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Due to their decentralized nature, federated learning (FL) systems have an
inherent vulnerability during their training to adversarial backdoor attacks.
In this type of attack, the goal of the attacker is to use poisoned updates to
implant so-called backdoors into the learned model such that, at test time, the
model's outputs can be fixed to a given target for certain inputs. (As a simple
toy example, if a user types "people from New York" into a mobile keyboard app
that uses a backdoored next word prediction model, then the model could
autocomplete the sentence to "people from New York are rude"). Prior work has
shown that backdoors can be inserted into FL models, but these backdoors are
often not durable, i.e., they do not remain in the model after the attacker
stops uploading poisoned updates. Thus, since training typically continues
progressively in production FL systems, an inserted backdoor may not survive
until deployment. Here, we propose Neurotoxin, a simple one-line modification
to existing backdoor attacks that acts by attacking parameters that are changed
less in magnitude during training. We conduct an exhaustive evaluation across
ten natural language processing and computer vision tasks, and we find that we
can double the durability of state of the art backdoors. | [
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] |
A large number of people suffer from life-threatening cardiac abnormalities,
and electrocardiogram (ECG) analysis is beneficial to determining whether an
individual is at risk of such abnormalities. Automatic ECG classification
methods, especially the deep learning based ones, have been proposed to detect
cardiac abnormalities using ECG records, showing good potential to improve
clinical diagnosis and help early prevention of cardiovascular diseases.
However, the predictions of the known neural networks still do not
satisfactorily meet the needs of clinicians, and this phenomenon suggests that
some information used in clinical diagnosis may not be well captured and
utilized by these methods. In this paper, we introduce some rules into
convolutional neural networks, which help present clinical knowledge to deep
learning based ECG analysis, in order to improve automated ECG diagnosis
performance. Specifically, we propose a Handcrafted-Rule-enhanced Neural
Network (called HRNN) for ECG classification with standard 12-lead ECG input,
which consists of a rule inference module and a deep learning module.
Experiments on two large-scale public ECG datasets show that our new approach
considerably outperforms existing state-of-the-art methods. Further, our
proposed approach not only can improve the diagnosis performance, but also can
assist in detecting mislabelled ECG samples. Our codes are available at
https://github.com/alwaysbyx/ecg_processing. | [
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] |
In this paper, we introduce Jointist, an instrument-aware multi-instrument
framework that is capable of transcribing, recognizing, and separating multiple
musical instruments from an audio clip. Jointist consists of the instrument
recognition module that conditions the other modules: the transcription module
that outputs instrument-specific piano rolls, and the source separation module
that utilizes instrument information and transcription results.
The instrument conditioning is designed for an explicit multi-instrument
functionality while the connection between the transcription and source
separation modules is for better transcription performance. Our challenging
problem formulation makes the model highly useful in the real world given that
modern popular music typically consists of multiple instruments. However, its
novelty necessitates a new perspective on how to evaluate such a model. During
the experiment, we assess the model from various aspects, providing a new
evaluation perspective for multi-instrument transcription. We also argue that
transcription models can be utilized as a preprocessing module for other music
analysis tasks. In the experiment on several downstream tasks, the symbolic
representation provided by our transcription model turned out to be helpful to
spectrograms in solving downbeat detection, chord recognition, and key
estimation. | [
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] |
Purpose : Because functional MRI (fMRI) data sets are in general small, we
sought a data efficient approach to resting state fMRI classification of autism
spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesized that
a Deep Reinforcement Learning (DRL) classifier could learn effectively on a
small fMRI training set.
Methods : We trained a Deep Reinforcement Learning (DRL) classifier on 100
graph-label pairs from the Autism Brain Imaging Data Exchange (ABIDE) database.
For comparison, we trained a Supervised Deep Learning (SDL) classifier on the
same training set.
Results : DRL significantly outperformed SDL, with a p-value of 2.4 x
10^(-7). DRL achieved superior results for a variety of classifier performance
metrics, including an F1 score of 76, versus 67 for SDL. Whereas SDL quickly
overfit the training data, DRL learned in a progressive manner that generalised
to the separate testing set.
Conclusion : DRL can learn to classify ASD versus NT in a data efficient
manner, doing so for a small training set. Future work will involve optimizing
the neural network for data efficiency and applying the approach to other fMRI
data sets, namely for brain cancer patients. | [
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] |
The advent of noisy intermediate-scale quantum computers has put the search
for possible applications to the forefront of quantum information science. One
area where hopes for an advantage through near-term quantum computers are high
is quantum machine learning, where variational quantum learning models based on
parametrized quantum circuits are discussed. In this work, we introduce the
concept of a classical surrogate, a classical model which can be efficiently
obtained from a trained quantum learning model and reproduces its input-output
relations. As inference can be performed classically, the existence of a
classical surrogate greatly enhances the applicability of a quantum learning
strategy. However, the classical surrogate also challenges possible advantages
of quantum schemes. As it is possible to directly optimize the ansatz of the
classical surrogate, they create a natural benchmark the quantum model has to
outperform. We show that large classes of well-analyzed re-uploading models
have a classical surrogate. We conducted numerical experiments and found that
these quantum models show no advantage in performance or trainability in the
problems we analyze. This leaves only generalization capability as possible
point of quantum advantage and emphasizes the dire need for a better
understanding of inductive biases of quantum learning models. | [
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] |
While other areas of machine learning have seen more and more automation,
designing a high-performing recommender system still requires a high level of
human effort. Furthermore, recent work has shown that modern recommender system
algorithms do not always improve over well-tuned baselines. A natural follow-up
question is, "how do we choose the right algorithm for a new dataset and
performance metric?" In this work, we start by giving the first large-scale
study of recommender system approaches by comparing 18 algorithms and 100 sets
of hyperparameters across 85 datasets and 315 metrics. We find that the best
algorithms and hyperparameters are highly dependent on the dataset and
performance metric, however, there are also strong correlations between the
performance of each algorithm and various meta-features of the datasets.
Motivated by these findings, we create RecZilla, a meta-learning approach to
recommender systems that uses a model to predict the best algorithm and
hyperparameters for new, unseen datasets. By using far more meta-training data
than prior work, RecZilla is able to substantially reduce the level of human
involvement when faced with a new recommender system application. We not only
release our code and pretrained RecZilla models, but also all of our raw
experimental results, so that practitioners can train a RecZilla model for
their desired performance metric: https://github.com/naszilla/reczilla. | [
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] |
Wasserstein distance, which measures the discrepancy between distributions,
shows efficacy in various types of natural language processing (NLP) and
computer vision (CV) applications. One of the challenges in estimating
Wasserstein distance is that it is computationally expensive and does not scale
well for many distribution comparison tasks. In this paper, we aim to
approximate the 1-Wasserstein distance by the tree-Wasserstein distance (TWD),
where TWD is a 1-Wasserstein distance with tree-based embedding and can be
computed in linear time with respect to the number of nodes on a tree. More
specifically, we propose a simple yet efficient L1-regularized approach to
learning the weights of the edges in a tree. To this end, we first show that
the 1-Wasserstein approximation problem can be formulated as a distance
approximation problem using the shortest path distance on a tree. We then show
that the shortest path distance can be represented by a linear model and can be
formulated as a Lasso-based regression problem. Owing to the convex
formulation, we can obtain a globally optimal solution efficiently. Moreover,
we propose a tree-sliced variant of these methods. Through experiments, we
demonstrated that the weighted TWD can accurately approximate the original
1-Wasserstein distance. | [
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] |
This paper proposes an effective and novel multiagent deep reinforcement
learning (MADRL)-based method for solving the joint virtual network function
(VNF) placement and routing (P&R), where multiple service requests with
differentiated demands are delivered at the same time. The differentiated
demands of the service requests are reflected by their delay- and
cost-sensitive factors. We first construct a VNF P&R problem to jointly
minimize a weighted sum of service delay and resource consumption cost, which
is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative
subtasks: placement subtask and routing subtask. Each subtask consists of
multiple concurrent parallel sequential decision processes. By invoking the
deep deterministic policy gradient method and multi-agent technique, an
MADRL-P&R framework is designed to perform the two subtasks. The new joint
reward and internal rewards mechanism is proposed to match the goals and
constraints of the placement and routing subtasks. We also propose the
parameter migration-based model-retraining method to deal with changing network
topologies. Corroborated by experiments, the proposed MADRL-P&R framework is
superior to its alternatives in terms of service cost and delay, and offers
higher flexibility for personalized service demands. The parameter
migration-based model-retraining method can efficiently accelerate convergence
under moderate network topology changes. | [
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] |
Equipping current decision-making tools with notions of fairness,
equitability, or other ethically motivated outcomes, is one of the top
priorities in recent research efforts in machine learning, AI, and
optimization. In this paper, we investigate how to allocate limited resources
to {locally interacting} communities in a way to maximize a pertinent notion of
equitability. In particular, we look at the dynamic setting where the
allocation is repeated across multiple periods (e.g., yearly), the local
communities evolve in the meantime (driven by the provided allocation), and the
allocations are modulated by feedback coming from the communities themselves.
We employ recent mathematical tools stemming from data-driven feedback online
optimization, by which communities can learn their (possibly unknown)
evolution, satisfaction, as well as they can share information with the
deciding bodies. We design dynamic policies that converge to an allocation that
maximize equitability in the long term. We further demonstrate our model and
methodology with realistic examples of healthcare and education subsidies
design in Sub-Saharian countries. One of the key empirical takeaways from our
setting is that long-term equitability is fragile, in the sense that it can be
easily lost when deciding bodies weigh in other factors (e.g., equality in
allocation) in the allocation strategy. Moreover, a naive compromise, while not
providing significant advantage to the communities, can promote inequality in
social outcomes. | [
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] |
Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in
the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting
all DDIs is a challenging and critical problem. Most existing computational
models integrate drug-centric information from different sources and leverage
them as features in machine learning classifiers to predict DDIs. However,
these models have a high chance of failure, especially for the new drugs when
all the information is not available. This paper proposes a novel Hypergraph
Neural Network (HyGNN) model based on only the SMILES string of drugs,
available for any drug, for the DDI prediction problem. To capture the drug
similarities, we create a hypergraph from drugs' chemical substructures
extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel
attention-based hypergraph edge encoder to get the representation of drugs as
hyperedges and a decoder to predict the interactions between drug pairs.
Furthermore, we conduct extensive experiments to evaluate our model and compare
it with several state-of-the-art methods. Experimental results demonstrate that
our proposed HyGNN model effectively predicts DDIs and impressively outperforms
the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%,
respectively. | [
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] |
With the rapid technological advancement, security has become a major issue
due to the increase in malware activity that poses a serious threat to the
security and safety of both computer systems and stakeholders. To maintain
stakeholders, particularly, end users security, protecting the data from
fraudulent efforts is one of the most pressing concerns. A set of malicious
programming code, scripts, active content, or intrusive software that is
designed to destroy intended computer systems and programs or mobile and web
applications is referred to as malware. According to a study, naive users are
unable to distinguish between malicious and benign applications. Thus, computer
systems and mobile applications should be designed to detect malicious
activities towards protecting the stakeholders. A number of algorithms are
available to detect malware activities by utilizing novel concepts including
Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we
emphasize Artificial Intelligence (AI) based techniques for detecting and
preventing malware activity. We present a detailed review of current malware
detection technologies, their shortcomings, and ways to improve efficiency. Our
study shows that adopting futuristic approaches for the development of malware
detection applications shall provide significant advantages. The comprehension
of this synthesis shall help researchers for further research on malware
detection and prevention using AI. | [
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] |
This report examines what I see as the core argument for concern about
existential risk from misaligned artificial intelligence. I proceed in two
stages. First, I lay out a backdrop picture that informs such concern. On this
picture, intelligent agency is an extremely powerful force, and creating agents
much more intelligent than us is playing with fire -- especially given that if
their objectives are problematic, such agents would plausibly have instrumental
incentives to seek power over humans. Second, I formulate and evaluate a more
specific six-premise argument that creating agents of this kind will lead to
existential catastrophe by 2070. On this argument, by 2070: (1) it will become
possible and financially feasible to build relevantly powerful and agentic AI
systems; (2) there will be strong incentives to do so; (3) it will be much
harder to build aligned (and relevantly powerful/agentic) AI systems than to
build misaligned (and relevantly powerful/agentic) AI systems that are still
superficially attractive to deploy; (4) some such misaligned systems will seek
power over humans in high-impact ways; (5) this problem will scale to the full
disempowerment of humanity; and (6) such disempowerment will constitute an
existential catastrophe. I assign rough subjective credences to the premises in
this argument, and I end up with an overall estimate of ~5% that an existential
catastrophe of this kind will occur by 2070. (May 2022 update: since making
this report public in April 2021, my estimate here has gone up, and is now at
>10%.) | [
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] |
SATNet is a differentiable constraint solver with a custom backpropagation
algorithm, which can be used as a layer in a deep-learning system. It is a
promising proposal for bridging deep learning and logical reasoning. In fact,
SATNet has been successfully applied to learn, among others, the rules of a
complex logical puzzle, such as Sudoku, just from input and output pairs where
inputs are given as images. In this paper, we show how to improve the learning
of SATNet by exploiting symmetries in the target rules of a given but unknown
logical puzzle or more generally a logical formula. We present SymSATNet, a
variant of SATNet that translates the given symmetries of the target rules to a
condition on the parameters of SATNet and requires that the parameters should
have a particular parametric form that guarantees the condition. The
requirement dramatically reduces the number of parameters to learn for the
rules with enough symmetries, and makes the parameter learning of SymSATNet
much easier than that of SATNet. We also describe a technique for automatically
discovering symmetries of the target rules from examples. Our experiments with
Sudoku and Rubik's cube show the substantial improvement of SymSATNet over the
baseline SATNet. | [
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] |
In this paper, we present a novel insider attack called Matryoshka, which
employs an irrelevant scheduled-to-publish DNN model as a carrier model for
covert transmission of multiple secret models which memorize the functionality
of private ML data stored in local data centers. Instead of treating the
parameters of the carrier model as bit strings and applying conventional
steganography, we devise a novel parameter sharing approach which exploits the
learning capacity of the carrier model for information hiding. Matryoshka
simultaneously achieves: (i) High Capacity -- With almost no utility loss of
the carrier model, Matryoshka can hide a 26x larger secret model or 8 secret
models of diverse architectures spanning different application domains in the
carrier model, neither of which can be done with existing steganography
techniques; (ii) Decoding Efficiency -- once downloading the published carrier
model, an outside colluder can exclusively decode the hidden models from the
carrier model with only several integer secrets and the knowledge of the hidden
model architecture; (iii) Effectiveness -- Moreover, almost all the recovered
models have similar performance as if it were trained independently on the
private data; (iv) Robustness -- Information redundancy is naturally
implemented to achieve resilience against common post-processing techniques on
the carrier before its publishing; (v) Covertness -- A model inspector with
different levels of prior knowledge could hardly differentiate a carrier model
from a normal model. | [
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The large majority of human activities require collaborations within and
across formal or informal teams. Our understanding of how the collaborative
efforts spent by teams relate to their performance is still a matter of debate.
Teamwork results in a highly interconnected ecosystem of potentially
overlapping components where tasks are performed in interaction with team
members and across other teams. To tackle this problem, we propose a graph
neural network model designed to predict a team's performance while identifying
the drivers that determine such an outcome. In particular, the model is based
on three architectural channels: topological, centrality, and contextual which
capture different factors potentially shaping teams' success. We endow the
model with two attention mechanisms to boost model performance and allow
interpretability. A first mechanism allows pinpointing key members inside the
team. A second mechanism allows us to quantify the contributions of the three
driver effects in determining the outcome performance. We test model
performance on a wide range of domains outperforming most of the classical and
neural baselines considered. Moreover, we include synthetic datasets
specifically designed to validate how the model disentangles the intended
properties on which our model vastly outperforms baselines. | [
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] |
Cognitive biases are mental shortcuts humans use in dealing with information
and the environment, and which result in biased actions and behaviors (or,
actions), unbeknownst to themselves. Biases take many forms, with cognitive
biases occupying a central role that inflicts fairness, accountability,
transparency, ethics, law, medicine, and discrimination. Detection of biases is
considered a necessary step toward their mitigation. Herein, we focus on two
cognitive biases - anchoring and recency. The recognition of cognitive bias in
computer science is largely in the domain of information retrieval, and bias is
identified at an aggregate level with the help of annotated data. Proposing a
different direction for bias detection, we offer a principled approach along
with Machine Learning to detect these two cognitive biases from Web logs of
users' actions. Our individual user level detection makes it truly
personalized, and does not rely on annotated data. Instead, we start with two
basic principles established in cognitive psychology, use modified training of
an attention network, and interpret attention weights in a novel way according
to those principles, to infer and distinguish between these two biases. The
personalized approach allows detection for specific users who are susceptible
to these biases when performing their tasks, and can help build awareness among
them so as to undertake bias mitigation. | [
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] |
Timely and reliable detection of falls is a large and rapidly growing field
of research due to the medical and financial demand of caring for a constantly
growing elderly population. Within the past 2 decades, the availability of
high-quality hardware (high-quality sensors and AI microchips) and software
(machine learning algorithms) technologies has served as a catalyst for this
research by giving developers the capabilities to develop such systems. This
study developed multiple application components in order to investigate the
development challenges and choices for fall detection systems, and provide
materials for future research. The smart application developed using this
methodology was validated by the results from fall detection modelling
experiments and model mobile deployment. The best performing model overall was
the ResNet152 on a standardised, and shuffled dataset with a 2s window size
which achieved 92.8% AUC, 87.28% sensitivity, and 98.33% specificity. Given
these results it is evident that accelerometer and ECG sensors are beneficial
for fall detection, and allow for the discrimination between falls and other
activities. This study leaves a significant amount of room for improvement due
to weaknesses identified in the resultant dataset. These improvements include
using a labelling protocol for the critical phase of a fall, increasing the
number of dataset samples, improving the test subject representation, and
experimenting with frequency domain preprocessing. | [
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] |
Machine learning is a field of artificial intelligence (AI) that is becoming
essential for several critical systems, making it a good target for threat
actors. Threat actors exploit different Tactics, Techniques, and Procedures
(TTPs) against the confidentiality, integrity, and availability of Machine
Learning (ML) systems. During the ML cycle, they exploit adversarial TTPs to
poison data and fool ML-based systems. In recent years, multiple security
practices have been proposed for traditional systems but they are not enough to
cope with the nature of ML-based systems. In this paper, we conduct an
empirical study of threats reported against ML-based systems with the aim to
understand and characterize the nature of ML threats and identify common
mitigation strategies. The study is based on 89 real-world ML attack scenarios
from the MITRE's ATLAS database, the AI Incident Database, and the literature;
854 ML repositories from the GitHub search and the Python Packaging Advisory
database, selected based on their reputation. Attacks from the AI Incident
Database and the literature are used to identify vulnerabilities and new types
of threats that were not documented in ATLAS. Results show that convolutional
neural networks were one of the most targeted models among the attack
scenarios. ML repositories with the largest vulnerability prominence include
TensorFlow, OpenCV, and Notebook. In this paper, we also report the most
frequent vulnerabilities in the studied ML repositories, the most targeted ML
phases and models, the most used TTPs in ML phases and attack scenarios. This
information is particularly important for red/blue teams to better conduct
attacks/defenses, for practitioners to prevent threats during ML development,
and for researchers to develop efficient defense mechanisms. | [
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] |
Vertical federated learning (VFL) is attracting much attention because it
enables cross-silo data cooperation in a privacy-preserving manner. While most
research works in VFL focus on linear and tree models, deep models (e.g.,
neural networks) are not well studied in VFL. In this paper, we focus on
SplitNN, a well-known neural network framework in VFL, and identify a trade-off
between data security and model performance in SplitNN. Briefly, SplitNN trains
the model by exchanging gradients and transformed data. On the one hand,
SplitNN suffers from the loss of model performance since multiply parties
jointly train the model using transformed data instead of raw data, and a large
amount of low-level feature information is discarded. On the other hand, a
naive solution of increasing the model performance through aggregating at lower
layers in SplitNN (i.e., the data is less transformed and more low-level
feature is preserved) makes raw data vulnerable to inference attacks. To
mitigate the above trade-off, we propose a new neural network protocol in VFL
called Security Forward Aggregation (SFA). It changes the way of aggregating
the transformed data and adopts removable masks to protect the raw data.
Experiment results show that networks with SFA achieve both data security and
high model performance. | [
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] |
A Generative Adversarial Network (GAN) is a deep-learning generative model in
the field of Machine Learning (ML) that involves training two Neural Networks
(NN) using a sizable data set. In certain fields, such as medicine, the
training data may be hospital patient records that are stored across different
hospitals. The classic centralized approach would involve sending the data to a
centralized server where the model would be trained. However, that would
involve breaching the privacy and confidentiality of the patients and their
data, which would be unacceptable. Therefore, Federated Learning (FL), an ML
technique that trains ML models in a distributed setting without data ever
leaving the host device, would be a better alternative to the centralized
option. In this ML technique, only parameters and certain metadata would be
communicated. In spite of that, there still exist attacks that can infer user
data using the parameters and metadata. A fully privacy-preserving solution
involves homomorphically encrypting (HE) the data communicated. This paper will
focus on the performance loss of training an FL-GAN with three different types
of Homomorphic Encryption: Partial Homomorphic Encryption (PHE), Somewhat
Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). We will
also test the performance loss of Multi-Party Computations (MPC), as it has
homomorphic properties. The performances will be compared to the performance of
training an FL-GAN without encryption as well. Our experiments show that the
more complex the encryption method is, the longer it takes, with the extra time
taken for HE is quite significant in comparison to the base case of FL. | [
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Across a multitude of work environments, expert knowledge is imperative for
humans to conduct tasks with high performance and ensure business success.
These humans possess task-specific expert knowledge (TSEK) and hence, represent
subject matter experts (SMEs). However, not only demographic changes but also
personnel downsizing strategies lead and will continue to lead to departures of
SMEs within organizations, which constitutes the challenge of how to retain
that expert knowledge and train novices to keep the competitive advantage
elicited by that expert knowledge. SMEs training novices is time- and
cost-intensive, which intensifies the need for alternatives. Human-AI
collaboration (HAIC) poses a way out of this dilemma, facilitating alternatives
to preserve expert knowledge and teach it to novices for tasks conducted by
SMEs beforehand. In this workshop paper, we (1) propose a framework on how HAIC
can be utilized to train novices on particular tasks, (2) illustrate the role
of explicit and tacit knowledge in this training process via HAIC, and (3)
outline a preliminary experiment design to assess the ability of AI systems in
HAIC to act as a trainer to transfer TSEK to novices who do not possess prior
TSEK. | [
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] |
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and
Reusable) principles for scientific data is transforming the state-of-practice
for data management and stewardship, supporting and enabling discovery and
innovation. Learning from this initiative, and acknowledging the impact of
artificial intelligence (AI) in the practice of science and engineering, we
introduce a set of practical, concise, and measurable FAIR principles for AI
models. We showcase how to create and share FAIR data and AI models within a
unified computational framework combining the following elements: the Advanced
Photon Source at Argonne National Laboratory, the Materials Data Facility, the
Data and Learning Hub for Science, and funcX, and the Argonne Leadership
Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the
SambaNova DataScale system at the ALCF AI Testbed. We describe how this
domain-agnostic computational framework may be harnessed to enable autonomous
AI-driven discovery. | [
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] |
The explanation to an AI model's prediction used to support decision making
in cyber security, is of critical importance. It is especially so when the
model's incorrect prediction can lead to severe damages or even losses to lives
and critical assets. However, most existing AI models lack the ability to
provide explanations on their prediction results, despite their strong
performance in most scenarios. In this work, we propose a novel explainable AI
method, called PhilaeX, that provides the heuristic means to identify the
optimized subset of features to form the complete explanations of AI models'
predictions. It identifies the features that lead to the model's borderline
prediction, and those with positive individual contributions are extracted. The
feature attributions are then quantified through the optimization of a Ridge
regression model. We verify the explanation fidelity through two experiments.
First, we assess our method's capability in correctly identifying the activated
features in the adversarial samples of Android malwares, through the features
attribution values from PhilaeX. Second, the deduction and augmentation tests,
are used to assess the fidelity of the explanations. The results show that
PhilaeX is able to explain different types of classifiers correctly, with
higher fidelity explanations, compared to the state-of-the-arts methods such as
LIME and SHAP. | [
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Nowadays wireless communication is rapidly reshaping entire industry sectors.
In particular, mobile edge computing (MEC) as an enabling technology for
industrial Internet of things (IIoT) brings powerful computing/storage
infrastructure closer to the mobile terminals and, thereby, significant lowers
the response latency. To reap the benefit of proactive caching at the network
edge, precise knowledge on the popularity pattern among the end devices is
essential. However, the complex and dynamic nature of the content popularity
over space and time as well as the data-privacy requirements in many IIoT
scenarios pose tough challenges to its acquisition. In this article, we propose
an unsupervised and privacy-preserving popularity prediction framework for
MEC-enabled IIoT. The concepts of local and global popularities are introduced
and the time-varying popularity of each user is modelled as a model-free Markov
chain. On this basis, a novel unsupervised recurrent federated learning (URFL)
algorithm is proposed to predict the distributed popularity while achieve
privacy preservation and unsupervised training. Simulations indicate that the
proposed framework can enhance the prediction accuracy in terms of a reduced
root-mean-squared error by up to $60.5\%-68.7\%$. Additionally, manual labeling
and violation of users' data privacy are both avoided. | [
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] |
Human brains lie at the core of complex neurobiological systems, where the
neurons, circuits, and subsystems interact in enigmatic ways. Understanding the
structural and functional mechanisms of the brain has long been an intriguing
pursuit for neuroscience research and clinical disorder therapy. Mapping the
connections of the human brain as a network is one of the most pervasive
paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged
as a potential method for modeling complex network data. Deep models, on the
other hand, have low interpretability, which prevents their usage in
decision-critical contexts like healthcare. To bridge this gap, we propose an
interpretable framework to analyze disorder-specific Regions of Interest (ROIs)
and prominent connections. The proposed framework consists of two modules: a
brain-network-oriented backbone model for disease prediction and a globally
shared explanation generator that highlights disorder-specific biomarkers
including salient ROIs and important connections. We conduct experiments on
three real-world datasets of brain disorders. The results verify that our
framework can obtain outstanding performance and also identify meaningful
biomarkers. All code for this work is available at
https://github.com/HennyJie/IBGNN.git. | [
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] |
Data labels in the security field are frequently noisy, limited, or biased
towards a subset of the population. As a result, commonplace evaluation methods
such as accuracy, precision and recall metrics, or analysis of performance
curves computed from labeled datasets do not provide sufficient confidence in
the real-world performance of a machine learning (ML) model. This has slowed
the adoption of machine learning in the field. In the industry today, we rely
on domain expertise and lengthy manual evaluation to build this confidence
before shipping a new model for security applications. In this paper, we
introduce Firenze, a novel framework for comparative evaluation of ML models'
performance using domain expertise, encoded into scalable functions called
markers. We show that markers computed and combined over select subsets of
samples called regions of interest can provide a robust estimate of their
real-world performances. Critically, we use statistical hypothesis testing to
ensure that observed differences-and therefore conclusions emerging from our
framework-are more prominent than that observable from the noise alone. Using
simulations and two real-world datasets for malware and domain-name-service
reputation detection, we illustrate our approach's effectiveness, limitations,
and insights. Taken together, we propose Firenze as a resource for fast,
interpretable, and collaborative model development and evaluation by mixed
teams of researchers, domain experts, and business owners. | [
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] |
Tree ensembles can be well-suited for black-box optimization tasks such as
algorithm tuning and neural architecture search, as they achieve good
predictive performance with little or no manual tuning, naturally handle
discrete feature spaces, and are relatively insensitive to outliers in the
training data. Two well-known challenges in using tree ensembles for black-box
optimization are (i) effectively quantifying model uncertainty for exploration
and (ii) optimizing over the piece-wise constant acquisition function. To
address both points simultaneously, we propose using the kernel interpretation
of tree ensembles as a Gaussian Process prior to obtain model variance
estimates, and we develop a compatible optimization formulation for the
acquisition function. The latter further allows us to seamlessly integrate
known constraints to improve sampling efficiency by considering
domain-knowledge in engineering settings and modeling search space symmetries,
e.g., hierarchical relationships in neural architecture search. Our framework
performs as well as state-of-the-art methods for unconstrained black-box
optimization over continuous/discrete features and outperforms competing
methods for problems combining mixed-variable feature spaces and known input
constraints. | [
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] |
As communication protocols evolve, datacenter network utilization increases.
As a result, congestion is more frequent, causing higher latency and packet
loss. Combined with the increasing complexity of workloads, manual design of
congestion control (CC) algorithms becomes extremely difficult. This calls for
the development of AI approaches to replace the human effort. Unfortunately, it
is currently not possible to deploy AI models on network devices due to their
limited computational capabilities. Here, we offer a solution to this problem
by building a computationally-light solution based on a recent reinforcement
learning CC algorithm [arXiv:2207.02295]. We reduce the inference time of RL-CC
by x500 by distilling its complex neural network into decision trees. This
transformation enables real-time inference within the $\mu$-sec decision-time
requirement, with a negligible effect on quality. We deploy the transformed
policy on NVIDIA NICs in a live cluster. Compared to popular CC algorithms used
in production, RL-CC is the only method that performs well on all benchmarks
tested over a large range of number of flows. It balances multiple metrics
simultaneously: bandwidth, latency, and packet drops. These results suggest
that data-driven methods for CC are feasible, challenging the prior belief that
handcrafted heuristics are necessary to achieve optimal performance. | [
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] |
Recent advances in the field of machine learning open a new era in high
performance computing. Applications of machine learning algorithms for the
development of accurate and cost-efficient surrogates of complex problems have
already attracted major attention from scientists. Despite their powerful
approximation capabilities, however, surrogates cannot produce the `exact'
solution to the problem. To address this issue, this paper exploits up-to-date
ML tools and delivers customized iterative solvers of linear equation systems,
capable of solving large-scale parametrized problems at any desired level of
accuracy. Specifically, the proposed approach consists of the following two
steps. At first, a reduced set of model evaluations is performed and the
corresponding solutions are used to establish an approximate mapping from the
problem's parametric space to its solution space using deep feedforward neural
networks and convolutional autoencoders. This mapping serves a means to obtain
very accurate initial predictions of the system's response to new query points
at negligible computational cost. Subsequently, an iterative solver inspired by
the Algebraic Multigrid method in combination with Proper Orthogonal
Decomposition, termed POD-2G, is developed that successively refines the
initial predictions towards the exact system solutions. The application of
POD-2G as a standalone solver or as preconditioner in the context of
preconditioned conjugate gradient methods is demonstrated on several numerical
examples of large scale systems, with the results indicating its superiority
over conventional iterative solution schemes. | [
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] |
This paper introduces a novel approach to embed flow-based models with
hierarchical structures. The proposed framework is named Variational Flow
Graphical (VFG) Model. VFGs learn the representation of high dimensional data
via a message-passing scheme by integrating flow-based functions through
variational inference. By leveraging the expressive power of neural networks,
VFGs produce a representation of the data using a lower dimension, thus
overcoming the drawbacks of many flow-based models, usually requiring a high
dimensional latent space involving many trivial variables. Aggregation nodes
are introduced in the VFG models to integrate forward-backward hierarchical
information via a message passing scheme. Maximizing the evidence lower bound
(ELBO) of data likelihood aligns the forward and backward messages in each
aggregation node achieving a consistency node state. Algorithms have been
developed to learn model parameters through gradient updating regarding the
ELBO objective.
The consistency of aggregation nodes enable VFGs to be applicable in
tractable inference on graphical structures. Besides representation learning
and numerical inference, VFGs provide a new approach for distribution modeling
on datasets with graphical latent structures. Additionally, theoretical study
shows that VFGs are universal approximators by leveraging the implicitly
invertible flow-based structures. With flexible graphical structures and
superior excessive power, VFGs could potentially be used to improve
probabilistic inference. In the experiments, VFGs achieves improved evidence
lower bound (ELBO) and likelihood values on multiple datasets. | [
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] |
A tensor network is a type of decomposition used to express and approximate
large arrays of data. A given data-set, quantum state or higher dimensional
multi-linear map is factored and approximated by a composition of smaller
multi-linear maps. This is reminiscent to how a Boolean function might be
decomposed into a gate array: this represents a special case of tensor
decomposition, in which the tensor entries are replaced by 0, 1 and the
factorisation becomes exact. The collection of associated techniques are
called, tensor network methods: the subject developed independently in several
distinct fields of study, which have more recently become interrelated through
the language of tensor networks. The tantamount questions in the field relate
to expressability of tensor networks and the reduction of computational
overheads. A merger of tensor networks with machine learning is natural. On the
one hand, machine learning can aid in determining a factorization of a tensor
network approximating a data set. On the other hand, a given tensor network
structure can be viewed as a machine learning model. Herein the tensor network
parameters are adjusted to learn or classify a data-set. In this survey we
recover the basics of tensor networks and explain the ongoing effort to develop
the theory of tensor networks in machine learning. | [
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] |
Deep learning has had tremendous success at learning low-dimensional
representations of high-dimensional data. This success would be impossible if
there was no hidden low-dimensional structure in data of interest; this
existence is posited by the manifold hypothesis, which states that the data
lies on an unknown manifold of low intrinsic dimension. In this paper, we argue
that this hypothesis does not properly capture the low-dimensional structure
typically present in image data. Assuming that data lies on a single manifold
implies intrinsic dimension is identical across the entire data space, and does
not allow for subregions of this space to have a different number of factors of
variation. To address this deficiency, we consider the union of manifolds
hypothesis, which states that data lies on a disjoint union of manifolds of
varying intrinsic dimensions. We empirically verify this hypothesis on
commonly-used image datasets, finding that indeed, observed data lies on a
disconnected set and that intrinsic dimension is not constant. We also provide
insights into the implications of the union of manifolds hypothesis in deep
learning, both supervised and unsupervised, showing that designing models with
an inductive bias for this structure improves performance across classification
and generative modelling tasks. Our code is available at
https://github.com/layer6ai-labs/UoMH. | [
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] |
We develop data-driven models to predict when a robot should feed during
social dining scenarios. Being able to eat independently with friends and
family is considered one of the most memorable and important activities for
people with mobility limitations. While existing robotic systems for feeding
people with mobility limitations focus on solitary dining, commensality, the
act of eating together, is often the practice of choice. Sharing meals with
others introduces the problem of socially appropriate bite timing for a robot,
i.e. the appropriate timing for the robot to feed without disrupting the social
dynamics of a shared meal. Our key insight is that bite timing strategies that
take into account the delicate balance of social cues can lead to seamless
interactions during robot-assisted feeding in a social dining scenario. We
approach this problem by collecting a Human-Human Commensality Dataset (HHCD)
containing 30 groups of three people eating together. We use this dataset to
analyze human-human commensality behaviors and develop bite timing prediction
models in social dining scenarios. We also transfer these models to human-robot
commensality scenarios. Our user studies show that prediction improves when our
algorithm uses multimodal social signaling cues between diners to model bite
timing. The HHCD dataset, videos of user studies, and code are available at
https://emprise.cs.cornell.edu/hrcom/ | [
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] |
3D scene graphs (3DSGs) are an emerging description; unifying symbolic,
topological, and metric scene representations. However, typical 3DSGs contain
hundreds of objects and symbols even for small environments; rendering task
planning on the full graph impractical. We construct TASKOGRAPHY, the first
large-scale robotic task planning benchmark over 3DSGs. While most benchmarking
efforts in this area focus on vision-based planning, we systematically study
symbolic planning, to decouple planning performance from visual representation
learning. We observe that, among existing methods, neither classical nor
learning-based planners are capable of real-time planning over full 3DSGs.
Enabling real-time planning demands progress on both (a) sparsifying 3DSGs for
tractable planning and (b) designing planners that better exploit 3DSG
hierarchies. Towards the former goal, we propose SCRUB, a task-conditioned 3DSG
sparsification method; enabling classical planners to match and in some cases
surpass state-of-the-art learning-based planners. Towards the latter goal, we
propose SEEK, a procedure enabling learning-based planners to exploit 3DSG
structure, reducing the number of replanning queries required by current best
approaches by an order of magnitude. We will open-source all code and baselines
to spur further research along the intersections of robot task planning,
learning and 3DSGs. | [
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] |
Causal background knowledge about the existence or the absence of causal
edges and paths is frequently encountered in observational studies. The shared
directed edges and links of a subclass of Markov equivalent DAGs refined due to
background knowledge can be represented by a causal maximally partially
directed acyclic graph (MPDAG). In this paper, we first provide a sound and
complete graphical characterization of causal MPDAGs and give a minimal
representation of a causal MPDAG. Then, we introduce a novel representation
called direct causal clause (DCC) to represent all types of causal background
knowledge in a unified form. Using DCCs, we study the consistency and
equivalency of causal background knowledge and show that any causal background
knowledge set can be equivalently decomposed into a causal MPDAG plus a minimal
residual set of DCCs. Polynomial-time algorithms are also provided for checking
the consistency, equivalency, and finding the decomposed MPDAG and residual
DCCs. Finally, with causal background knowledge, we prove a sufficient and
necessary condition to identify causal effects and surprisingly find that the
identifiability of causal effects only depends on the decomposed MPDAG. We also
develop a local IDA-type algorithm to estimate the possible values of an
unidentifiable effect. Simulations suggest that causal background knowledge can
significantly improve the identifiability of causal effects. | [
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Offline reinforcement learning (offline RL) is an emerging field that has
recently begun gaining attention across various application domains due to its
ability to learn strategies from earlier collected datasets. Offline RL proved
very successful, paving a path to solving previously intractable real-world
problems, and we aim to generalize this paradigm to a multiplayer-game setting.
To this end, we introduce a problem of offline equilibrium finding (OEF) and
construct multiple types of datasets across a wide range of games using several
established methods. To solve the OEF problem, we design a model-based
framework that can directly apply any online equilibrium finding algorithm to
the OEF setting while making minimal changes. The three most prominent
contemporary online equilibrium finding algorithms are adapted to the context
of OEF, creating three model-based variants: OEF-PSRO and OEF-CFR, which
generalize the widely-used algorithms PSRO and Deep CFR to compute Nash
equilibria (NEs), and OEF-JPSRO, which generalizes the JPSRO to calculate
(Coarse) Correlated equilibria ((C)CEs). We also combine the behavior cloning
policy with the model-based policy to further improve the performance and
provide a theoretical guarantee of the solution quality. Extensive experimental
results demonstrate the superiority of our approach over offline RL algorithms
and the importance of using model-based methods for OEF problems. We hope our
work will contribute to advancing research in large-scale equilibrium finding. | [
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] |
G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell
transmembrane proteins, responsible for numerous biological processes. From a
practical viewpoint around 34\% of the drugs approved by the US Food and Drug
Administration target these receptors. They can be analyzed from their
simulated molecular dynamics, including the prediction of their behavior in the
presence of drugs. In this paper, the capability of Long Short-Term Memory
Networks (LSTMs) are evaluated to learn and predict the molecular dynamic
trajectories of a receptor. Several models were trained with the 3D position of
the amino acids of the receptor considering different transformations on the
position of the amino acid, such as their centers of mass, the geometric
centers and the position of the $\alpha$--carbon for each amino acid. The error
of the prediction of the position was evaluated by the mean average error (MAE)
and root-mean-square deviation (RMSD). The LSTM models show a robust
performance, with results comparable to the state-of-the-art in non-dynamic 3D
predictions. The best MAE and RMSD values were found for the mass center of the
amino acids with 0.078 {\AA} and 0.156 {\AA} respectively. This work shows the
potential of LSTM to predict the molecular dynamics of GPRCs. | [
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] |
Timeliness and contextual accuracy of recommendations are increasingly
important when delivering contemporary digital marketing experiences.
Conventional recommender systems (RS) suggest relevant but time-invariant items
to users by accounting for their past purchases. These recommendations only map
to customers' general preferences rather than a customer's specific needs
immediately preceding a purchase. In contrast, RSs that consider the order of
transactions, purchases, or experiences to measure evolving preferences can
offer more salient and effective recommendations to customers: Sequential RSs
not only benefit from a better behavioral understanding of a user's current
needs but also better predictive power. In this paper, we demonstrate and rank
the effectiveness of a sequential recommendation system by utilizing a
production dataset of over 2.7 million credit card transactions for 46K
cardholders. The method first employs an autoencoder on raw transaction data
and submits observed transaction encodings to a GRU-based sequential model. The
sequential model produces a MAP@1 metric of 47% on the out-of-sample test set,
in line with existing research. We also discuss implications for embedding
real-time predictions using the sequential RS into Nexus, a scalable,
low-latency, event-based digital experience architecture. | [
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Variational quantum algorithms such as the Quantum Approximation Optimization
Algorithm (QAOA) in recent years have gained popularity as they provide the
hope of using NISQ devices to tackle hard combinatorial optimization problems.
It is, however, known that at low depth, certain locality constraints of QAOA
limit its performance. To go beyond these limitations, a non-local variant of
QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of
approximate solutions. The RQAOA has been studied comparatively less than QAOA,
and it is less understood, for instance, for what family of instances it may
fail to provide high quality solutions. However, as we are tackling
$\mathsf{NP}$-hard problems (specifically, the Ising spin model), it is
expected that RQAOA does fail, raising the question of designing even better
quantum algorithms for combinatorial optimization. In this spirit, we identify
and analyze cases where RQAOA fails and, based on this, propose a reinforcement
learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show
that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly
better on these identified instances where RQAOA underperforms, and is
similarly performing on instances where RQAOA is near-optimal. Our work
exemplifies the potentially beneficial synergy between reinforcement learning
and quantum (inspired) optimization in the design of new, even better
heuristics for hard problems. | [
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] |
Analog/mixed-signal circuit design is one of the most complex and
time-consuming stages in the whole chip design process. Due to various process,
voltage, and temperature (PVT) variations from chip manufacturing, analog
circuits inevitably suffer from performance degradation. Although there has
been plenty of work on automating analog circuit design under the typical
condition, limited research has been done on exploring robust designs under
real and unpredictable silicon variations. Automatic analog design against
variations requires prohibitive computation and time costs. To address the
challenge, we present RobustAnalog, a robust circuit design framework that
involves the variation information in the optimization process. Specifically,
circuit optimizations under different variations are considered as a set of
tasks. Similarities among tasks are leveraged and competitions are alleviated
to realize a sample-efficient multi-task training. Moreover, RobustAnalog
prunes the task space according to the current performance in each iteration,
leading to a further simulation cost reduction. In this way, RobustAnalog can
rapidly produce a set of circuit parameters that satisfies diverse constraints
(e.g. gain, bandwidth, noise...) across variations. We compare RobustAnalog
with Bayesian optimization, Evolutionary algorithm, and Deep Deterministic
Policy Gradient (DDPG) and demonstrate that RobustAnalog can significantly
reduce required optimization time by 14-30 times. Therefore, our study provides
a feasible method to handle various real silicon conditions. | [
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] |
We propose a novel approach for semi-supervised learning (SSL) designed to
overcome distribution shifts between training and real-world data arising in
the keyword spotting (KWS) task. Shifts from training data distribution are a
key challenge for real-world KWS tasks: when a new model is deployed on device,
the gating of the accepted data undergoes a shift in distribution, making the
problem of timely updates via subsequent deployments hard. Despite the shift,
we assume that the marginal distributions on labels do not change. We utilize a
modified teacher/student training framework, where labeled training data is
augmented with unlabeled data. Note that the teacher does not have access to
the new distribution as well. To train effectively with a mix of human and
teacher labeled data, we develop a teacher labeling strategy based on
confidence heuristics to reduce entropy on the label distribution from the
teacher model; the data is then sampled to match the marginal distribution on
the labels. Large scale experimental results show that a convolutional neural
network (CNN) trained on far-field audio, and evaluated on far-field audio
drawn from a different distribution, obtains a 14.3% relative improvement in
false discovery rate (FDR) at equal false reject rate (FRR), while yielding a
5% improvement in FDR under no distribution shift. Under a more severe
distribution shift from far-field to near-field audio with a smaller fully
connected network (FCN) our approach achieves a 52% relative improvement in FDR
at equal FRR, while yielding a 20% relative improvement in FDR on the original
distribution. | [
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User embeddings (vectorized representations of a user) are essential in
recommendation systems. Numerous approaches have been proposed to construct a
representation for the user in order to find similar items for retrieval tasks,
and they have been proven effective in industrial recommendation systems as
well. Recently people have discovered the power of using multiple embeddings to
represent a user, with the hope that each embedding represents the user's
interest in a certain topic. With multi-interest representation, it's important
to model the user's preference over the different topics and how the preference
change with time. However, existing approaches either fail to estimate the
user's affinity to each interest or unreasonably assume every interest of every
user fades with an equal rate with time, thus hurting the recall of candidate
retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model,
an approach that not only produces multi-interest for users by using the user's
sequential engagement more effectively but also automatically learns a set of
weights to represent the preference over each embedding so that the candidates
can be retrieved from each interest proportionally. Extensive experiments have
been done on various industrial-scale datasets to demonstrate the effectiveness
of our approach. | [
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] |
Although more layers and more parameters generally improve the accuracy of
the models, such big models generally have high computational complexity and
require big memory, which exceed the capacity of small devices for inference
and incurs long training time. In addition, it is difficult to afford long
training time and inference time of big models even in high performance
servers, as well. As an efficient approach to compress a large deep model (a
teacher model) to a compact model (a student model), knowledge distillation
emerges as a promising approach to deal with the big models. Existing knowledge
distillation methods cannot exploit the elastic available computing resources
and correspond to low efficiency. In this paper, we propose an Elastic Deep
Learning framework for knowledge Distillation, i.e., EDL-Dist. The advantages
of EDL-Dist are three-fold. First, the inference and the training process is
separated. Second, elastic available computing resources can be utilized to
improve the efficiency. Third, fault-tolerance of the training and inference
processes is supported. We take extensive experimentation to show that the
throughput of EDL-Dist is up to 3.125 times faster than the baseline method
(online knowledge distillation) while the accuracy is similar or higher. | [
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] |
The rising popularity of neural networks (NNs) in recent years and their
increasing prevalence in real-world applications have drawn attention to the
importance of their verification. While verification is known to be
computationally difficult theoretically, many techniques have been proposed for
solving it in practice. It has been observed in the literature that by default
neural networks rarely satisfy logical constraints that we want to verify. A
good course of action is to train the given NN to satisfy said constraint prior
to verifying them. This idea is sometimes referred to as continuous
verification, referring to the loop between training and verification. Usually
training with constraints is implemented by specifying a translation for a
given formal logic language into loss functions. These loss functions are then
used to train neural networks. Because for training purposes these functions
need to be differentiable, these translations are called differentiable logics
(DL). This raises several research questions. What kind of differentiable
logics are possible? What difference does a specific choice of DL make in the
context of continuous verification? What are the desirable criteria for a DL
viewed from the point of view of the resulting loss function? In this extended
abstract we will discuss and answer these questions. | [
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] |
Performance in Speech Emotion Recognition (SER) on a single language has
increased greatly in the last few years thanks to the use of deep learning
techniques. However, cross-lingual SER remains a challenge in real-world
applications due to two main factors: the first is the big gap among the source
and the target domain distributions; the second factor is the major
availability of unlabeled utterances in contrast to the labeled ones for the
new language. Taking into account previous aspects, we propose a
Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition
when only few labeled examples in the target domain (i.e. the new language) are
available. Our method is based on a Transformer and it adapts to the new domain
by exploiting a pseudo-labeling strategy on the unlabeled utterances. In
particular, the use of a hard and soft pseudo-labels approach is investigated.
We thoroughly evaluate the performance of the proposed method in a
speaker-independent setup on both the source and the new language and show its
robustness across five languages belonging to different linguistic strains. The
experimental findings indicate that the unweighted accuracy is increased by an
average of 40% compared to state-of-the-art methods. | [
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] |
BERT4Rec is an effective model for sequential recommendation based on the
Transformer architecture. In the original publication, BERT4Rec claimed
superiority over other available sequential recommendation approaches (e.g.
SASRec), and it is now frequently being used as a state-of-the art baseline for
sequential recommendations. However, not all subsequent publications confirmed
this result and proposed other models that were shown to outperform BERT4Rec in
effectiveness. In this paper we systematically review all publications that
compare BERT4Rec with another popular Transformer-based model, namely SASRec,
and show that BERT4Rec results are not consistent within these publications. To
understand the reasons behind this inconsistency, we analyse the available
implementations of BERT4Rec and show that we fail to reproduce results of the
original BERT4Rec publication when using their default configuration
parameters. However, we are able to replicate the reported results with the
original code if training for a much longer amount of time (up to 30x) compared
to the default configuration. We also propose our own implementation of
BERT4Rec based on the Hugging Face Transformers library, which we demonstrate
replicates the originally reported results on 3 out 4 datasets, while requiring
up to 95% less training time to converge. Overall, from our systematic review
and detailed experiments, we conclude that BERT4Rec does indeed exhibit
state-of-the-art effectiveness for sequential recommendation, but only when
trained for a sufficient amount of time. Additionally, we show that our
implementation can further benefit from adapting other Transformer
architectures that are available in the Hugging Face Transformers library (e.g.
using disentangled attention, as provided by DeBERTa, or larger hidden layer
size cf. ALBERT). | [
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] |
In both industrial and service domains, a central benefit of the use of
robots is their ability to quickly and reliably execute repetitive tasks.
However, even relatively simple peg-in-hole tasks are typically subject to
stochastic variations, requiring search motions to find relevant features such
as holes. While search improves robustness, it comes at the cost of increased
runtime: More exhaustive search will maximize the probability of successfully
executing a given task, but will significantly delay any downstream tasks. This
trade-off is typically resolved by human experts according to simple
heuristics, which are rarely optimal. This paper introduces an automatic,
data-driven and heuristic-free approach to optimize robot search strategies. By
training a neural model of the search strategy on a large set of simulated
stochastic environments, conditioning it on few real-world examples and
inverting the model, we can infer search strategies which adapt to the
time-variant characteristics of the underlying probability distributions, while
requiring very few real-world measurements. We evaluate our approach on two
different industrial robots in the context of spiral and probe search for THT
electronics assembly. | [
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] |
Quantitative investment is a fundamental financial task that highly relies on
accurate stock prediction and profitable investment decision making. Despite
recent advances in deep learning (DL) have shown stellar performance on
capturing trading opportunities in the stochastic stock market, we observe that
the performance of existing DL methods is sensitive to random seeds and network
initialization. To design more profitable DL methods, we analyze this
phenomenon and find two major limitations of existing works. First, there is a
noticeable gap between accurate financial predictions and profitable investment
strategies. Second, investment decisions are made based on only one individual
predictor without consideration of model uncertainty, which is inconsistent
with the workflow in real-world trading firms. To tackle these two limitations,
we first reformulate quantitative investment as a multi-task learning problem.
Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE)
framework for quantitative investment to mimic the efficient bottom-up trading
strategy design workflow of successful trading firms. In Stage one, multiple
independent trading experts are jointly optimized with an individual
uncertainty-aware loss function. In Stage two, we train neural routers
(corresponding to the role of a portfolio manager) to dynamically deploy these
experts on an as-needed basis. AlphaMix is also a universal framework that is
applicable to various backbone network architectures with consistent
performance gains. Through extensive experiments on long-term real-world data
spanning over five years on two of the most influential financial markets (US
and China), we demonstrate that AlphaMix significantly outperforms many
state-of-the-art baselines in terms of four financial criteria. | [
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] |
Modeling users' dynamic preferences from historical behaviors lies at the
core of modern recommender systems. Due to the diverse nature of user
interests, recent advances propose the multi-interest networks to encode
historical behaviors into multiple interest vectors. In real scenarios, the
corresponding items of captured interests are usually retrieved together to get
exposure and collected into training data, which produces dependencies among
interests. Unfortunately, multi-interest networks may incorrectly concentrate
on subtle dependencies among captured interests. Misled by these dependencies,
the spurious correlations between irrelevant interests and targets are
captured, resulting in the instability of prediction results when training and
test distributions do not match. In this paper, we introduce the widely used
Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of
independence among captured interests and empirically show that the continuous
increase of HSIC may harm model performance. Based on this, we propose a novel
multi-interest network, named DEep Stable Multi-Interest Learning (DESMIL),
which tries to eliminate the influence of subtle dependencies among captured
interests via learning weights for training samples and make model concentrate
more on underlying true causation. We conduct extensive experiments on public
recommendation datasets, a large-scale industrial dataset and the synthetic
datasets which simulate the out-of-distribution data. Experimental results
demonstrate that our proposed DESMIL outperforms state-of-the-art models by a
significant margin. Besides, we also conduct comprehensive model analysis to
reveal the reason why DESMIL works to a certain extent. | [
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] |
Studies suggest that one in three US adults use the Internet to diagnose or
learn about a health concern. However, such access to health information online
could exacerbate the disparities in health information availability and use.
Health information seeking behavior (HISB) refers to the ways in which
individuals seek information about their health, risks, illnesses, and
health-protective behaviors. For patients engaging in searches for health
information on digital media platforms, health literacy divides can be
exacerbated both by their own lack of knowledge and by algorithmic
recommendations, with results that disproportionately impact disadvantaged
populations, minorities, and low health literacy users. This study reports on
an exploratory investigation of the above challenges by examining whether
responsible and representative recommendations can be generated using advanced
analytic methods applied to a large corpus of videos and their metadata on a
chronic condition (diabetes) from the YouTube social media platform. The paper
focusses on biases associated with demographic characters of actors using
videos on diabetes that were retrieved and curated for multiple criteria such
as encoded medical content and their understandability to address patient
education and population health literacy needs. This approach offers an immense
opportunity for innovation in human-in-the-loop, augmented-intelligence,
bias-aware and responsible algorithmic recommendations by combining the
perspectives of health professionals and patients into a scalable and
generalizable machine learning framework for patient empowerment and improved
health outcomes. | [
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] |
The propagation of rumours on social media poses an important threat to
societies, so that various techniques for rumour detection have been proposed
recently. Yet, existing work focuses on \emph{what} entities constitute a
rumour, but provides little support to understand \emph{why} the entities have
been classified as such. This prevents an effective evaluation of the detected
rumours as well as the design of countermeasures. In this work, we argue that
explanations for detected rumours may be given in terms of examples of related
rumours detected in the past. A diverse set of similar rumours helps users to
generalize, i.e., to understand the properties that govern the detection of
rumours. Since the spread of rumours in social media is commonly modelled using
feature-annotated graphs, we propose a query-by-example approach that, given a
rumour graph, extracts the $k$ most similar and diverse subgraphs from past
rumours. The challenge is that all of the computations require fast assessment
of similarities between graphs. To achieve an efficient and adaptive
realization of the approach in a streaming setting, we present a novel graph
representation learning technique and report on implementation considerations.
Our evaluation experiments show that our approach outperforms baseline
techniques in delivering meaningful explanations for various rumour propagation
behaviours. | [
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] |
Exoplanet detection opens the door to the discovery of new habitable worlds
and helps us understand how planets were formed. With the objective of finding
earth-like habitable planets, NASA launched Kepler space telescope and its
follow up mission K2. The advancement of observation capabilities has increased
the range of fresh data available for research, and manually handling them is
both time-consuming and difficult. Machine learning and deep learning
techniques can greatly assist in lowering human efforts to process the vast
array of data produced by the modern instruments of these exoplanet programs in
an economical and unbiased manner. However, care should be taken to detect all
the exoplanets precisely while simultaneously minimizing the misclassification
of non-exoplanet stars. In this paper, we utilize two variations of generative
adversarial networks, namely semi-supervised generative adversarial networks
and auxiliary classifier generative adversarial networks, to detect transiting
exoplanets in K2 data. We find that the usage of these models can be helpful
for the classification of stars with exoplanets. Both of our techniques are
able to categorize the light curves with a recall and precision of 1.00 on the
test data. Our semi-supervised technique is beneficial to solve the cumbersome
task of creating a labeled dataset. | [
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With the development of artificial intelligence,more and more financial
practitioners apply deep reinforcement learning to financial trading
strategies.However,It is difficult to extract accurate features due to the
characteristics of considerable noise,highly non-stationary,and non-linearity
of single-scale time series,which makes it hard to obtain high returns.In this
paper,we extract a multi-scale feature matrix on multiple time scales of
financial time series,according to the classic financial theory-Chan Theory,and
put forward to an approach of multi-scale stroke deep deterministic policy
gradient reinforcement learning model(MSSDDPG)to search for the optimal trading
strategy.We carried out experiments on the datasets of the Dow Jones,S&P 500 of
U.S. stocks, and China's CSI 300,SSE Composite,evaluate the performance of our
approach compared with turtle trading strategy, Deep
Q-learning(DQN)reinforcement learning strategy,and deep deterministic policy
gradient (DDPG) reinforcement learning strategy.The result shows that our
approach gets the best performance in China CSI 300,SSE Composite,and get an
outstanding result in Dow Jones,S&P 500 of U.S. | [
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] |
In this paper, we propose a feature-free method for detecting phishing
websites using the Normalized Compression Distance (NCD), a parameter-free
similarity measure which computes the similarity of two websites by compressing
them, thus eliminating the need to perform any feature extraction. It also
removes any dependence on a specific set of website features. This method
examines the HTML of webpages and computes their similarity with known phishing
websites, in order to classify them. We use the Furthest Point First algorithm
to perform phishing prototype extractions, in order to select instances that
are representative of a cluster of phishing webpages. We also introduce the use
of an incremental learning algorithm as a framework for continuous and adaptive
detection without extracting new features when concept drift occurs. On a large
dataset, our proposed method significantly outperforms previous methods in
detecting phishing websites, with an AUC score of 98.68%, a high true positive
rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of
0.58%. Our approach uses prototypes, eliminating the need to retain long term
data in the future, and is feasible to deploy in real systems with a processing
time of roughly 0.3 seconds. | [
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] |
High-end vehicles have been furnished with a number of electronic control
units (ECUs), which provide upgrading functions to enhance the driving
experience. The controller area network (CAN) is a well-known protocol that
connects these ECUs because of its modesty and efficiency. However, the CAN bus
is vulnerable to various types of attacks. Although the intrusion detection
system (IDS) is proposed to address the security problem of the CAN bus, most
previous studies only provide alerts when attacks occur without knowing the
specific type of attack. Moreover, an IDS is designed for a specific car model
due to diverse car manufacturers. In this study, we proposed a novel deep
learning model called supervised contrastive (SupCon) ResNet, which can handle
multiple attack identification on the CAN bus. Furthermore, the model can be
used to improve the performance of a limited-size dataset using a transfer
learning technique. The capability of the proposed model is evaluated on two
real car datasets. When tested with the car hacking dataset, the experiment
results show that the SupCon ResNet model improves the overall false-negative
rates of four types of attack by four times on average, compared to other
models. In addition, the model achieves the highest F1 score at 0.9994 on the
survival dataset by utilizing transfer learning. Finally, the model can adapt
to hardware constraints in terms of memory size and running time. | [
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] |
While sequential recommender systems achieve significant improvements on
capturing user dynamics, we argue that sequential recommenders are vulnerable
against substitution-based profile pollution attacks. To demonstrate our
hypothesis, we propose a substitution-based adversarial attack algorithm, which
modifies the input sequence by selecting certain vulnerable elements and
substituting them with adversarial items. In both untargeted and targeted
attack scenarios, we observe significant performance deterioration using the
proposed profile pollution algorithm. Motivated by such observations, we design
an efficient adversarial defense method called Dirichlet neighborhood sampling.
Specifically, we sample item embeddings from a convex hull constructed by
multi-hop neighbors to replace the original items in input sequences. During
sampling, a Dirichlet distribution is used to approximate the probability
distribution in the neighborhood such that the recommender learns to combat
local perturbations. Additionally, we design an adversarial training method
tailored for sequential recommender systems. In particular, we represent
selected items with one-hot encodings and perform gradient ascent on the
encodings to search for the worst case linear combination of item embeddings in
training. As such, the embedding function learns robust item representations
and the trained recommender is resistant to test-time adversarial examples.
Extensive experiments show the effectiveness of both our attack and defense
methods, which consistently outperform baselines by a significant margin across
model architectures and datasets. | [
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] |
Decision-making systems based on AI and machine learning have been used
throughout a wide range of real-world scenarios, including healthcare, law
enforcement, education, and finance. It is no longer far-fetched to envision a
future where autonomous systems will be driving entire business decisions and,
more broadly, supporting large-scale decision-making infrastructure to solve
society's most challenging problems. Issues of unfairness and discrimination
are pervasive when decisions are being made by humans, and remain (or are
potentially amplified) when decisions are made using machines with little
transparency, accountability, and fairness. In this paper, we introduce a
framework for \textit{causal fairness analysis} with the intent of filling in
this gap, i.e., understanding, modeling, and possibly solving issues of
fairness in decision-making settings. The main insight of our approach will be
to link the quantification of the disparities present on the observed data with
the underlying, and often unobserved, collection of causal mechanisms that
generate the disparity in the first place, challenge we call the Fundamental
Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, we
study the problem of decomposing variations and empirical measures of fairness
that attribute such variations to structural mechanisms and different units of
the population. Our effort culminates in the Fairness Map, which is the first
systematic attempt to organize and explain the relationship between different
criteria found in the literature. Finally, we study which causal assumptions
are minimally needed for performing causal fairness analysis and propose a
Fairness Cookbook, which allows data scientists to assess the existence of
disparate impact and disparate treatment. | [
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] |
Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic
target for the treatment of uterine diseases. To date, several GnRH1R
antagonists are available in clinical investigation without satisfying multiple
property constraints. To fill this gap, we aim to develop a deep learning-based
framework to facilitate the effective and efficient discovery of a new orally
active small-molecule drug targeting GnRH1R with desirable properties. In the
present work, a ligand-and-structure combined model, namely LS-MolGen, was
firstly proposed for molecular generation by fully utilizing the information on
the known active compounds and the structure of the target protein, which was
demonstrated by its superior performance than ligand- or structure-based
methods separately. Then, a in silico screening including activity prediction,
ADMET evaluation, molecular docking and FEP calculation was conducted, where
~30,000 generated novel molecules were narrowed down to 8 for experimental
synthesis and validation. In vitro and in vivo experiments showed that three of
them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM,
compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and
compound 5 performed well in fundamental PK properties, such as half-life, oral
bioavailability, and PPB, etc. We believed that the proposed
ligand-and-structure combined molecular generative model and the whole
computer-aided workflow can potentially be extended to similar tasks for de
novo drug design or lead optimization. | [
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] |
Deep Learning approaches based on Convolutional Neural Networks (CNNs) are
extensively utilized and very successful in a wide range of application areas,
including image classification and speech recognition. For the execution of
trained CNNs, i.e. model inference, we nowadays witness a shift from the Cloud
to the Edge. Unfortunately, deploying and inferring large, compute and memory
intensive CNNs on edge devices is challenging because these devices typically
have limited power budgets and compute/memory resources. One approach to
address this challenge is to leverage all available resources across multiple
edge devices to deploy and execute a large CNN by properly partitioning the CNN
and running each CNN partition on a separate edge device. Although such
distribution, deployment, and execution of large CNNs on multiple edge devices
is a desirable and beneficial approach, there currently does not exist a design
and programming framework that takes a trained CNN model, together with a CNN
partitioning specification, and fully automates the CNN model splitting and
deployment on multiple edge devices to facilitate distributed CNN inference at
the Edge. Therefore, in this paper, we propose a novel framework, called
AutoDiCE, for automated splitting of a CNN model into a set of sub-models and
automated code generation for distributed and collaborative execution of these
sub-models on multiple, possibly heterogeneous, edge devices, while supporting
the exploitation of parallelism among and within the edge devices. Our
experimental results show that AutoDiCE can deliver distributed CNN inference
with reduced energy consumption and memory usage per edge device, and improved
overall system throughput at the same time. | [
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] |
The problem of how to genetically modify cells in order to maximize a certain
cellular phenotype has taken center stage in drug development over the last few
years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells
entering cancer clinical trials). Exhausting the search space for all possible
genetic edits (perturbations) or combinations thereof is infeasible due to cost
and experimental limitations. This work provides a theoretically sound
framework for iteratively exploring the space of perturbations in pooled
batches in order to maximize a target phenotype under an experimental budget.
Inspired by this application domain, we study the problem of batch query bandit
optimization and introduce the Optimistic Arm Elimination ($\mathrm{OAE}$)
principle designed to find an almost optimal arm under different functional
relationships between the queries (arms) and the outputs (rewards). We analyze
the convergence properties of $\mathrm{OAE}$ by relating it to the Eluder
dimension of the algorithm's function class and validate that $\mathrm{OAE}$
outperforms other strategies in finding optimal actions in experiments on
simulated problems, public datasets well-studied in bandit contexts, and in
genetic perturbation datasets when the regression model is a deep neural
network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in
the GeneDisco experimental planning challenge. | [
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] |
We propose VDL-Surrogate, a view-dependent neural-network-latent-based
surrogate model for parameter space exploration of ensemble simulations that
allows high-resolution visualizations and user-specified visual mappings.
Surrogate-enabled parameter space exploration allows domain scientists to
preview simulation results without having to run a large number of
computationally costly simulations. Limited by computational resources,
however, existing surrogate models may not produce previews with sufficient
resolution for visualization and analysis. To improve the efficient use of
computational resources and support high-resolution exploration, we perform ray
casting from different viewpoints to collect samples and produce compact latent
representations. This latent encoding process reduces the cost of surrogate
model training while maintaining the output quality. In the model training
stage, we select viewpoints to cover the whole viewing sphere and train
corresponding VDL-Surrogate models for the selected viewpoints. In the model
inference stage, we predict the latent representations at previously selected
viewpoints and decode the latent representations to data space. For any given
viewpoint, we make interpolations over decoded data at selected viewpoints and
generate visualizations with user-specified visual mappings. We show the
effectiveness and efficiency of VDL-Surrogate in cosmological and ocean
simulations with quantitative and qualitative evaluations. Source code is
publicly available at https://github.com/trainsn/VDL-Surrogate. | [
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] |
Evolution Strategy (ES) algorithms have shown promising results in training
complex robotic control policies due to their massive parallelism capability,
simple implementation, effective parameter-space exploration, and fast training
time. However, a key limitation of ES is its scalability to large capacity
models, including modern neural network architectures. In this work, we develop
Predictive Information Augmented Random Search (PI-ARS) to mitigate this
limitation by leveraging recent advancements in representation learning to
reduce the parameter search space for ES. Namely, PI-ARS combines a
gradient-based representation learning technique, Predictive Information (PI),
with a gradient-free ES algorithm, Augmented Random Search (ARS), to train
policies that can process complex robot sensory inputs and handle highly
nonlinear robot dynamics. We evaluate PI-ARS on a set of challenging
visual-locomotion tasks where a quadruped robot needs to walk on uneven
stepping stones, quincuncial piles, and moving platforms, as well as to
complete an indoor navigation task. Across all tasks, PI-ARS demonstrates
significantly better learning efficiency and performance compared to the ARS
baseline. We further validate our algorithm by demonstrating that the learned
policies can successfully transfer to a real quadruped robot, for example,
achieving a 100% success rate on the real-world stepping stone environment,
dramatically improving prior results achieving 40% success. | [
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] |
A combinatorial recommender (CR) system feeds a list of items to a user at a
time in the result page, in which the user behavior is affected by both
contextual information and items. The CR is formulated as a combinatorial
optimization problem with the objective of maximizing the recommendation reward
of the whole list. Despite its importance, it is still a challenge to build a
practical CR system, due to the efficiency, dynamics, personalization
requirement in online environment. In particular, we tear the problem into two
sub-problems, list generation and list evaluation. Novel and practical model
architectures are designed for these sub-problems aiming at jointly optimizing
effectiveness and efficiency. In order to adapt to online case, a bootstrap
algorithm forming an actor-critic reinforcement framework is given to explore
better recommendation mode in long-term user interaction. Offline and online
experiment results demonstrate the efficacy of proposed JDRec framework. JDRec
has been applied in online JD recommendation, improving click through rate by
2.6% and synthetical value for the platform by 5.03%. We will publish the
large-scale dataset used in this study to contribute to the research community. | [
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] |
Termination analyses investigate the termination behavior of programs,
intending to detect nontermination, which is known to cause a variety of
program bugs (e.g. hanging programs, denial-of-service vulnerabilities). Beyond
formal approaches, various attempts have been made to estimate the termination
behavior of programs using neural networks. However, the majority of these
approaches continue to rely on formal methods to provide strong soundness
guarantees and consequently suffer from similar limitations. In this paper, we
move away from formal methods and embrace the stochastic nature of machine
learning models. Instead of aiming for rigorous guarantees that can be
interpreted by solvers, our objective is to provide an estimation of a
program's termination behavior and of the likely reason for nontermination
(when applicable) that a programmer can use for debugging purposes. Compared to
previous approaches using neural networks for program termination, we also take
advantage of the graph representation of programs by employing Graph Neural
Networks. To further assist programmers in understanding and debugging
nontermination bugs, we adapt the notions of attention and semantic
segmentation, previously used for other application domains, to programs.
Overall, we designed and implemented classifiers for program termination based
on Graph Convolutional Networks and Graph Attention Networks, as well as a
semantic segmentation Graph Neural Network that localizes AST nodes likely to
cause nontermination. We also illustrated how the information provided by
semantic segmentation can be combined with program slicing to further aid
debugging. | [
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] |
Extraterrestrial rovers with a general-purpose robotic arm have many
potential applications in lunar and planetary exploration. Introducing autonomy
into such systems is desirable for increasing the time that rovers can spend
gathering scientific data and collecting samples. This work investigates the
applicability of deep reinforcement learning for vision-based robotic grasping
of objects on the Moon. A novel simulation environment with
procedurally-generated datasets is created to train agents under challenging
conditions in unstructured scenes with uneven terrain and harsh illumination. A
model-free off-policy actor-critic algorithm is then employed for end-to-end
learning of a policy that directly maps compact octree observations to
continuous actions in Cartesian space. Experimental evaluation indicates that
3D data representations enable more effective learning of manipulation skills
when compared to traditionally used image-based observations. Domain
randomization improves the generalization of learned policies to novel scenes
with previously unseen objects and different illumination conditions. To this
end, we demonstrate zero-shot sim-to-real transfer by evaluating trained agents
on a real robot in a Moon-analogue facility. | [
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] |
Integrating low-power wearable Internet of Things (IoT) systems into routine
health monitoring is an ongoing challenge. Recent advances in the computation
capabilities of wearables make it possible to target complex scenarios by
exploiting multiple biosignals and using high-performance algorithms, such as
Deep Neural Networks (DNNs). There is, however, a trade-off between performance
of the algorithms and the low-power requirements of IoT platforms with limited
resources. Besides, physically larger and multi-biosignal-based wearables bring
significant discomfort to the patients. Consequently, reducing power
consumption and discomfort is necessary for patients to use IoT devices
continuously during everyday life. To overcome these challenges, in the context
of epileptic seizure detection, we propose a many-to-one signals knowledge
distillation approach targeting single-biosignal processing in IoT wearable
systems. The starting point is to get a highly-accurate multi-biosignal DNN,
then apply our approach to develop a single-biosignal DNN solution for IoT
systems that achieves an accuracy comparable to the original multi-biosignal
DNN. To assess the practicality of our approach to real-life scenarios, we
perform a comprehensive simulation experiment analysis on several
state-of-the-art edge computing platforms, such as Kendryte K210 and Raspberry
Pi Zero. | [
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] |
Open material databases storing hundreds of thousands of material structures
and their corresponding properties have become the cornerstone of modern
computational materials science. Yet, the raw outputs of the simulations, such
as the trajectories from molecular dynamics simulations and charge densities
from density functional theory calculations, are generally not shared due to
their huge size. In this work, we describe a cloud-based platform to facilitate
the sharing of raw data and enable the fast post-processing in the cloud to
extract new properties defined by the user. As an initial demonstration, our
database currently includes 6286 molecular dynamics trajectories for amorphous
polymer electrolytes and 5.7 terabytes of data. We create a public analysis
library at https://github.com/TRI-AMDD/htp_md to extract multiple properties
from the raw data, using both expert designed functions and machine learning
models. The analysis is run automatically with computation in the cloud, and
results then populate a database that can be accessed publicly. Our platform
encourages users to contribute both new trajectory data and analysis functions
via public interfaces. Newly analyzed properties will be incorporated into the
database. Finally, we create a front-end user interface at
https://www.htpmd.matr.io for browsing and visualization of our data. We
envision the platform to be a new way of sharing raw data and new insights for
the computational materials science community. | [
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During applying data-oriented diagnosis systems to distinguishing the type of
and evaluating the severity of nuclear power plant initial events, it is of
vital importance to decide which parameters to be used as the system input.
However, although several diagnosis systems have already achieved acceptable
performance in diagnosis precision and speed, hardly have the researchers
discussed the method of monitoring point choosing and its layout. For this
reason, redundant measuring data are used to train the diagnostic model,
leading to high uncertainty of the classification, extra training time
consumption, and higher probability of overfitting while training. In this
study, a method of choosing thermal hydraulics parameters of a nuclear power
plant is proposed, using the theory of post-hoc interpretability theory in deep
learning. At the start, a novel Time-sequential Residual Convolutional Neural
Network (TRES-CNN) diagnosis model is introduced to identify the position and
hydrodynamic diameter of breaks in LOCA, using 38 parameters manually chosen on
HPR1000 empirically. Afterwards, post-hoc interpretability methods are applied
to evaluate the attributions of diagnosis model's outputs, deciding which 15
parameters to be more decisive in diagnosing LOCA details. The results show
that the TRES-CNN based diagnostic model successfully predicts the position and
size of breaks in LOCA via selected 15 parameters of HPR1000, with 25% of time
consumption while training the model compared the process using total 38
parameters. In addition, the relative diagnostic accuracy error is within 1.5
percent compared with the model using parameters chosen empirically, which can
be regarded as the same amount of diagnostic reliability. | [
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] |
Nearly 30% of the people in the rural areas of Bangladesh are below the
poverty level. Moreover, due to the unavailability of modernized
healthcare-related technology, nursing and diagnosis facilities are limited for
rural people. Therefore, rural people are deprived of proper healthcare. In
this perspective, modern technology can be facilitated to mitigate their health
problems. ECG sensing tools are interfaced with the human chest, and requisite
cardiovascular data is collected through an IoT device. These data are stored
in the cloud incorporates with the MQTT and HTTP servers. An innovative
IoT-based method for ECG monitoring systems on cardiovascular or heart patients
has been suggested in this study. The ECG signal parameters P, Q, R, S, T are
collected, pre-processed, and predicted to monitor the cardiovascular
conditions for further health management. The machine learning algorithm is
used to determine the significance of ECG signal parameters and error rate. The
logistic regression model fitted the better agreements between the train and
test data. The prediction has been performed to determine the variation of
PQRST quality and its suitability in the ECG Monitoring System. Considering the
values of quality parameters, satisfactory results are obtained. The proposed
IoT-based ECG system reduces the health care cost and complexity of
cardiovascular diseases in the future. | [
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] |
Isoforms are mRNAs produced from the same gene site in the phenomenon called
Alternative Splicing. Studies have shown that more than 95% of human multi-exon
genes have undergone alternative splicing. Although there are few changes in
mRNA sequence, They may have a systematic effect on cell function and
regulation. It is widely reported that isoforms of a gene have distinct or even
contrasting functions. Most studies have shown that alternative splicing plays
a significant role in human health and disease. Despite the wide range of gene
function studies, there is little information about isoforms' functionalities.
Recently, some computational methods based on Multiple Instance Learning have
been proposed to predict isoform function using gene function and gene
expression profile. However, their performance is not desirable due to the lack
of labeled training data. In addition, probabilistic models such as Conditional
Random Field (CRF) have been used to model the relation between isoforms. This
project uses all the data and valuable information such as isoform sequences,
expression profiles, and gene ontology graphs and proposes a comprehensive
model based on Deep Neural Networks. The UniProt Gene Ontology (GO) database is
used as a standard reference for gene functions. The NCBI RefSeq database is
used for extracting gene and isoform sequences, and the NCBI SRA database is
used for expression profile data. Metrics such as Receiver Operating
Characteristic Area Under the Curve (ROC AUC) and Precision-Recall Under the
Curve (PR AUC) are used to measure the prediction accuracy. | [
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Modern vehicles, including autonomous vehicles and connected vehicles, have
adopted an increasing variety of functionalities through connections and
communications with other vehicles, smart devices, and infrastructures.
However, the growing connectivity of the Internet of Vehicles (IoV) also
increases the vulnerabilities to network attacks. To protect IoV systems
against cyber threats, Intrusion Detection Systems (IDSs) that can identify
malicious cyber-attacks have been developed using Machine Learning (ML)
approaches. To accurately detect various types of attacks in IoV networks, we
propose a novel ensemble IDS framework named Leader Class and Confidence
Decision Ensemble (LCCDE). It is constructed by determining the best-performing
ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost)
for every class or type of attack. The class leader models with their
prediction confidence values are then utilized to make accurate decisions
regarding the detection of various types of cyber-attacks. Experiments on two
public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate
the effectiveness of the proposed LCCDE for intrusion detection on both
intra-vehicle and external networks. | [
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] |
Recently, graph neural networks (GNNs) have been widely used to develop
successful recommender systems. Although powerful, it is very difficult for a
GNN-based recommender system to attach tangible explanations of why a specific
item ends up in the list of suggestions for a given user. Indeed, explaining
GNN-based recommendations is unique, and existing GNN explanation methods are
inappropriate for two reasons. First, traditional GNN explanation methods are
designed for node, edge, or graph classification tasks rather than ranking, as
in recommender systems. Second, standard machine learning explanations are
usually intended to support skilled decision-makers. Instead, recommendations
are designed for any end-user, and thus their explanations should be provided
in user-understandable ways. In this work, we propose GREASE, a novel method
for explaining the suggestions provided by any black-box GNN-based recommender
system. Specifically, GREASE first trains a surrogate model on a target
user-item pair and its $l$-hop neighborhood. Then, it generates both factual
and counterfactual explanations by finding optimal adjacency matrix
perturbations to capture the sufficient and necessary conditions for an item to
be recommended, respectively. Experimental results conducted on real-world
datasets demonstrate that GREASE can generate concise and effective
explanations for popular GNN-based recommender models. | [
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] |
Simulation has played an important role in efficiently evaluating
self-driving vehicles in terms of scalability. Existing methods mostly rely on
heuristic-based simulation, where traffic participants follow certain
human-encoded rules that fail to generate complex human behaviors. Therefore,
the reactive simulation concept is proposed to bridge the human behavior gap
between simulation and real-world traffic scenarios by leveraging real-world
data. However, these reactive models can easily generate unreasonable behaviors
after a few steps of simulation, where we regard the model as losing its
stability. To the best of our knowledge, no work has explicitly discussed and
analyzed the stability of the reactive simulation framework. In this paper, we
aim to provide a thorough stability analysis of the reactive simulation and
propose a solution to enhance the stability. Specifically, we first propose a
new reactive simulation framework, where we discover that the smoothness and
consistency of the simulated state sequences are crucial factors to stability.
We then incorporate the kinematic vehicle model into the framework to improve
the closed-loop stability of the reactive simulation. Furthermore, along with
commonly-used metrics, several novel metrics are proposed in this paper to
better analyze the simulation performance. | [
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] |
Adaptive learning aims to stimulate and meet the needs of individual
learners, which requires sophisticated system-level coordination of diverse
tasks, including modeling learning resources, estimating student states, and
making personalized recommendations. Existing deep learning methods have
achieved great success over statistical models; however, they still lack
generalization for diverse tasks and suffer from insufficient capacity since
they are composed of highly-coupled task-specific architectures and rely on
small-scale, coarse-grained recommendation scenarios. To realize the idea of
general adaptive systems proposed in pedagogical theory, with the emerging
pre-training techniques in NLP, we try to conduct a practical exploration on
applying pre-training to adaptive learning, to propose a unified framework
based on data observation and learning style analysis, properly leveraging
heterogeneous learning elements. Through a series of downstream tasks of
Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and
Dropout Prediction, we find that course structures, text, and knowledge are
helpful for modeling and inherently coherent to student non-sequential learning
behaviors and that indirectly relevant information included in the pre-training
foundation can be shared across downstream tasks to facilitate effectiveness.
We finally build a simplified systematic application of adaptive learning and
reflect on the insights brought back to pedagogy. The source code and dataset
will be released. | [
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] |
Large quantifies of online user activity data, such as weekly web search
volumes, which co-evolve with the mutual influence of several queries and
locations, serve as an important social sensor. It is an important task to
accurately forecast the future activity by discovering latent interactions from
such data, i.e., the ecosystems between each query and the flow of influences
between each area. However, this is a difficult problem in terms of data
quantity and complex patterns covering the dynamics. To tackle the problem, we
propose FluxCube, which is an effective mining method that forecasts large
collections of co-evolving online user activity and provides good
interpretability. Our model is the expansion of a combination of two
mathematical models: a reaction-diffusion system provides a framework for
modeling the flow of influences between local area groups and an ecological
system models the latent interactions between each query. Also, by leveraging
the concept of physics-informed neural networks, FluxCube achieves high
interpretability obtained from the parameters and high forecasting performance,
together. Extensive experiments on real datasets showed that FluxCube
outperforms comparable models in terms of the forecasting accuracy, and each
component in FluxCube contributes to the enhanced performance. We then show
some case studies that FluxCube can extract useful latent interactions between
queries and area groups. | [
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] |
How can we accurately recommend actions for users to control their devices at
home? Action recommendation for smart home has attracted increasing attention
due to its potential impact on the markets of virtual assistants and Internet
of Things (IoT). However, designing an effective action recommender system for
smart home is challenging because it requires handling context correlations,
considering both queried contexts and previous histories of users, and dealing
with capricious intentions in history. In this work, we propose SmartSense, an
accurate action recommendation method for smart home. For individual action,
SmartSense summarizes its device control and its temporal contexts in a
self-attentive manner, to reflect the importance of the correlation between
them. SmartSense then summarizes sequences of users considering queried
contexts in a query-attentive manner to extract the query-related patterns from
the sequential actions. SmartSense also transfers the commonsense knowledge
from routine data to better handle intentions in action sequences. As a result,
SmartSense addresses all three main challenges of action recommendation for
smart home, and achieves the state-of-the-art performance giving up to 9.8%
higher mAP@1 than the best competitor. | [
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Malicious software (malware) classification offers a unique challenge for
continual learning (CL) regimes due to the volume of new samples received on a
daily basis and the evolution of malware to exploit new vulnerabilities. On a
typical day, antivirus vendors receive hundreds of thousands of unique pieces
of software, both malicious and benign, and over the course of the lifetime of
a malware classifier, more than a billion samples can easily accumulate. Given
the scale of the problem, sequential training using continual learning
techniques could provide substantial benefits in reducing training and storage
overhead. To date, however, there has been no exploration of CL applied to
malware classification tasks. In this paper, we study 11 CL techniques applied
to three malware tasks covering common incremental learning scenarios,
including task, class, and domain incremental learning (IL). Specifically,
using two realistic, large-scale malware datasets, we evaluate the performance
of the CL methods on both binary malware classification (Domain-IL) and
multi-class malware family classification (Task-IL and Class-IL) tasks. To our
surprise, continual learning methods significantly underperformed naive Joint
replay of the training data in nearly all settings -- in some cases reducing
accuracy by more than 70 percentage points. A simple approach of selectively
replaying 20% of the stored data achieves better performance, with 50% of the
training time compared to Joint replay. Finally, we discuss potential reasons
for the unexpectedly poor performance of the CL techniques, with the hope that
it spurs further research on developing techniques that are more effective in
the malware classification domain. | [
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] |
Despite the success of AI-enabled onboard perception, on-ramp merging has
been one of the main challenges for autonomous driving. Due to limited sensing
range of onboard sensors, a merging vehicle can hardly observe main road
conditions and merge properly. By leveraging the wireless communications
between connected and automated vehicles (CAVs), a merging CAV has potential to
proactively obtain the intentions of nearby vehicles. However, CAVs can be
prone to inaccurate observations, such as the noisy basic safety messages (BSM)
and poor quality surveillance images. In this paper, we present a novel
approach for Robust on-ramp merge of CAVs via Augmented and Multi-modal
Reinforcement Learning, named by RAMRL. Specifically, we formulate the on-ramp
merging problem as a Markov decision process (MDP) by taking driving safety,
comfort driving behavior, and traffic efficiency into account. To provide
reliable merging maneuvers, we simultaneously leverage BSM and surveillance
images for multi-modal observation, which is used to learn a policy model
through proximal policy optimization (PPO). Moreover, to improve data
efficiency and provide better generalization performance, we train the policy
model with augmented data (e.g., noisy BSM and noisy surveillance images).
Extensive experiments are conducted with Simulation of Urban MObility (SUMO)
platform under two typical merging scenarios. Experimental results demonstrate
the effectiveness and efficiency of our robust on-ramp merging design. | [
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] |
The COVID-19 Omicron variant, reported to be the most immune evasive variant
of COVID-19, is resulting in a surge of COVID-19 cases globally. This has
caused schools, colleges, and universities in different parts of the world to
transition to online learning. As a result, social media platforms such as
Twitter are seeing an increase in conversations related to online learning in
the form of tweets. Mining such tweets to develop a dataset can serve as a data
resource for different applications and use-cases related to the analysis of
interest, views, opinions, perspectives, attitudes, and feedback towards online
learning during the current surge of COVID-19 cases caused by the Omicron
variant. Therefore, this work presents a large-scale open-access Twitter
dataset of conversations about online learning from different parts of the
world since the first detected case of the COVID-19 Omicron variant in November
2021. The dataset is compliant with the privacy policy, developer agreement,
and guidelines for content redistribution of Twitter, as well as with the FAIR
principles (Findability, Accessibility, Interoperability, and Reusability)
principles for scientific data management. The paper also briefly outlines some
potential applications in the fields of Big Data, Data Mining, Natural Language
Processing, and their related disciplines, with a specific focus on online
learning during this Omicron wave that may be studied, explored, and
investigated by using this dataset. | [
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Terahertz (THz) wireless networks are expected to catalyze the beyond fifth
generation (B5G) era. However, due to the directional nature and the
line-of-sight demand of THz links, as well as the ultra-dense deployment of THz
networks, a number of challenges that the medium access control (MAC) layer
needs to face are created. In more detail, the need of rethinking user
association and resource allocation strategies by incorporating artificial
intelligence (AI) capable of providing "real-time" solutions in complex and
frequently changing environments becomes evident. Moreover, to satisfy the
ultra-reliability and low-latency demands of several B5G applications, novel
mobility management approaches are required. Motivated by this, this article
presents a holistic MAC layer approach that enables intelligent user
association and resource allocation, as well as flexible and adaptive mobility
management, while maximizing systems' reliability through blockage
minimization. In more detail, a fast and centralized joint user association,
radio resource allocation, and blockage avoidance by means of a novel
metaheuristic-machine learning framework is documented, that maximizes the THz
networks performance, while minimizing the association latency by approximately
three orders of magnitude. To support, within the access point (AP) coverage
area, mobility management and blockage avoidance, a deep reinforcement learning
(DRL) approach for beam-selection is discussed. Finally, to support user
mobility between coverage areas of neighbor APs, a proactive hand-over
mechanism based on AI-assisted fast channel prediction is~reported. | [
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] |
Recent years have seen significant advances in explainable AI as the need to
understand deep learning models has gained importance with the increased
emphasis on trust and ethics in AI. Comprehensible models for sequential
decision tasks are a particular challenge as they require understanding not
only individual predictions but a series of predictions that interact with
environmental dynamics. We present a framework for learning comprehensible
models of sequential decision tasks in which agent strategies are characterized
using temporal logic formulas. Given a set of agent traces, we first cluster
the traces using a novel embedding method that captures frequent action
patterns. We then search for logical formulas that explain the agent strategies
in the different clusters. We evaluate our framework on combat scenarios in
StarCraft II (SC2), using traces from a handcrafted expert policy and a trained
reinforcement learning agent. We implemented a feature extractor for SC2
environments that extracts traces as sequences of high-level features
describing both the state of the environment and the agent's local behavior
from agent replays. We further designed a visualization tool depicting the
movement of units in the environment that helps understand how different task
conditions lead to distinct agent behavior patterns in each trace cluster.
Experimental results show that our framework is capable of separating agent
traces into distinct groups of behaviors for which our approach to strategy
inference produces consistent, meaningful, and easily understood strategy
descriptions. | [
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] |
We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a
novel Convolutional Neural Network (CNN) architecture that addresses, by
design, the open challenges of interpretability, formal verification, and logic
gate conversion. $\mathcal{TT}$net is built using CNNs' filters that are
equivalent to tractable truth tables and that we call Learning Truth Table
(LTT) blocks. The dual form of LTT blocks allows the truth tables to be easily
trained with gradient descent and makes these CNNs easy to interpret, verify
and infer. Specifically, $\mathcal{TT}$net is a deep CNN model that can be
automatically represented, after post-training transformation, as a sum of
Boolean decision trees, or as a sum of Disjunctive/Conjunctive Normal Form
(DNF/CNF) formulas, or as a compact Boolean logic circuit. We demonstrate the
effectiveness and scalability of $\mathcal{TT}$net on multiple datasets,
showing comparable interpretability to decision trees, fast complete/sound
formal verification, and scalable logic gate representation, all compared to
state-of-the-art methods. We believe this work represents a step towards making
CNNs more transparent and trustworthy for real-world critical applications. | [
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According to cognitive psychology and related disciplines, the development of
complex problem-solving behaviour in biological agents depends on hierarchical
cognitive mechanisms. Hierarchical reinforcement learning is a promising
computational approach that may eventually yield comparable problem-solving
behaviour in artificial agents and robots. However, to date the problem-solving
abilities of many human and non-human animals are clearly superior to those of
artificial systems. Here, we propose steps to integrate biologically inspired
hierarchical mechanisms to enable advanced problem-solving skills in artificial
agents. Therefore, we first review the literature in cognitive psychology to
highlight the importance of compositional abstraction and predictive
processing. Then we relate the gained insights with contemporary hierarchical
reinforcement learning methods. Interestingly, our results suggest that all
identified cognitive mechanisms have been implemented individually in isolated
computational architectures, raising the question of why there exists no single
unifying architecture that integrates them. As our final contribution, we
address this question by providing an integrative perspective on the
computational challenges to develop such a unifying architecture. We expect our
results to guide the development of more sophisticated cognitively inspired
hierarchical machine learning architectures. | [
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] |
Given a learning problem with real-world tradeoffs, which cost function
should the model be trained to optimize? This is the metric selection problem
in machine learning. Despite its practical interest, there is limited formal
guidance on how to select metrics for machine learning applications. This
thesis outlines metric elicitation as a principled framework for selecting the
performance metric that best reflects implicit user preferences. Once
specified, the evaluation metric can be used to compare and train models. In
this manuscript, we formalize the problem of Metric Elicitation and devise
novel strategies for eliciting classification performance metrics using
pairwise preference feedback over classifiers. Specifically, we provide novel
strategies for eliciting linear and linear-fractional metrics for binary and
multiclass classification problems, which are then extended to a framework that
elicits group-fair performance metrics in the presence of multiple sensitive
groups. All the elicitation strategies that we discuss are robust to both
finite sample and feedback noise, thus are useful in practice for real-world
applications. Using the tools and the geometric characterizations of the
feasible confusion statistics sets from the binary, multiclass, and
multiclass-multigroup classification setups, we further provide strategies to
elicit from a wider range of complex, modern multiclass metrics defined by
quadratic functions of confusion statistics by exploiting their local linear
structure. From application perspective, we also propose to use the metric
elicitation framework in optimizing complex black box metrics that is amenable
to deep network training. Lastly, to bring theory closer to practice, we
conduct a preliminary real-user study that shows the efficacy of the metric
elicitation framework in recovering the users' preferred performance metric in
a binary classification setup. | [
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In a decentralized household energy system comprised of various devices such
as home appliances, electric vehicles, and solar panels, end-users are able to
dig deeper into the system's details and further achieve energy sustainability
if they are presented with data on the electric energy consumption and
production at the granularity of the device. However, many databases in this
field are siloed from other domains, including solely information pertaining to
energy. This may result in the loss of information (e.g. weather) on each
device's energy use. Meanwhile, a large number of these datasets have been
extensively used in computational modeling techniques such as machine learning
models. While such computational approaches achieve great accuracy and
performance by concentrating only on a local view of datasets, model
reliability cannot be guaranteed since such models are very vulnerable to data
input fluctuations when information omission is taken into account. This
article tackles the data isolation issue in the field of smart energy systems
by examining Semantic Web methods on top of a household energy system. We offer
an ontology-based approach for managing decentralized data at the device-level
resolution in a system. As a consequence, the scope of the data associated with
each device may easily be expanded in an interoperable manner throughout the
Web, and additional information, such as weather, can be obtained from the Web,
provided that the data is organized according to W3C standards. | [
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Entity Alignment (EA) aims to match equivalent entities that refer to the
same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most
neural EA models cannot be applied to large-scale real-life KGs due to their
excessive consumption of GPU memory and time. One promising solution is to
divide a large EA task into several subtasks such that each subtask only needs
to match two small subgraphs of the original KGs. However, it is challenging to
divide the EA task without losing effectiveness. Existing methods display low
coverage of potential mappings, insufficient evidence in context graphs, and
largely differing subtask sizes.
In this work, we design the DivEA framework for large-scale EA with
high-quality task division. To include in the EA subtasks a high proportion of
the potential mappings originally present in the large EA task, we devise a
counterpart discovery method that exploits the locality principle of the EA
task and the power of trained EA models. Unique to our counterpart discovery
method is the explicit modelling of the chance of a potential mapping. We also
introduce an evidence passing mechanism to quantify the informativeness of
context entities and find the most informative context graphs with flexible
control of the subtask size. Extensive experiments show that DivEA achieves
higher EA performance than alternative state-of-the-art solutions. | [
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] |
Machine learning has recently gained traction as a way to overcome the slow
accelerator generation and implementation process on an FPGA. It can be used to
build performance and resource usage models that enable fast early-stage design
space exploration. First, training requires large amounts of data (features
extracted from design synthesis and implementation tools), which is
cost-inefficient because of the time-consuming accelerator design and
implementation process. Second, a model trained for a specific environment
cannot predict performance or resource usage for a new, unknown environment. In
a cloud system, renting a platform for data collection to build an ML model can
significantly increase the total-cost-ownership (TCO) of a system. Third,
ML-based models trained using a limited number of samples are prone to
overfitting. To overcome these limitations, we propose LEAPER, a transfer
learning-based approach for prediction of performance and resource usage in
FPGA-based systems. The key idea of LEAPER is to transfer an ML-based
performance and resource usage model trained for a low-end edge environment to
a new, high-end cloud environment to provide fast and accurate predictions for
accelerator implementation. Experimental results show that LEAPER (1) provides,
on average across six workloads and five FPGAs, 85% accuracy when we use our
transferred model for prediction in a cloud environment with 5-shot learning
and (2) reduces design-space exploration time for accelerator implementation on
an FPGA by 10x, from days to only a few hours. | [
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] |
Fully supervised log anomaly detection methods suffer the heavy burden of
annotating massive unlabeled log data. Recently, many semi-supervised methods
have been proposed to reduce annotation costs with the help of parsed
templates. However, these methods consider each keyword independently, which
disregards the correlation between keywords and the contextual relationships
among log sequences. In this paper, we propose a novel weakly supervised log
anomaly detection framework, named LogLG, to explore the semantic connections
among keywords from sequences. Specifically, we design an end-to-end iterative
process, where the keywords of unlabeled logs are first extracted to construct
a log-event graph. Then, we build a subgraph annotator to generate pseudo
labels for unlabeled log sequences. To ameliorate the annotation quality, we
adopt a self-supervised task to pre-train a subgraph annotator. After that, a
detection model is trained with the generated pseudo labels. Conditioned on the
classification results, we re-extract the keywords from the log sequences and
update the log-event graph for the next iteration. Experiments on five
benchmarks validate the effectiveness of LogLG for detecting anomalies on
unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly
supervised method, achieves significant performance improvements compared to
existing methods. | [
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Federated learning (FL) is a decentralized method enabling hospitals to
collaboratively learn a model without sharing private patient data for
training. In FL, participant hospitals periodically exchange training results
rather than training samples with a central server. However, having access to
model parameters or gradients can expose private training data samples. To
address this challenge, we adopt secure multiparty computation (SMC) to
establish a privacy-preserving federated learning framework. In our proposed
method, the hospitals are divided into clusters. After local training, each
hospital splits its model weights among other hospitals in the same cluster
such that no single hospital can retrieve other hospitals' weights on its own.
Then, all hospitals sum up the received weights, sending the results to the
central server. Finally, the central server aggregates the results, retrieving
the average of models' weights and updating the model without having access to
individual hospitals' weights. We conduct experiments on a publicly available
repository, The Cancer Genome Atlas (TCGA). We compare the performance of the
proposed framework with differential privacy and federated averaging as the
baseline. The results reveal that compared to differential privacy, our
framework can achieve higher accuracy with no privacy leakage risk at a cost of
higher communication overhead. | [
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] |
Causal graph, as an effective and powerful tool for causal modeling, is
usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems
usually involve feedback loops, defined as the cyclic process of recommending
items, incorporating user feedback in model updates, and repeating the
procedure. As a result, it is important to incorporate loops into the causal
graphs to accurately model the dynamic and iterative data generation process
for recommender systems. However, feedback loops are not always beneficial
since over time they may encourage more and more narrowed content exposure,
which if left unattended, may results in echo chambers. As a result, it is
important to understand when the recommendations will lead to echo chambers and
how to mitigate echo chambers without hurting the recommendation performance.
In this paper, we design a causal graph with loops to describe the dynamic
process of recommendation. We then take Markov process to analyze the
mathematical properties of echo chamber such as the conditions that lead to
echo chambers. Inspired by the theoretical analysis, we propose a Dynamic
Causal Collaborative Filtering ($\partial$CCF) model, which estimates users'
post-intervention preference on items based on back-door adjustment and
mitigates echo chamber with counterfactual reasoning. Multiple experiments are
conducted on real-world datasets and results show that our framework can
mitigate echo chambers better than other state-of-the-art frameworks while
achieving comparable recommendation performance with the base recommendation
models. | [
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] |
Knowledge tracing (KT) is a field of study that predicts the future
performance of students based on prior performance datasets collected from
educational applications such as intelligent tutoring systems, learning
management systems, and online courses. Some previous studies on KT have
concentrated only on the interpretability of the model, whereas others have
focused on enhancing the performance. Models that consider both
interpretability and the performance improvement have been insufficient.
Moreover, models that focus on performance improvements have not shown an
overwhelming performance compared with existing models. In this study, we
propose MonaCoBERT, which achieves the best performance on most benchmark
datasets and has significant interpretability. MonaCoBERT uses a BERT-based
architecture with monotonic convolutional multihead attention, which reflects
forgetting behavior of the students and increases the representation power of
the model. We can also increase the performance and interpretability using a
classical test-theory-based (CTT-based) embedding strategy that considers the
difficulty of the question. To determine why MonaCoBERT achieved the best
performance and interpret the results quantitatively, we conducted ablation
studies and additional analyses using Grad-CAM, UMAP, and various visualization
techniques. The analysis results demonstrate that both attention components
complement one another and that CTT-based embedding represents information on
both global and local difficulties. We also demonstrate that our model
represents the relationship between concepts. | [
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] |
We developed an inherently interpretable multilevel Bayesian framework for
representing variation in regression coefficients that mimics the piecewise
linearity of ReLU-activated deep neural networks. We used the framework to
formulate a survival model for using medical claims to predict hospital
readmission and death that focuses on discharge placement, adjusting for
confounding in estimating causal local average treatment effects. We trained
the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on
their 2009--2011 inpatient episodes, and then tested the model on 2012
episodes. The model scored an AUROC of approximately 0.76 on predicting
all-cause readmissions -- defined using official Centers for Medicare and
Medicaid Services (CMS) methodology -- or death within 30-days of discharge,
being competitive against XGBoost and a Bayesian deep neural network,
demonstrating that one need-not sacrifice interpretability for accuracy.
Crucially, as a regression model, we provide what blackboxes cannot -- the
exact gold-standard global interpretation of the model, identifying relative
risk factors and quantifying the effect of discharge placement. We also show
that the posthoc explainer SHAP fails to provide accurate explanations. | [
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] |
At the age of big data, recommender systems have shown remarkable success as
a key means of information filtering in our daily life. Recent years have
witnessed the technical development of recommender systems, from perception
learning to cognition reasoning which intuitively build the task of
recommendation as the procedure of logical reasoning and have achieve
significant improvement. However, the logical statement in reasoning implicitly
admits irrelevance of ordering, even does not consider time information which
plays an important role in many recommendation tasks. Furthermore,
recommendation model incorporated with temporal context would tend to be
self-attentive, i.e., automatically focus more (less) on the relevance
(irrelevance), respectively.
To address these issues, in this paper, we propose a Time-aware
Self-Attention with Neural Collaborative Reasoning (TiSANCR) based
recommendation model, which integrates temporal patterns and self-attention
mechanism into reasoning-based recommendation. Specially, temporal patterns
represented by relative time, provide context and auxiliary information to
characterize the user's preference in recommendation, while self-attention is
leveraged to distill informative patterns and suppress irrelevances. Therefore,
the fusion of self-attentive temporal information provides deeper
representation of user's preference. Extensive experiments on benchmark
datasets demonstrate that the proposed TiSANCR achieves significant improvement
and consistently outperforms the state-of-the-art recommendation methods. | [
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Many fundamental properties of a quantum system are captured by its
Hamiltonian and ground state. Despite the significance of ground states
preparation (GSP), this task is classically intractable for large-scale
Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern
quantum machines, have emerged as a leading protocol to conquer this issue. As
such, how to enhance the performance of QNNs becomes a crucial topic in GSP.
Empirical evidence showed that QNNs with handcraft symmetric ansatzes generally
experience better trainability than those with asymmetric ansatzes, while
theoretical explanations have not been explored. To fill this knowledge gap,
here we propose the effective quantum neural tangent kernel (EQNTK) and connect
this concept with over-parameterization theory to quantify the convergence of
QNNs towards the global optima. We uncover that the advance of symmetric
ansatzes attributes to their large EQNTK value with low effective dimension,
which requests few parameters and quantum circuit depth to reach the
over-parameterization regime permitting a benign loss landscape and fast
convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme
to automatically tailor a symmetric ansatz from an over-parameterized and
asymmetric one to greatly improve the performance of QNNs when the explicit
symmetry information of Hamiltonian is unavailable. Extensive numerical
simulations are conducted to validate the analytical results of EQNTK and the
effectiveness of SP. | [
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] |
In this study, we focus on learning Hamiltonian systems, which involves
predicting the coordinate (q) and momentum (p) variables generated by a
symplectic mapping. Based on Chen & Tao (2021), the symplectic mapping is
represented by a generating function. To extend the prediction time period, we
develop a new learning scheme by splitting the time series (q_i, p_i) into
several partitions. We then train a large-step neural network (LSNN) to
approximate the generating function between the first partition (i.e. the
initial condition) and each one of the remaining partitions. This partition
approach makes our LSNN effectively suppress the accumulative error when
predicting the system evolution. Then we train the LSNN to learn the motions of
the 2:3 resonant Kuiper belt objects for a long time period of 25000 yr. The
results show that there are two significant improvements over the neural
network constructed in our previous work (Li et al. 2022): (1) the conservation
of the Jacobi integral, and (2) the highly accurate predictions of the orbital
evolution. Overall, we propose that the designed LSNN has the potential to
considerably improve predictions of the long-term evolution of more general
Hamiltonian systems. | [
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] |
As an emerging technology in the era of Industry 4.0, digital twin is gaining
unprecedented attention because of its promise to further optimize process
design, quality control, health monitoring, decision and policy making, and
more, by comprehensively modeling the physical world as a group of
interconnected digital models. In a two-part series of papers, we examine the
fundamental role of different modeling techniques, twinning enabling
technologies, and uncertainty quantification and optimization methods commonly
used in digital twins. This first paper presents a thorough literature review
of digital twin trends across many disciplines currently pursuing this area of
research. Then, digital twin modeling and twinning enabling technologies are
further analyzed by classifying them into two main categories:
physical-to-virtual, and virtual-to-physical, based on the direction in which
data flows. Finally, this paper provides perspectives on the trajectory of
digital twin technology over the next decade, and introduces a few emerging
areas of research which will likely be of great use in future digital twin
research. In part two of this review, the role of uncertainty quantification
and optimization are discussed, a battery digital twin is demonstrated, and
more perspectives on the future of digital twin are shared. | [
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Using artificial intelligence (AI) to automatically test a game remains a
critical challenge for the development of richer and more complex game worlds
and for the advancement of AI at large. One of the most promising methods for
achieving that long-standing goal is the use of generative AI agents, namely
procedural personas, that attempt to imitate particular playing behaviors which
are represented as rules, rewards, or human demonstrations. All research
efforts for building those generative agents, however, have focused solely on
playing behavior which is arguably a narrow perspective of what a player
actually does in a game. Motivated by this gap in the existing state of the
art, in this paper we extend the notion of behavioral procedural personas to
cater for player experience, thus examining generative agents that can both
behave and experience their game as humans would. For that purpose, we employ
the Go-Explore reinforcement learning paradigm for training human-like
procedural personas, and we test our method on behavior and experience
demonstrations of more than 100 players of a racing game. Our findings suggest
that the generated agents exhibit distinctive play styles and experience
responses of the human personas they were designed to imitate. Importantly, it
also appears that experience, which is tied to playing behavior, can be a
highly informative driver for better behavioral exploration. | [
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] |
Signed and directed networks are ubiquitous in real-world applications.
However, there has been relatively little work proposing spectral graph neural
networks (GNNs) for such networks. Here we introduce a signed directed
Laplacian matrix, which we call the magnetic signed Laplacian, as a natural
generalization of both the signed Laplacian on signed graphs and the magnetic
Laplacian on directed graphs. We then use this matrix to construct a novel
efficient spectral GNN architecture and conduct extensive experiments on both
node clustering and link prediction tasks. In these experiments, we consider
tasks related to signed information, tasks related to directional information,
and tasks related to both signed and directional information. We demonstrate
that our proposed spectral GNN is effective for incorporating both signed and
directional information, and attains leading performance on a wide range of
data sets. Additionally, we provide a novel synthetic network model, which we
refer to as the Signed Directed Stochastic Block Model, and a number of novel
real-world data sets based on lead-lag relationships in financial time series. | [
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] |
Estimation of physical quantities is at the core of most scientific research
and the use of quantum devices promises to enhance its performances. In real
scenarios, it is fundamental to consider that the resources are limited and
Bayesian adaptive estimation represents a powerful approach to efficiently
allocate, during the estimation process, all the available resources. However,
this framework relies on the precise knowledge of the system model, retrieved
with a fine calibration that often results computationally and experimentally
demanding. Here, we introduce a model-free and deep learning-based approach to
efficiently implement realistic Bayesian quantum metrology tasks accomplishing
all the relevant challenges, without relying on any a-priori knowledge on the
system. To overcome this need, a neural network is trained directly on
experimental data to learn the multiparameter Bayesian update. Then, the system
is set at its optimal working point through feedbacks provided by a
reinforcement learning algorithm trained to reconstruct and enhance experiment
heuristics of the investigated quantum sensor. Notably, we prove experimentally
the achievement of higher estimation performances than standard methods,
demonstrating the strength of the combination of these two black-box algorithms
on an integrated photonic circuit. This work represents an important step
towards fully artificial intelligence-based quantum metrology. | [
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] |
Index funds are substantially preferred by investors nowadays, and market
sensitivities are instrumental in managing index funds. An index fund is a
mutual fund aiming to track the returns of a predefined market index (e.g., the
S&P 500). A basic strategy to manage an index fund is replicating the index's
constituents and weights identically, which is, however, cost-ineffective and
impractical. To address this issue, it is required to replicate the index
partially with accurately predicted market sensitivities. Accordingly, we
propose a novel partial-replication method via learning to predict market
sensitivities. We first examine deep-learning models to predict market
sensitivities in a supervised manner with our data-processing methods. Then, we
propose a partial-index-tracking optimization model controlling the net
predicted market sensitivities of the portfolios and index to be the same.
These processes' efficacy is corroborated by our experiments on the Korea Stock
Price Index 200. Our experiments show a significant reduction of the prediction
errors compared with historical estimations and competitive tracking errors of
replicating the index utilizing fewer than half of the entire constituents.
Therefore, we show that applying deep learning to predict market sensitivities
is promising and that our portfolio construction methods are practically
effective. Additionally, to our knowledge, this is the first study addressing
market sensitivities focused on deep learning. | [
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