
unlabelled target II domain and explicitly aligns feature representations from
a global and local perspective in a concentrated manner. Comprehensive
experiments on various tasks verify the superiority of the JSTN against
state-of-the-art comparing methods, on average a 10.3% of accuracy boost is
achieved. The statistical soundness of each constituting component and the
computational efficiency are also verified. | [
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] |
In this work, we propose a self-improving artificial intelligence system to
enhance the safety performance of reinforcement learning (RL)-based autonomous
driving (AD) agents using black-box verification methods. RL algorithms have
become popular in AD applications in recent years. However, the performance of
existing RL algorithms heavily depends on the diversity of training scenarios.
A lack of safety-critical scenarios during the training phase could result in
poor generalization performance in real-world driving applications. We propose
a novel framework in which the weaknesses of the training set are explored
through black-box verification methods. After discovering AD failure scenarios,
the RL agent's training is re-initiated via transfer learning to improve the
performance of previously unsafe scenarios. Simulation results demonstrate that
our approach efficiently discovers safety failures of action decisions in
RL-based adaptive cruise control (ACC) applications and significantly reduces
the number of vehicle collisions through iterative applications of our method.
The source code is publicly available at
https://github.com/data-and-decision-lab/self-improving-RL. | [
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] |
In the past few years, Softmax has become a common component in neural
network frameworks. In this paper, a gradient decay hyperparameter is
introduced in Softmax to control the probability-dependent gradient decay rate
during training. By following the theoretical analysis and empirical results of
a variety of model architectures trained on MNIST, CIFAR-10/100 and SVHN, we
find that the generalization performance depends significantly on the gradient
decay rate as the confidence probability rises, i.e., the gradient decreases
convexly or concavely as the sample probability increases. Moreover,
optimization with the small gradient decay shows a similar curriculum learning
sequence where hard samples are in the spotlight only after easy samples are
convinced sufficiently, and well-separated samples gain a higher gradient to
reduce intra-class distance. Based on the analysis results, we can provide
evidence that the large margin Softmax will affect the local Lipschitz
constraint of the loss function by regulating the probability-dependent
gradient decay rate. This paper provides a new perspective and understanding of
the relationship among concepts of large margin Softmax, local Lipschitz
constraint and curriculum learning by analyzing the gradient decay rate.
Besides, we propose a warm-up strategy to dynamically adjust Softmax loss in
training, where the gradient decay rate increases from over-small to speed up
the convergence rate. | [
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] |
GNN-based methods have achieved excellent results as a mainstream task in
drug response prediction tasks in recent years. Traditional GNN methods use
only the atoms in a drug molecule as nodes to obtain the representation of the
molecular graph through node information passing, whereas the method using the
transformer can only extract information about the nodes. However, the covalent
bonding and chirality of a drug molecule have a great influence on the
pharmacological properties of the molecule, and these information are implied
in the chemical bonds formed by the edges between the atoms. In addition, CNN
methods for modelling cell lines genomics sequences can only perceive local
rather than global information about the sequence. In order to solve the above
problems, we propose the decoupled dual transformer structure with edge
embedded for drug respond prediction (TransEDRP), which is used for the
representation of cell line genomics and drug respectively. For the drug
branch, we encoded the chemical bond information within the molecule as the
embedding of the edge in the molecular graph, extracted the global structural
and biochemical information of the drug molecule using graph transformer. For
the branch of cell lines genomics, we use the multi-headed attention mechanism
to globally represent the genomics sequence. Finally, the drug and genomics
branches are fused to predict IC50 values through the transformer layer and the
fully connected layer, which two branches are different modalities. Extensive
experiments have shown that our method is better than the current mainstream
approach in all evaluation indicators. | [
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