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We consider the problem of optimizing the distribution operations at a drone
hub that dispatches drones to different geographic locations generating
stochastic demands for medical supplies. Drone delivery is an innovative method
that introduces many benefits, such as low-contact delivery, thereby reducing
the spread of pandemic and vaccine-preventable diseases. While we focus on
medical supply delivery for this work, drone delivery is suitable for many
other items, including food, postal parcels, and e-commerce. In this paper, our
goal is to address drone delivery challenges related to the stochastic demands
of different geographic locations. We consider different classes of demand
related to geographic locations that require different flight ranges, which is
directly related to the amount of charge held in a drone battery. We classify
the stochastic demands based on their distance from the drone hub, use a Markov
decision process to model the problem, and perform computational tests using
realistic data representing a prominent drone delivery company. We solve the
problem using a reinforcement learning method and show its high performance
compared with the exact solution found using dynamic programming. Finally, we
analyze the results and provide insights for managing the drone hub operations. | [
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] |
Node clustering is a powerful tool in the analysis of networks. We introduce
a graph neural network framework, named DIGRAC, to obtain node embeddings for
directed networks in a self-supervised manner, including a novel probabilistic
imbalance loss, which can be used for network clustering. Here, we propose
\textit{directed flow imbalance} measures, which are tightly related to
directionality, to reveal clusters in the network even when there is no density
difference between clusters. In contrast to standard approaches in the
literature, in this paper, directionality is not treated as a nuisance, but
rather contains the main signal. DIGRAC optimizes directed flow imbalance for
clustering without requiring label supervision, unlike existing graph neural
network methods, and can naturally incorporate node features, unlike existing
spectral methods. Extensive experimental results on synthetic data, in the form
of directed stochastic block models, and real-world data at different scales,
demonstrate that our method, based on flow imbalance, attains state-of-the-art
results on directed graph clustering when compared against 10 state-of-the-art
methods from the literature, for a wide range of noise and sparsity levels,
graph structures, and topologies, and even outperforms supervised methods. | [
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] |
The sequential patterns within the user interactions are pivotal for
representing the user's preference and capturing latent relationships among
items. The recent advancements of sequence modeling by Transformers advocate
the community to devise more effective encoders for the sequential
recommendation. Most existing sequential methods assume users are
deterministic. However, item-item transitions might fluctuate significantly in
several item aspects and exhibit randomness of user interests. This
\textit{stochastic characteristics} brings up a solid demand to include
uncertainties in representing sequences and items. Additionally, modeling
sequences and items with uncertainties expands users' and items' interaction
spaces, thus further alleviating cold-start problems.
In this work, we propose a Distribution-based Transformer for Sequential
Recommendation (DT4SR), which injects uncertainties into sequential modeling.
We use Elliptical Gaussian distributions to describe items and sequences with
uncertainty. We describe the uncertainty in items and sequences as Elliptical
Gaussian distribution. And we adopt Wasserstein distance to measure the
similarity between distributions. We devise two novel Trans-formers for
modeling mean and covariance, which guarantees the positive-definite property
of distributions. The proposed method significantly outperforms the
state-of-the-art methods. The experiments on three benchmark datasets also
demonstrate its effectiveness in alleviating cold-start issues. The code is
available inhttps://github.com/DyGRec/DT4SR. | [
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] |
To reduce the time-to-market and access to state-of-the-art techniques, CNN
hardware mapping and deployment on embedded accelerators are often outsourced
to untrusted third parties, which is going to be more prevalent in futuristic
artificial intelligence of things (AIoT) systems. These AIoT systems anticipate
horizontal collaboration among different resource-constrained AIoT node
devices, where CNN layers are partitioned and these devices collaboratively
compute complex CNN tasks. This horizontal collaboration opens another attack
surface to the CNN-based application, like inserting the hardware Trojans (HT)
into the embedded accelerators designed for the CNN. Therefore, there is a dire
need to explore this attack surface for designing secure embedded hardware
accelerators for CNNs. Towards this goal, in this paper, we exploited this
attack surface to propose an HT-based attack called FeSHI. Since in horizontal
collaboration of RC AIoT devices different sections of CNN architectures are
outsourced to different untrusted third parties, the attacker may not know the
input image, but it has access to the layer-by-layer output feature maps
information for the assigned sections of the CNN architecture. This attack
exploits the statistical distribution, i.e., Gaussian distribution, of the
layer-by-layer feature maps of the CNN to design two triggers for stealthy HT
with a very low probability of triggering. Also, three different novel,
stealthy and effective trigger designs are proposed. | [
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] |
The influence maximization (IM) problem aims at finding a subset of seed
nodes in a social network that maximize the spread of influence. In this study,
we focus on a sub-class of IM problems, where whether the nodes are willing to
be the seeds when being invited is uncertain, called contingency-aware IM. Such
contingency aware IM is critical for applications for non-profit organizations
in low resource communities (e.g., spreading awareness of disease prevention).
Despite the initial success, a major practical obstacle in promoting the
solutions to more communities is the tremendous runtime of the greedy
algorithms and the lack of high performance computing (HPC) for the non-profits
in the field -- whenever there is a new social network, the non-profits usually
do not have the HPCs to recalculate the solutions. Motivated by this and
inspired by the line of works that use reinforcement learning (RL) to address
combinatorial optimization on graphs, we formalize the problem as a Markov
Decision Process (MDP), and use RL to learn an IM policy over historically seen
networks, and generalize to unseen networks with negligible runtime at test
phase. To fully exploit the properties of our targeted problem, we propose two
technical innovations that improve the existing methods, including
state-abstraction and theoretically grounded reward shaping. Empirical results
show that our method achieves influence as high as the state-of-the-art methods
for contingency-aware IM, while having negligible runtime at test phase. | [
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The digital media, identified as computational propaganda provides a pathway
for propaganda to expand its reach without limit. State-backed propaganda aims
to shape the audiences' cognition toward entities in favor of a certain
political party or authority. Furthermore, it has become part of modern
information warfare used in order to gain an advantage over opponents. Most of
the current studies focus on using machine learning, quantitative, and
qualitative methods to distinguish if a certain piece of information on social
media is propaganda. Mainly conducted on English content, but very little
research addresses Chinese Mandarin content. From propaganda detection, we want
to go one step further to provide more fine-grained information on propaganda
techniques that are applied. In this research, we aim to bridge the information
gap by providing a multi-labeled propaganda techniques dataset in Mandarin
based on a state-backed information operation dataset provided by Twitter. In
addition to presenting the dataset, we apply a multi-label text classification
using fine-tuned BERT. Potentially this could help future research in detecting
state-backed propaganda online especially in a cross-lingual context and cross
platforms identity consolidation. | [
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] |
Modern kernel-based two-sample tests have shown great success in
distinguishing complex, high-dimensional distributions with appropriate learned
kernels. Previous work has demonstrated that this kernel learning procedure
succeeds, assuming a considerable number of observed samples from each
distribution. In realistic scenarios with very limited numbers of data samples,
however, it can be challenging to identify a kernel powerful enough to
distinguish complex distributions. We address this issue by introducing the
problem of meta two-sample testing (M2ST), which aims to exploit (abundant)
auxiliary data on related tasks to find an algorithm that can quickly identify
a powerful test on new target tasks. We propose two specific algorithms for
this task: a generic scheme which improves over baselines and a more tailored
approach which performs even better. We provide both theoretical justification
and empirical evidence that our proposed meta-testing schemes out-perform
learning kernel-based tests directly from scarce observations, and identify
when such schemes will be successful. | [
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] |
Explainable Artificial Intelligence (XAI) is a set of techniques that allows
the understanding of both technical and non-technical aspects of Artificial
Intelligence (AI) systems. XAI is crucial to help satisfying the increasingly
important demand of \emph{trustworthy} Artificial Intelligence, characterized
by fundamental characteristics such as respect of human autonomy, prevention of
harm, transparency, accountability, etc. Within XAI techniques, counterfactual
explanations aim to provide to end users a set of features (and their
corresponding values) that need to be changed in order to achieve a desired
outcome. Current approaches rarely take into account the feasibility of actions
needed to achieve the proposed explanations, and in particular they fall short
of considering the causal impact of such actions. In this paper, we present
Counterfactual Explanations as Interventions in Latent Space (CEILS), a
methodology to generate counterfactual explanations capturing by design the
underlying causal relations from the data, and at the same time to provide
feasible recommendations to reach the proposed profile. Moreover, our
methodology has the advantage that it can be set on top of existing
counterfactuals generator algorithms, thus minimising the complexity of
imposing additional causal constrains. We demonstrate the effectiveness of our
approach with a set of different experiments using synthetic and real datasets
(including a proprietary dataset of the financial domain). | [
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Multi-hop logical reasoning is an established problem in the field of
representation learning on knowledge graphs (KGs). It subsumes both one-hop
link prediction as well as other more complex types of logical queries.
Existing algorithms operate only on classical, triple-based graphs, whereas
modern KGs often employ a hyper-relational modeling paradigm. In this paradigm,
typed edges may have several key-value pairs known as qualifiers that provide
fine-grained context for facts. In queries, this context modifies the meaning
of relations, and usually reduces the answer set. Hyper-relational queries are
often observed in real-world KG applications, and existing approaches for
approximate query answering cannot make use of qualifier pairs. In this work,
we bridge this gap and extend the multi-hop reasoning problem to
hyper-relational KGs allowing to tackle this new type of complex queries.
Building upon recent advancements in Graph Neural Networks and query embedding
techniques, we study how to embed and answer hyper-relational conjunctive
queries. Besides that, we propose a method to answer such queries and
demonstrate in our experiments that qualifiers improve query answering on a
diverse set of query patterns. | [
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] |
Attributed network embedding (ANE) is to learn low-dimensional vectors so
that not only the network structure but also node attributes can be preserved
in the embedding space. Existing ANE models do not consider the specific
combination between graph structure and attributes. While each node has its
structural characteristics, such as highly-interconnected neighbors along with
their certain patterns of attribute distribution, each node's neighborhood
should be not only depicted by multi-hop nodes, but consider certain clusters
or social circles. To model such information, in this paper, we propose a novel
ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE).
The basic idea of CoANE is to model the context attributes that each node's
involved diverse patterns, and apply the convolutional mechanism to encode
positional information by treating each attribute as a channel. The learning of
context co-occurrence can capture the latent social circles of each node. To
better encode structural and semantic knowledge of nodes, we devise a three-way
objective function, consisting of positive graph likelihood, contextual
negative sampling, and attribute reconstruction. We conduct experiments on five
real datasets in the tasks of link prediction, node label classification, and
node clustering. The results exhibit that CoANE can significantly outperform
state-of-the-art ANE models. | [
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] |
Existing observational approaches for learning human preferences, such as
inverse reinforcement learning, usually make strong assumptions about the
observability of the human's environment. However, in reality, people make many
important decisions under uncertainty. To better understand preference learning
in these cases, we study the setting of inverse decision theory (IDT), a
previously proposed framework where a human is observed making non-sequential
binary decisions under uncertainty. In IDT, the human's preferences are
conveyed through their loss function, which expresses a tradeoff between
different types of mistakes. We give the first statistical analysis of IDT,
providing conditions necessary to identify these preferences and characterizing
the sample complexity -- the number of decisions that must be observed to learn
the tradeoff the human is making to a desired precision. Interestingly, we show
that it is actually easier to identify preferences when the decision problem is
more uncertain. Furthermore, uncertain decision problems allow us to relax the
unrealistic assumption that the human is an optimal decision maker but still
identify their exact preferences; we give sample complexities in this
suboptimal case as well. Our analysis contradicts the intuition that partial
observability should make preference learning more difficult. It also provides
a first step towards understanding and improving preference learning methods
for uncertain and suboptimal humans. | [
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] |
Path planning, the problem of efficiently discovering high-reward
trajectories, often requires optimizing a high-dimensional and multimodal
reward function. Popular approaches like CEM and CMA-ES greedily focus on
promising regions of the search space and may get trapped in local maxima. DOO
and VOOT balance exploration and exploitation, but use space partitioning
strategies independent of the reward function to be optimized. Recently, LaMCTS
empirically learns to partition the search space in a reward-sensitive manner
for black-box optimization. In this paper, we develop a novel formal regret
analysis for when and why such an adaptive region partitioning scheme works. We
also propose a new path planning method LaP3 which improves the function value
estimation within each sub-region, and uses a latent representation of the
search space. Empirically, LaP3 outperforms existing path planning methods in
2D navigation tasks, especially in the presence of difficult-to-escape local
optima, and shows benefits when plugged into the planning components of
model-based RL such as PETS. These gains transfer to highly multimodal
real-world tasks, where we outperform strong baselines in compiler phase
ordering by up to 39% on average across 9 tasks, and in molecular design by up
to 0.4 on properties on a 0-1 scale. Code is available at
https://github.com/yangkevin2/neurips2021-lap3. | [
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] |
Over the past two decades, recommender systems have attracted a lot of
interest due to the explosion in the amount of data in online applications. A
particular attention has been paid to collaborative filtering, which is the
most widely used in applications that involve information recommendations.
Collaborative filtering (CF) uses the known preference of a group of users to
make predictions and recommendations about the unknown preferences of other
users (recommendations are made based on the past behavior of users). First
introduced in the 1990s, a wide variety of increasingly successful models have
been proposed. Due to the success of machine learning techniques in many areas,
there has been a growing emphasis on the application of such algorithms in
recommendation systems. In this article, we present an overview of the CF
approaches for recommender systems, their two main categories, and their
evaluation metrics. We focus on the application of classical Machine Learning
algorithms to CF recommender systems by presenting their evolution from their
first use-cases to advanced Machine Learning models. We attempt to provide a
comprehensive and comparative overview of CF systems (with python
implementations) that can serve as a guideline for research and practice in
this area. | [
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] |
At present, people usually use some methods based on convolutional neural
networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have
limitations in perceiving global dependencies, which is not adequate for common
EEG paradigms with a strong overall relationship. Regarding this issue, we
propose a novel EEG decoding method that mainly relies on the attention
mechanism. The EEG data is firstly preprocessed and spatially filtered. And
then, we apply attention transforming on the feature-channel dimension so that
the model can enhance more relevant spatial features. The most crucial step is
to slice the data in the time dimension for attention transforming, and finally
obtain a highly distinguishable representation. At this time, global averaging
pooling and a simple fully-connected layer are used to classify different
categories of EEG data. Experiments on two public datasets indicate that the
strategy of attention transforming effectively utilizes spatial and temporal
features. And we have reached the level of the state-of-the-art in
multi-classification of EEG, with fewer parameters. As far as we know, it is
the first time that a detailed and complete method based on the transformer
idea has been proposed in this field. It has good potential to promote the
practicality of brain-computer interface (BCI). The source code can be found
at: \textit{https://github.com/anranknight/EEG-Transformer}. | [
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] |
Self-training is an effective approach to semi-supervised learning. The key
idea is to let the learner itself iteratively generate "pseudo-supervision" for
unlabeled instances based on its current hypothesis. In combination with
consistency regularization, pseudo-labeling has shown promising performance in
various domains, for example in computer vision. To account for the
hypothetical nature of the pseudo-labels, these are commonly provided in the
form of probability distributions. Still, one may argue that even a probability
distribution represents an excessive level of informedness, as it suggests that
the learner precisely knows the ground-truth conditional probabilities. In our
approach, we therefore allow the learner to label instances in the form of
credal sets, that is, sets of (candidate) probability distributions. Thanks to
this increased expressiveness, the learner is able to represent uncertainty and
a lack of knowledge in a more flexible and more faithful manner. To learn from
weakly labeled data of that kind, we leverage methods that have recently been
proposed in the realm of so-called superset learning. In an exhaustive
empirical evaluation, we compare our methodology to state-of-the-art
self-supervision approaches, showing competitive to superior performance
especially in low-label scenarios incorporating a high degree of uncertainty. | [
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] |
Dynamical variational autoencoders (DVAEs) are a class of deep generative
models with latent variables, dedicated to model time series of
high-dimensional data. DVAEs can be considered as extensions of the variational
autoencoder (VAE) that include temporal dependencies between successive
observed and/or latent vectors. Previous work has shown the interest of using
DVAEs over the VAE for speech spectrograms modeling. Independently, the VAE has
been successfully applied to speech enhancement in noise, in an unsupervised
noise-agnostic set-up that requires neither noise samples nor noisy speech
samples at training time, but only requires clean speech signals. In this
paper, we extend these works to DVAE-based single-channel unsupervised speech
enhancement, hence exploiting both speech signals unsupervised representation
learning and dynamics modeling. We propose an unsupervised speech enhancement
algorithm that combines a DVAE speech prior pre-trained on clean speech signals
with a noise model based on nonnegative matrix factorization, and we derive a
variational expectation-maximization (VEM) algorithm to perform speech
enhancement. The algorithm is presented with the most general DVAE formulation
and is then applied with three specific DVAE models to illustrate the
versatility of the framework. Experimental results show that the proposed
DVAE-based approach outperforms its VAE-based counterpart, as well as several
supervised and unsupervised noise-dependent baselines, especially when the
noise type is unseen during training. | [
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] |
Whilst recent neural text-to-speech (TTS) approaches produce high-quality
speech, they typically require a large amount of recordings from the target
speaker. In previous work, a 3-step method was proposed to generate
high-quality TTS while greatly reducing the amount of data required for
training. However, we have observed a ceiling effect in the level of
naturalness achievable for highly expressive voices when using this approach.
In this paper, we present a method for building highly expressive TTS voices
with as little as 15 minutes of speech data from the target speaker. Compared
to the current state-of-the-art approach, our proposed improvements close the
gap to recordings by 23.3% for naturalness of speech and by 16.3% for speaker
similarity. Further, we match the naturalness and speaker similarity of a
Tacotron2-based full-data (~10 hours) model using only 15 minutes of target
speaker data, whereas with 30 minutes or more, we significantly outperform it.
The following improvements are proposed: 1) changing from an autoregressive,
attention-based TTS model to a non-autoregressive model replacing attention
with an external duration model and 2) an additional Conditional Generative
Adversarial Network (cGAN) based fine-tuning step. | [
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Anime is quite well-received today, especially among the younger generations.
With many genres of available shows, more and more people are increasingly
getting attracted to this niche section of the entertainment industry. As anime
has recently garnered mainstream attention, we have insufficient information
regarding users' penchant and watching habits. Therefore, it is an uphill task
to build a recommendation engine for this relatively obscure entertainment
medium. In this attempt, we have built a novel hybrid recommendation system
that could act both as a recommendation system and as a means of exploring new
anime genres and titles. We have analyzed the general trends in this field and
the users' watching habits for coming up with our efficacious solution. Our
solution employs deep autoencoders for the tasks of predicting ratings and
generating embeddings. Following this, we formed clusters using the embeddings
of the anime titles. These clusters form the search space for anime with
similarities and are used to find anime similar to the ones liked and disliked
by the user. This method, combined with the predicted ratings, forms the novel
hybrid filter. In this article, we have demonstrated this idea and compared the
performance of our implemented model with the existing state-of-the-art
techniques. | [
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Deep neural networks have recently shown great success in the task of blind
source separation, both under monaural and binaural settings. Although these
methods were shown to produce high-quality separations, they were mainly
applied under offline settings, in which the model has access to the full input
signal while separating the signal. In this study, we convert a non-causal
state-of-the-art separation model into a causal and real-time model and
evaluate its performance under both online and offline settings. We compare the
performance of the proposed model to several baseline methods under anechoic,
noisy, and noisy-reverberant recording conditions while exploring both monaural
and binaural inputs and outputs. Our findings shed light on the relative
difference between causal and non-causal models when performing separation. Our
stateful implementation for online separation leads to a minor drop in
performance compared to the offline model; 0.8dB for monaural inputs and 0.3dB
for binaural inputs while reaching a real-time factor of 0.65. Samples can be
found under the following link:
https://kwanum.github.io/sagrnnc-stream-results/. | [
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] |
The learning efficiency and generalization ability of an intelligent agent
can be greatly improved by utilizing a useful set of skills. However, the
design of robot skills can often be intractable in real-world applications due
to the prohibitive amount of effort and expertise that it requires. In this
work, we introduce Skill Learning In Diversified Environments (SLIDE), a method
to discover generalizable skills via automated generation of a diverse set of
tasks. As opposed to prior work on unsupervised discovery of skills which
incentivizes the skills to produce different outcomes in the same environment,
our method pairs each skill with a unique task produced by a trainable task
generator. To encourage generalizable skills to emerge, our method trains each
skill to specialize in the paired task and maximizes the diversity of the
generated tasks. A task discriminator defined on the robot behaviors in the
generated tasks is jointly trained to estimate the evidence lower bound of the
diversity objective. The learned skills can then be composed in a hierarchical
reinforcement learning algorithm to solve unseen target tasks. We demonstrate
that the proposed method can effectively learn a variety of robot skills in two
tabletop manipulation domains. Our results suggest that the learned skills can
effectively improve the robot's performance in various unseen target tasks
compared to existing reinforcement learning and skill learning methods. | [
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] |
We develop and study new adversarial perturbations that enable an attacker to
gain control over decisions in generic Artificial Intelligence (AI) systems
including deep learning neural networks. In contrast to adversarial data
modification, the attack mechanism we consider here involves alterations to the
AI system itself. Such a stealth attack could be conducted by a mischievous,
corrupt or disgruntled member of a software development team. It could also be
made by those wishing to exploit a ``democratization of AI'' agenda, where
network architectures and trained parameter sets are shared publicly. We
develop a range of new implementable attack strategies with accompanying
analysis, showing that with high probability a stealth attack can be made
transparent, in the sense that system performance is unchanged on a fixed
validation set which is unknown to the attacker, while evoking any desired
output on a trigger input of interest. The attacker only needs to have
estimates of the size of the validation set and the spread of the AI's relevant
latent space. In the case of deep learning neural networks, we show that a one
neuron attack is possible - a modification to the weights and bias associated
with a single neuron - revealing a vulnerability arising from
over-parameterization. We illustrate these concepts using state of the art
architectures on two standard image data sets. Guided by the theory and
computational results, we also propose strategies to guard against stealth
attacks. | [
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] |
We consider the $k$-clustering problem with $\ell_p$-norm cost, which
includes $k$-median, $k$-means and $k$-center, under an individual notion of
fairness proposed by Jung et al. [2020]: given a set of points $P$ of size $n$,
a set of $k$ centers induces a fair clustering if every point in $P$ has a
center among its $n/k$ closest neighbors. Mahabadi and Vakilian [2020]
presented a $(p^{O(p)},7)$-bicriteria approximation for fair clustering with
$\ell_p$-norm cost: every point finds a center within distance at most $7$
times its distance to its $(n/k)$-th closest neighbor and the $\ell_p$-norm
cost of the solution is at most $p^{O(p)}$ times the cost of an optimal fair
solution.
In this work, for any $\varepsilon>0$, we present an improved $(16^p
+\varepsilon,3)$-bicriteria for this problem. Moreover, for $p=1$ ($k$-median)
and $p=\infty$ ($k$-center), we present improved cost-approximation factors
$7.081+\varepsilon$ and $3+\varepsilon$ respectively. To achieve our
guarantees, we extend the framework of [Charikar et al., 2002, Swamy, 2016] and
devise a $16^p$-approximation algorithm for the facility location with
$\ell_p$-norm cost under matroid constraint which might be of an independent
interest.
Besides, our approach suggests a reduction from our individually fair
clustering to a clustering with a group fairness requirement proposed by
Kleindessner et al. [2019], which is essentially the median matroid problem
[Krishnaswamy et al., 2011]. | [
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] |
App builders commonly use security challenges, a form of step-up
authentication, to add security to their apps. However, the ethical
implications of this type of architecture has not been studied previously. In
this paper, we present a large-scale measurement study of running an existing
anti-fraud security challenge, Boxer, in real apps running on mobile devices.
We find that although Boxer does work well overall, it is unable to scan
effectively on devices that run its machine learning models at less than one
frame per second (FPS), blocking users who use inexpensive devices. With the
insights from our study, we design Daredevil, anew anti-fraud system for
scanning payment cards that work swell across the broad range of performance
characteristics and hardware configurations found on modern mobile devices.
Daredevil reduces the number of devices that run at less than one FPS by an
order of magnitude compared to Boxer, providing a more equitable system for
fighting fraud. In total, we collect data from 5,085,444 real devices spread
across 496 real apps running production software and interacting with real
users. | [
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Critical role of Internet of Things (IoT) in various domains like smart city,
healthcare, supply chain and transportation has made them the target of
malicious attacks. Past works in this area focused on centralized Intrusion
Detection System (IDS), assuming the existence of a central entity to perform
data analysis and identify threats. However, such IDS may not always be
feasible, mainly due to spread of data across multiple sources and gathering at
central node can be costly. Also, the earlier works primarily focused on
improving True Positive Rate (TPR) and ignored the False Positive Rate (FPR),
which is also essential to avoid unnecessary downtime of the systems. In this
paper, we first present an architecture for IDS based on hybrid ensemble model,
named PHEC, which gives improved performance compared to state-of-the-art
architectures. We then adapt this model to a federated learning framework that
performs local training and aggregates only the model parameters. Next, we
propose Noise-Tolerant PHEC in centralized and federated settings to address
the label-noise problem. The proposed idea uses classifiers using weighted
convex surrogate loss functions. Natural robustness of KNN classifier towards
noisy data is also used in the proposed architecture. Experimental results on
four benchmark datasets drawn from various security attacks show that our model
achieves high TPR while keeping FPR low on noisy and clean data. Further, they
also demonstrate that the hybrid ensemble models achieve performance in
federated settings close to that of the centralized settings. | [
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] |
AI has provided us with the ability to automate tasks, extract information
from vast amounts of data, and synthesize media that is nearly
indistinguishable from the real thing. However, positive tools can also be used
for negative purposes. In particular, cyber adversaries can use AI (such as
machine learning) to enhance their attacks and expand their campaigns.
Although offensive AI has been discussed in the past, there is a need to
analyze and understand the threat in the context of organizations. For example,
how does an AI-capable adversary impact the cyber kill chain? Does AI benefit
the attacker more than the defender? What are the most significant AI threats
facing organizations today and what will be their impact on the future?
In this survey, we explore the threat of offensive AI on organizations.
First, we present the background and discuss how AI changes the adversary's
methods, strategies, goals, and overall attack model. Then, through a
literature review, we identify 33 offensive AI capabilities which adversaries
can use to enhance their attacks. Finally, through a user study spanning
industry and academia, we rank the AI threats and provide insights on the
adversaries. | [
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] |
Remaining useful life prediction (RUL) is one of the key technologies of
condition-based maintenance, which is important to maintain the reliability and
safety of industrial equipments. Massive industrial measurement data has
effectively improved the performance of the data-driven based RUL prediction
method. While deep learning has achieved great success in RUL prediction,
existing methods have difficulties in processing long sequences and extracting
information from the sensor and time step aspects. In this paper, we propose
Dual Aspect Self-attention based on Transformer (DAST), a novel deep RUL
prediction method, which is an encoder-decoder structure purely based on
self-attention without any RNN/CNN module. DAST consists of two encoders, which
work in parallel to simultaneously extract features of different sensors and
time steps. Solely based on self-attention, the DAST encoders are more
effective in processing long data sequences, and are capable of adaptively
learning to focus on more important parts of input. Moreover, the parallel
feature extraction design avoids mutual influence of information from two
aspects. Experiments on two widely used turbofan engines datasets show that our
method significantly outperforms the state-of-the-art RUL prediction methods. | [
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] |
The ability to transfer a policy from one environment to another is a
promising avenue for efficient robot learning in realistic settings where task
supervision is not available. This can allow us to take advantage of
environments well suited for training, such as simulators or laboratories, to
learn a policy for a real robot in a home or office. To succeed, such policy
transfer must overcome both the visual domain gap (e.g. different illumination
or background) and the dynamics domain gap (e.g. different robot calibration or
modelling error) between source and target environments. However, prior policy
transfer approaches either cannot handle a large domain gap or can only address
one type of domain gap at a time. In this paper, we propose a novel policy
transfer method with iterative "environment grounding", IDAPT, that alternates
between (1) directly minimizing both visual and dynamics domain gaps by
grounding the source environment in the target environment domains, and (2)
training a policy on the grounded source environment. This iterative training
progressively aligns the domains between the two environments and adapts the
policy to the target environment. Once trained, the policy can be directly
executed on the target environment. The empirical results on locomotion and
robotic manipulation tasks demonstrate that our approach can effectively
transfer a policy across visual and dynamics domain gaps with minimal
supervision and interaction with the target environment. Videos and code are
available at https://clvrai.com/idapt . | [
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] |
Telerobotic systems must adapt to new environmental conditions and deal with
high uncertainty caused by long-time delays. As one of the best alternatives to
human-level intelligence, Reinforcement Learning (RL) may offer a solution to
cope with these issues. This paper proposes to integrate RL with the Model
Mediated Teleoperation (MMT) concept. The teleoperator interacts with a
simulated virtual environment, which provides instant feedback. Whereas
feedback from the real environment is delayed, feedback from the model is
instantaneous, leading to high transparency. The MMT is realized in combination
with an intelligent system with two layers. The first layer utilizes Dynamic
Movement Primitives (DMP) which accounts for certain changes in the avatar
environment. And, the second layer addresses the problems caused by uncertainty
in the model using RL methods. Augmented reality was also provided to fuse the
avatar device and virtual environment models for the teleoperator. Implemented
on DLR's Exodex Adam hand-arm haptic exoskeleton, the results show RL methods
are able to find different solutions when changes are applied to the object
position after the demonstration. The results also show DMPs to be effective at
adapting to new conditions where there is no uncertainty involved. | [
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] |
Machine learning techniques are becoming a fundamental tool for scientific
and engineering progress. These techniques are applied in contexts as diverse
as astronomy and spam filtering. However, correctly applying these techniques
requires careful engineering. Much attention has been paid to the technical
potential; relatively little attention has been paid to the software
engineering process required to bring research-based machine learning
techniques into practical utility. Technology companies have supported the
engineering community through machine learning frameworks such as TensorFLow
and PyTorch, but the details of how to engineer complex machine learning models
in these frameworks have remained hidden.
To promote best practices within the engineering community, academic
institutions and Google have partnered to launch a Special Interest Group on
Machine Learning Models (SIGMODELS) whose goal is to develop exemplary
implementations of prominent machine learning models in community locations
such as the TensorFlow Model Garden (TFMG). The purpose of this report is to
define a process for reproducing a state-of-the-art machine learning model at a
level of quality suitable for inclusion in the TFMG. We define the engineering
process and elaborate on each step, from paper analysis to model release. We
report on our experiences implementing the YOLO model family with a team of 26
student researchers, share the tools we developed, and describe the lessons we
learned along the way. | [
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] |
This paper investigates the problem of providing ultra-reliable and
energy-efficient virtual reality (VR) experiences for wireless mobile users. To
ensure reliable ultra-high-definition (UHD) video frame delivery to mobile
users and enhance their immersive visual experiences, a coordinated multipoint
(CoMP) transmission technique and millimeter wave (mmWave) communications are
exploited. Owing to user movement and time-varying wireless channels, the
wireless VR experience enhancement problem is formulated as a
sequence-dependent and mixed-integer problem with a goal of maximizing users'
feeling of presence (FoP) in the virtual world, subject to power consumption
constraints on access points (APs) and users' head-mounted displays (HMDs). The
problem, however, is hard to be directly solved due to the lack of users'
accurate tracking information and the sequence-dependent and mixed-integer
characteristics. To overcome this challenge, we develop a parallel echo state
network (ESN) learning method to predict users' tracking information by
training fresh and historical tracking samples separately collected by APs.
With the learnt results, we propose a deep reinforcement learning (DRL) based
optimization algorithm to solve the formulated problem. In this algorithm, we
implement deep neural networks (DNNs) as a scalable solution to produce integer
decision variables and solving a continuous power control problem to criticize
the integer decision variables. Finally, the performance of the proposed
algorithm is compared with various benchmark algorithms, and the impact of
different design parameters is also discussed. Simulation results demonstrate
that the proposed algorithm is more 4.14% energy-efficient than the benchmark
algorithms. | [
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] |
In spite of the great success of deep learning technologies, training and
delivery of a practically serviceable model is still a highly time-consuming
process. Furthermore, a resulting model is usually too generic and heavyweight,
and hence essentially goes through another expensive model compression phase to
fit in a resource-limited device like embedded systems. Inspired by the fact
that a machine learning task specifically requested by mobile users is often
much simpler than it is supported by a massive generic model, this paper
proposes a framework, called Pool of Experts (PoE), that instantly builds a
lightweight and task-specific model without any training process. For a
realtime model querying service, PoE first extracts a pool of primitive
components, called experts, from a well-trained and sufficiently generic
network by exploiting a novel conditional knowledge distillation method, and
then performs our train-free knowledge consolidation to quickly combine
necessary experts into a lightweight network for a target task. Thanks to this
train-free property, in our thorough empirical study, PoE can build a fairly
accurate yet compact model in a realtime manner, whereas it takes a few minutes
per query for the other training methods to achieve a similar level of the
accuracy. | [
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] |
Android malware is one of the most dangerous threats on the internet, and
it's been on the rise for several years. Despite significant efforts in
detecting and classifying android malware from innocuous android applications,
there is still a long way to go. As a result, there is a need to provide a
basic understanding of the behavior displayed by the most common Android
malware categories and families. Each Android malware family and category has a
distinct objective. As a result, it has impacted every corporate area,
including healthcare, banking, transportation, government, and e-commerce. In
this paper, we presented two machine-learning approaches for Dynamic Analysis
of Android Malware: one for detecting and identifying Android Malware
Categories and the other for detecting and identifying Android Malware
Families, which was accomplished by analyzing a massive malware dataset with 14
prominent malware categories and 180 prominent malware families of
CCCS-CIC-AndMal2020 dataset on Dynamic Layers. Our approach achieves in Android
Malware Category detection more than 96 % accurate and achieves in Android
Malware Family detection more than 99% accurate. Our approach provides a method
for high-accuracy Dynamic Analysis of Android Malware while also shortening the
time required to analyze smartphone malware. | [
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Software engineering of network-centric Artificial Intelligence (AI) and
Internet of Things (IoT) enabled Cyber-Physical Systems (CPS) and services,
involves complex design and validation challenges. In this paper, we propose a
novel approach, based on the model-driven software engineering paradigm, in
particular the domain-specific modeling methodology. We focus on a
sub-discipline of AI, namely Machine Learning (ML) and propose the delegation
of data analytics and ML to the IoT edge. This way, we may increase the service
quality of ML, for example, its availability and performance, regardless of the
network conditions, as well as maintaining the privacy, security and
sustainability. We let practitioners assign ML tasks to heterogeneous edge
devices, including highly resource-constrained embedded microcontrollers with
main memories in the order of Kilobytes, and energy consumption in the order of
milliwatts. This is known as TinyML. Furthermore, we show how software models
with different levels of abstraction, namely platform-independent and
platform-specific models can be used in the software development process.
Finally, we validate the proposed approach using a case study addressing the
predictive maintenance of a hydraulics system with various networked sensors
and actuators. | [
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] |
In this paper, we present ML-Quadrat, an open-source research prototype that
is based on the Eclipse Modeling Framework (EMF) and the state of the art in
the literature of Model-Driven Software Engineering (MDSE) for smart
Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned
users are mostly software developers who might not have deep knowledge and
skills in the heterogeneous IoT platforms and the diverse Artificial
Intelligence (AI) technologies, specifically regarding Machine Learning (ML).
ML-Quadrat is released under the terms of the Apache 2.0 license on Github.
Additionally, we demonstrate an early tool prototype of DriotData, a web-based
Low-Code platform targeting citizen data scientists and citizen/end-user
software developers. DriotData exploits and adopts ML-Quadrat in the industry
by offering an extended version of it as a subscription-based service to
companies, mainly Small- and Medium-Sized Enterprises (SME). The current
preliminary version of DriotData has three web-based model editors: text-based,
tree-/form-based and diagram-based. The latter is designed for domain experts
in the problem or use case domains (namely the IoT vertical domains) who might
not have knowledge and skills in the field of IT. Finally, a short video
demonstrating the tools is available on YouTube: https://youtu.be/VAuz25w0a5k | [
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] |
The large-scale data stream problem refers to high-speed information flow
which cannot be processed in scalable manner under a traditional computing
platform. This problem also imposes expensive labelling cost making the
deployment of fully supervised algorithms unfeasible. On the other hand, the
problem of semi-supervised large-scale data streams is little explored in the
literature because most works are designed in the traditional single-node
computing environments while also being fully supervised approaches. This paper
offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to
cope with the scarcity of labelled samples and the large-scale data streams
simultaneously. WeScatterNet is crafted under distributed computing platform of
Apache Spark with a data-free model fusion strategy for model compression after
parallel computing stage. It features an open network structure to address the
global and local drift problems while integrating a data augmentation,
annotation and auto-correction ($DA^3$) method for handling partially labelled
data streams. The performance of WeScatterNet is numerically evaluated in the
six large-scale data stream problems with only $25\%$ label proportions. It
shows highly competitive performance even if compared with fully supervised
learners with $100\%$ label proportions. | [
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The Ladder of Causation describes three qualitatively different types of
activities an agent may be interested in engaging in, namely, seeing
(observational), doing (interventional), and imagining (counterfactual) (Pearl
and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy
is that data is collected by an agent observing or intervening in a system
(layers 1 and 2), while its goal may be to understand what would have happened
had it taken a different course of action, contrary to what factually ended up
happening (layer 3). While there exists a solid understanding of the conditions
under which cross-layer inferences are allowed from observations to
interventions, the results are somewhat scarcer when targeting counterfactual
quantities. In this paper, we study the identification of nested
counterfactuals from an arbitrary combination of observations and experiments.
Specifically, building on a more explicit definition of nested counterfactuals,
we prove the counterfactual unnesting theorem (CUT), which allows one to map
arbitrary nested counterfactuals to unnested ones. For instance, applications
in mediation and fairness analysis usually evoke notions of direct, indirect,
and spurious effects, which naturally require nesting. Second, we introduce a
sufficient and necessary graphical condition for counterfactual identification
from an arbitrary combination of observational and experimental distributions.
Lastly, we develop an efficient and complete algorithm for identifying nested
counterfactuals; failure of the algorithm returning an expression for a query
implies it is not identifiable. | [
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] |
Deep learning models can enable accurate and efficient disease diagnosis, but
have thus far been hampered by the data scarcity present in the medical world.
Automated diagnosis studies have been constrained by underpowered single-center
datasets, and although some results have shown promise, their generalizability
to other institutions remains questionable as the data heterogeneity between
institutions is not taken into account. By allowing models to be trained in a
distributed manner that preserves patients' privacy, federated learning
promises to alleviate these issues, by enabling diligent multi-center studies.
We present the first federated learning study on the modality of cardiovascular
magnetic resonance (CMR) and use four centers derived from subsets of the M\&M
and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy
(HCM). We adapt a 3D-CNN network pretrained on action recognition and explore
two different ways of incorporating shape prior information to the model, and
four different data augmentation set-ups, systematically analyzing their impact
on the different collaborative learning choices. We show that despite the small
size of data (180 subjects derived from four centers), the privacy preserving
federated learning achieves promising results that are competitive with
traditional centralized learning. We further find that federatively trained
models exhibit increased robustness and are more sensitive to domain shift
effects. | [
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The needs for precisely estimating a student's academic performance have been
emphasized with an increasing amount of attention paid to Intelligent Tutoring
System (ITS). However, since labels for academic performance, such as test
scores, are collected from outside of ITS, obtaining the labels is costly,
leading to label-scarcity problem which brings challenge in taking machine
learning approaches for academic performance prediction. To this end, inspired
by the recent advancement of pre-training method in natural language processing
community, we propose DPA, a transfer learning framework with Discriminative
Pre-training tasks for Academic performance prediction. DPA pre-trains two
models, a generator and a discriminator, and fine-tunes the discriminator on
academic performance prediction. In DPA's pre-training phase, a sequence of
interactions where some tokens are masked is provided to the generator which is
trained to reconstruct the original sequence. Then, the discriminator takes an
interaction sequence where the masked tokens are replaced by the generator's
outputs, and is trained to predict the originalities of all tokens in the
sequence. Compared to the previous state-of-the-art generative pre-training
method, DPA is more sample efficient, leading to fast convergence to lower
academic performance prediction error. We conduct extensive experimental
studies on a real-world dataset obtained from a multi-platform ITS application
and show that DPA outperforms the previous state-of-the-art generative
pre-training method with a reduction of 4.05% in mean absolute error and more
robust to increased label-scarcity. | [
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Recent advances in robot learning have enabled robots to become increasingly
better at mastering a predefined set of tasks. On the other hand, as humans, we
have the ability to learn a growing set of tasks over our lifetime. Continual
robot learning is an emerging research direction with the goal of endowing
robots with this ability. In order to learn new tasks over time, the robot
first needs to infer the task at hand. Task inference, however, has received
little attention in the multi-task learning literature. In this paper, we
propose a novel approach to continual learning of robotic control tasks. Our
approach performs unsupervised learning of behavior embeddings by incrementally
self-organizing demonstrated behaviors. Task inference is made by finding the
nearest behavior embedding to a demonstrated behavior, which is used together
with the environment state as input to a multi-task policy trained with
reinforcement learning to optimize performance over tasks. Unlike previous
approaches, our approach makes no assumptions about task distribution and
requires no task exploration to infer tasks. We evaluate our approach in
experiments with concurrently and sequentially presented tasks and show that it
outperforms other multi-task learning approaches in terms of generalization
performance and convergence speed, particularly in the continual learning
setting. | [
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] |
Causal discovery from observational data is an important tool in many
branches of science. Under certain assumptions it allows scientists to explain
phenomena, predict, and make decisions. In the large sample limit, sound and
complete causal discovery algorithms have been previously introduced, where a
directed acyclic graph (DAG), or its equivalence class, representing causal
relations is searched. However, in real-world cases, only finite training data
is available, which limits the power of statistical tests used by these
algorithms, leading to errors in the inferred causal model. This is commonly
addressed by devising a strategy for using as few as possible statistical
tests. In this paper, we introduce such a strategy in the form of a recursive
wrapper for existing constraint-based causal discovery algorithms, which
preserves soundness and completeness. It recursively clusters the observed
variables using the normalized min-cut criterion from the outset, and uses a
baseline causal discovery algorithm during backtracking for learning local
sub-graphs. It then combines them and ensures completeness. By an ablation
study, using synthetic data, and by common real-world benchmarks, we
demonstrate that our approach requires significantly fewer statistical tests,
learns more accurate graphs, and requires shorter run-times than the baseline
algorithm. | [
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] |
Modern keyboards allow a musician to play multiple instruments at the same
time by assigning zones -- fixed pitch ranges of the keyboard -- to different
instruments. In this paper, we aim to further extend this idea and examine the
feasibility of automatic instrumentation -- dynamically assigning instruments
to notes in solo music during performance. In addition to the online,
real-time-capable setting for performative use cases, automatic instrumentation
can also find applications in assistive composing tools in an offline setting.
Due to the lack of paired data of original solo music and their full
arrangements, we approach automatic instrumentation by learning to separate
parts (e.g., voices, instruments and tracks) from their mixture in symbolic
multitrack music, assuming that the mixture is to be played on a keyboard. We
frame the task of part separation as a sequential multi-class classification
problem and adopt machine learning to map sequences of notes into sequences of
part labels. To examine the effectiveness of our proposed models, we conduct a
comprehensive empirical evaluation over four diverse datasets of different
genres and ensembles -- Bach chorales, string quartets, game music and pop
music. Our experiments show that the proposed models outperform various
baselines. We also demonstrate the potential for our proposed models to produce
alternative convincing instrumentations for an existing arrangement by
separating its mixture into parts. All source code and audio samples can be
found at https://salu133445.github.io/arranger/ . | [
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] |
Motivation: Predicting Drug-Target Interaction (DTI) is a well-studied topic
in bioinformatics due to its relevance in the fields of proteomics and
pharmaceutical research. Although many machine learning methods have been
successfully applied in this task, few of them aim at leveraging the inherent
heterogeneous graph structure in the DTI network to address the challenge. For
better learning and interpreting the DTI topological structure and the
similarity, it is desirable to have methods specifically for predicting
interactions from the graph structure.
Results: We present an end-to-end framework, DTI-GAT (Drug-Target Interaction
prediction with Graph Attention networks) for DTI predictions. DTI-GAT
incorporates a deep neural network architecture that operates on
graph-structured data with the attention mechanism, which leverages both the
interaction patterns and the features of drug and protein sequences. DTI-GAT
facilitates the interpretation of the DTI topological structure by assigning
different attention weights to each node with the self-attention mechanism.
Experimental evaluations show that DTI-GAT outperforms various state-of-the-art
systems on the binary DTI prediction problem. Moreover, the independent study
results further demonstrate that our model can be generalized better than other
conventional methods.
Availability: The source code and all datasets are available at
https://github.com/Haiyang-W/DTI-GRAPH | [
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] |
Recurrent neural networks for language models like long short-term memory
(LSTM) have been utilized as a tool for modeling and predicting long term
dynamics of complex stochastic molecular systems. Recently successful examples
on learning slow dynamics by LSTM are given with simulation data of low
dimensional reaction coordinate. However, in this report we show that the
following three key factors significantly affect the performance of language
model learning, namely dimensionality of reaction coordinates, temporal
resolution and state partition. When applying recurrent neural networks to
molecular dynamics simulation trajectories of high dimensionality, we find that
rare events corresponding to the slow dynamics might be obscured by other
faster dynamics of the system, and cannot be efficiently learned. Under such
conditions, we find that coarse graining the conformational space into
metastable states and removing recrossing events when estimating transition
probabilities between states could greatly help improve the accuracy of slow
dynamics learning in molecular dynamics. Moreover, we also explore other models
like Transformer, which do not show superior performance than LSTM in
overcoming these issues. Therefore, to learn rare events of slow molecular
dynamics by LSTM and Transformer, it is critical to choose proper temporal
resolution (i.e., saving intervals of MD simulation trajectories) and state
partition in high resolution data, since deep neural network models might not
automatically disentangle slow dynamics from fast dynamics when both are
present in data influencing each other. | [
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] |
Assessing the diversity of a dataset of information associated with people is
crucial before using such data for downstream applications. For a given
dataset, this often involves computing the imbalance or disparity in the
empirical marginal distribution of a protected attribute (e.g. gender, dialect,
etc.). However, real-world datasets, such as images from Google Search or
collections of Twitter posts, often do not have protected attributes labeled.
Consequently, to derive disparity measures for such datasets, the elements need
to hand-labeled or crowd-annotated, which are expensive processes.
We propose a cost-effective approach to approximate the disparity of a given
unlabeled dataset, with respect to a protected attribute, using a control set
of labeled representative examples. Our proposed algorithm uses the pairwise
similarity between elements in the dataset and elements in the control set to
effectively bootstrap an approximation to the disparity of the dataset.
Importantly, we show that using a control set whose size is much smaller than
the size of the dataset is sufficient to achieve a small approximation error.
Further, based on our theoretical framework, we also provide an algorithm to
construct adaptive control sets that achieve smaller approximation errors than
randomly chosen control sets. Simulations on two image datasets and one Twitter
dataset demonstrate the efficacy of our approach (using random and adaptive
control sets) in auditing the diversity of a wide variety of datasets. | [
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] |
When controllers (brains) and morphologies (bodies) of robots simultaneously
evolve, this can lead to a problem, namely the brain & body mismatch problem.
In this research, we propose a solution of lifetime learning. We set up a
system where modular robots can create offspring that inherit the bodies of
parents by recombination and mutation. With regards to the brains of the
offspring, we use two methods to create them. The first one entails solely
evolution which means the brain of a robot child is inherited from its parents.
The second approach is evolution plus learning which means the brain of a child
is inherited as well, but additionally is developed by a learning algorithm -
RevDEknn. We compare these two methods by running experiments in a simulator
called Revolve and use efficiency, efficacy, and the morphology intelligence of
the robots for the comparison. The experiments show that the evolution plus
learning method does not only lead to a higher fitness level, but also to more
morphologically evolving robots. This constitutes a quantitative demonstration
that changes in the brain can induce changes in the body, leading to the
concept of morphological intelligence, which is quantified by the learning
delta, meaning the ability of a morphology to facilitate learning. | [
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] |
Data-driven research on the automated discovery and repair of security
vulnerabilities in source code requires comprehensive datasets of real-life
vulnerable code and their fixes. To assist in such research, we propose a
method to automatically collect and curate a comprehensive vulnerability
dataset from Common Vulnerabilities and Exposures (CVE) records in the public
National Vulnerability Database (NVD). We implement our approach in a fully
automated dataset collection tool and share an initial release of the resulting
vulnerability dataset named CVEfixes.
The CVEfixes collection tool automatically fetches all available CVE records
from the NVD, gathers the vulnerable code and corresponding fixes from
associated open-source repositories, and organizes the collected information in
a relational database. Moreover, the dataset is enriched with meta-data such as
programming language, and detailed code and security metrics at five levels of
abstraction. The collection can easily be repeated to keep up-to-date with
newly discovered or patched vulnerabilities. The initial release of CVEfixes
spans all published CVEs up to 9 June 2021, covering 5365 CVE records for 1754
open-source projects that were addressed in a total of 5495 vulnerability
fixing commits.
CVEfixes supports various types of data-driven software security research,
such as vulnerability prediction, vulnerability classification, vulnerability
severity prediction, analysis of vulnerability-related code changes, and
automated vulnerability repair. | [
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] |
Robot manipulation for untangling 1D deformable structures such as ropes,
cables, and wires is challenging due to their infinite dimensional
configuration space, complex dynamics, and tendency to self-occlude. Analytical
controllers often fail in the presence of dense configurations, due to the
difficulty of grasping between adjacent cable segments. We present two
algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which
operate alongside HULK, a high-level planner from prior work. LOKI uses a
learned model of manipulation features to refine a coarse grasp keypoint
prediction to a precise, optimized location and orientation, while SPiDERMan
uses a learned model to sense task progress and apply recovery actions. We
evaluate these algorithms in physical cable untangling experiments with 336
knots and over 1500 actions on real cables using the da Vinci surgical robot.
We find that the combination of HULK, LOKI, and SPiDERMan is able to untangle
dense overhand, figure-eight, double-overhand, square, bowline, granny,
stevedore, and triple-overhand knots. The composition of these methods
successfully untangles a cable from a dense initial configuration in 68.3% of
60 physical experiments and achieves 50% higher success rates than baselines
from prior work. Supplementary material, code, and videos can be found at
https://tinyurl.com/rssuntangling. | [
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] |
Vessel tracing by modeling vascular structures in 3D medical images with
centerlines and radii can provide useful information for vascular health.
Existing algorithms have been developed but there are certain persistent
problems such as incomplete or inaccurate vessel tracing, especially in
complicated vascular beds like the intracranial arteries. We propose here a
deep learning based open curve active contour model (DOST) to trace vessels in
3D images. Initial curves were proposed from a centerline segmentation neural
network. Then data-driven machine knowledge was used to predict the stretching
direction and vessel radius of the initial curve, while the active contour
model (as human knowledge) maintained smoothness and intensity fitness of
curves. Finally, considering the nonloop topology of most vasculatures,
individually traced vessels were connected into a tree topology by applying a
minimum spanning tree algorithm on a global connection graph. We evaluated DOST
on a Time-of-Flight (TOF) MRA intracranial artery dataset and demonstrated its
superior performance over existing segmentation-based and tracking-based vessel
tracing methods. In addition, DOST showed strong adaptability on different
imaging modalities (CTA, MR T1 SPACE) and vascular beds (coronary arteries). | [
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] |
Machine learning methods, such as deep learning, show promising results in
the medical domain. However, the lack of interpretability of these algorithms
may hinder their applicability to medical decision support systems. This paper
studies an interpretable deep learning technique, called SincNet. SincNet is a
convolutional neural network that efficiently learns customized band-pass
filters through trainable sinc-functions. In this study, we use SincNet to
analyze the neural activity of individuals with Autism Spectrum Disorder (ASD),
who experience characteristic differences in neural oscillatory activity. In
particular, we propose a novel SincNet-based neural network for detecting
emotions in ASD patients using EEG signals. The learned filters can be easily
inspected to detect which part of the EEG spectrum is used for predicting
emotions. We found that our system automatically learns the high-$\alpha$ (9-13
Hz) and $\beta$ (13-30 Hz) band suppression often present in individuals with
ASD. This result is consistent with recent neuroscience studies on emotion
recognition, which found an association between these band suppressions and the
behavioral deficits observed in individuals with ASD. The improved
interpretability of SincNet is achieved without sacrificing performance in
emotion recognition. | [
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] |
This paper presents HURRA, a system that aims to reduce the time spent by
human operators in the process of network troubleshooting. To do so, it
comprises two modules that are plugged after any anomaly detection algorithm:
(i) a first attention mechanism, that ranks the present features in terms of
their relation with the anomaly and (ii) a second module able to incorporates
previous expert knowledge seamlessly, without any need of human interaction nor
decisions. We show the efficacy of these simple processes on a collection of
real router datasets obtained from tens of ISPs which exhibit a rich variety of
anomalies and very heterogeneous set of KPIs, on which we gather manually
annotated ground truth by the operator solving the troubleshooting ticket. Our
experimental evaluation shows that (i) the proposed system is effective in
achieving high levels of agreement with the expert, that (ii) even a simple
statistical approach is able to extracting useful information from expert
knowledge gained in past cases to further improve performance and finally that
(iii) the main difficulty in live deployment concerns the automated selection
of the anomaly detection algorithm and the tuning of its hyper-parameters. | [
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] |
Content delivery networks (CDNs) provide efficient content distribution over
the Internet. CDNs improve the connectivity and efficiency of global
communications, but their caching mechanisms may be breached by
cyber-attackers. Among the security mechanisms, effective anomaly detection
forms an important part of CDN security enhancement. In this work, we propose a
multi-perspective unsupervised learning framework for anomaly detection in
CDNs. In the proposed framework, a multi-perspective feature engineering
approach, an optimized unsupervised anomaly detection model that utilizes an
isolation forest and a Gaussian mixture model, and a multi-perspective
validation method, are developed to detect abnormal behaviors in CDNs mainly
from the client Internet Protocol (IP) and node perspectives, therefore to
identify the denial of service (DoS) and cache pollution attack (CPA) patterns.
Experimental results are presented based on the analytics of eight days of
real-world CDN log data provided by a major CDN operator. Through experiments,
the abnormal contents, compromised nodes, malicious IPs, as well as their
corresponding attack types, are identified effectively by the proposed
framework and validated by multiple cybersecurity experts. This shows the
effectiveness of the proposed method when applied to real-world CDN data. | [
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] |
One of the main challenges in autonomous robotic exploration and navigation
in unknown and unstructured environments is determining where the robot can or
cannot safely move. A significant source of difficulty in this determination
arises from stochasticity and uncertainty, coming from localization error,
sensor sparsity and noise, difficult-to-model robot-ground interactions, and
disturbances to the motion of the vehicle. Classical approaches to this problem
rely on geometric analysis of the surrounding terrain, which can be prone to
modeling errors and can be computationally expensive. Moreover, modeling the
distribution of uncertain traversability costs is a difficult task, compounded
by the various error sources mentioned above. In this work, we take a
principled learning approach to this problem. We introduce a neural network
architecture for robustly learning the distribution of traversability costs.
Because we are motivated by preserving the life of the robot, we tackle this
learning problem from the perspective of learning tail-risks, i.e. the
Conditional Value-at-Risk (CVaR). We show that this approach reliably learns
the expected tail risk given a desired probability risk threshold between 0 and
1, producing a traversability costmap which is more robust to outliers, more
accurately captures tail risks, and is more computationally efficient, when
compared against baselines. We validate our method on data collected a legged
robot navigating challenging, unstructured environments including an abandoned
subway, limestone caves, and lava tube caves. | [
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] |
Nowadays, multi-sensor technologies are applied in many fields, e.g., Health
Care (HC), Human Activity Recognition (HAR), and Industrial Control System
(ICS). These sensors can generate a substantial amount of multivariate
time-series data. Unsupervised anomaly detection on multi-sensor time-series
data has been proven critical in machine learning researches. The key challenge
is to discover generalized normal patterns by capturing spatial-temporal
correlation in multi-sensor data. Beyond this challenge, the noisy data is
often intertwined with the training data, which is likely to mislead the model
by making it hard to distinguish between the normal, abnormal, and noisy data.
Few of previous researches can jointly address these two challenges. In this
paper, we propose a novel deep learning-based anomaly detection algorithm
called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a
Deep Convolutional Autoencoder to characterize spatial dependence of
multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish
between the noisy, normal, and abnormal data. Then, we construct a Memory
Network consisting of linear (Autoregressive Model) and non-linear predictions
(Bidirectional LSTM with Attention) to capture temporal dependence from
time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We
empirically compare the proposed approach with several state-of-the-art anomaly
detection methods on HAR and HC datasets. Experimental results demonstrate that
our proposed model outperforms these existing methods. | [
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] |
The volume of flight traffic gets increasing over the time, which makes the
strategic traffic flow management become one of the challenging problems since
it requires a lot of computational resources to model entire traffic data. On
the other hand, Automatic Dependent Surveillance - Broadcast (ADS-B) technology
has been considered as a promising data technology to provide both flight crews
and ground control staff the necessary information safely and efficiently about
the position and velocity of the airplanes in a specific area. In the attempt
to tackle this problem, we presented in this paper a simplified framework that
can support to detect the typical air routes between airports based on ADS-B
data. Specifically, the flight traffic will be classified into major groups
based on similarity measures, which helps to reduce the number of flight paths
between airports. As a matter of fact, our framework can be taken into account
to reduce practically the computational cost for air flow optimization and
evaluate the operational performance. Finally, in order to illustrate the
potential applications of our proposed framework, an experiment was performed
using ADS-B traffic flight data of three different pairs of airports. The
detected typical routes between each couple of airports show promising results
by virtue of combining two indices for measuring the clustering performance and
incorporating human judgment into the visual inspection. | [
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] |
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match
customers to personalized lists of products. Approaches to top-k recommendation
mainly rely on Learning-To-Rank algorithms and, among them, the most widely
adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise
optimization approach. Recently, BPR has been found vulnerable against
adversarial perturbations of its model parameters. Adversarial Personalized
Ranking (APR) mitigates this issue by robustifying BPR via an adversarial
training procedure. The empirical improvements of APR's accuracy performance on
BPR have led to its wide use in several recommender models. However, a key
overlooked aspect has been the beyond-accuracy performance of APR, i.e.,
novelty, coverage, and amplification of popularity bias, considering that
recent results suggest that BPR, the building block of APR, is sensitive to the
intensification of biases and reduction of recommendation novelty. In this
work, we model the learning characteristics of the BPR and APR optimization
frameworks to give mathematical evidence that, when the feedback data have a
tailed distribution, APR amplifies the popularity bias more than BPR due to an
unbalanced number of received positive updates from short-head items. Using
matrix factorization (MF), we empirically validate the theoretical results by
performing preliminary experiments on two public datasets to compare BPR-MF and
APR-MF performance on accuracy and beyond-accuracy metrics. The experimental
results consistently show the degradation of novelty and coverage measures and
a worrying amplification of bias. | [
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Respiratory rate (RR) is a clinical metric used to assess overall health and
physical fitness. An individual's RR can change from their baseline due to
chronic illness symptoms (e.g., asthma, congestive heart failure), acute
illness (e.g., breathlessness due to infection), and over the course of the day
due to physical exhaustion during heightened exertion. Remote estimation of RR
can offer a cost-effective method to track disease progression and
cardio-respiratory fitness over time. This work investigates a model-driven
approach to estimate RR from short audio segments obtained after physical
exertion in healthy adults. Data was collected from 21 individuals using
microphone-enabled, near-field headphones before, during, and after strenuous
exercise. RR was manually annotated by counting perceived inhalations and
exhalations. A multi-task Long-Short Term Memory (LSTM) network with
convolutional layers was implemented to process mel-filterbank energies,
estimate RR in varying background noise conditions, and predict heavy
breathing, indicated by an RR of more than 25 breaths per minute. The
multi-task model performs both classification and regression tasks and
leverages a mixture of loss functions. It was observed that RR can be estimated
with a concordance correlation coefficient (CCC) of 0.76 and a mean squared
error (MSE) of 0.2, demonstrating that audio can be a viable signal for
approximating RR. | [
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] |
This paper presents a new approach to prevent transportation accidents and
monitor driver's behavior using a healthcare AI system that incorporates
fairness and ethics. Dangerous medical cases and unusual behavior of the driver
are detected. Fairness algorithm is approached in order to improve
decision-making and address ethical issues such as privacy issues, and to
consider challenges that appear in the wild within AI in healthcare and
driving. A healthcare professional will be alerted about any unusual activity,
and the driver's location when necessary, is provided in order to enable the
healthcare professional to immediately help to the unstable driver. Therefore,
using the healthcare AI system allows for accidents to be predicted and thus
prevented and lives may be saved based on the built-in AI system inside the
vehicle which interacts with the ER system. | [
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] |
Machine learning (ML) prediction APIs are increasingly widely used. An ML API
can change over time due to model updates or retraining. This presents a key
challenge in the usage of the API because it is often not clear to the user if
and how the ML model has changed. Model shifts can affect downstream
application performance and also create oversight issues (e.g. if consistency
is desired). In this paper, we initiate a systematic investigation of ML API
shifts. We first quantify the performance shifts from 2020 to 2021 of popular
ML APIs from Google, Microsoft, Amazon, and others on a variety of datasets. We
identified significant model shifts in 12 out of 36 cases we investigated.
Interestingly, we found several datasets where the API's predictions became
significantly worse over time. This motivated us to formulate the API shift
assessment problem at a more fine-grained level as estimating how the API
model's confusion matrix changes over time when the data distribution is
constant. Monitoring confusion matrix shifts using standard random sampling can
require a large number of samples, which is expensive as each API call costs a
fee. We propose a principled adaptive sampling algorithm, MASA, to efficiently
estimate confusion matrix shifts. MASA can accurately estimate the confusion
matrix shifts in commercial ML APIs using up to 90% fewer samples compared to
random sampling. This work establishes ML API shifts as an important problem to
study and provides a cost-effective approach to monitor such shifts. | [
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] |
The last few years have seen an increasing wave of attacks with serious
economic and privacy damages, which evinces the need for accurate Network
Intrusion Detection Systems (NIDS). Recent works propose the use of Machine
Learning (ML) techniques for building such systems (e.g., decision trees,
neural networks). However, existing ML-based NIDS are barely robust to common
adversarial attacks, which limits their applicability to real networks. A
fundamental problem of these solutions is that they treat and classify flows
independently. In contrast, in this paper we argue the importance of focusing
on the structural patterns of attacks, by capturing not only the individual
flow features, but also the relations between different flows (e.g., the
source/destination hosts they share). To this end, we use a graph
representation that keeps flow records and their relationships, and propose a
novel Graph Neural Network (GNN) model tailored to process and learn from such
graph-structured information. In our evaluation, we first show that the
proposed GNN model achieves state-of-the-art results in the well-known
CIC-IDS2017 dataset. Moreover, we assess the robustness of our solution under
two common adversarial attacks, that intentionally modify the packet size and
inter-arrival times to avoid detection. The results show that our model is able
to maintain the same level of accuracy as in previous experiments, while
state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score)
under these attacks. This unprecedented level of robustness is mainly induced
by the capability of our GNN model to learn flow patterns of attacks structured
as graphs. | [
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] |
Recent work in recommender systems has emphasized the importance of fairness,
with a particular interest in bias and transparency, in addition to predictive
accuracy. In this paper, we focus on the state of the art pairwise ranking
model, Bayesian Personalized Ranking (BPR), which has previously been found to
outperform pointwise models in predictive accuracy, while also being able to
handle implicit feedback. Specifically, we address two limitations of BPR: (1)
BPR is a black box model that does not explain its outputs, thus limiting the
user's trust in the recommendations, and the analyst's ability to scrutinize a
model's outputs; and (2) BPR is vulnerable to exposure bias due to the data
being Missing Not At Random (MNAR). This exposure bias usually translates into
an unfairness against the least popular items because they risk being
under-exposed by the recommender system. In this work, we first propose a novel
explainable loss function and a corresponding Matrix Factorization-based model
called Explainable Bayesian Personalized Ranking (EBPR) that generates
recommendations along with item-based explanations. Then, we theoretically
quantify additional exposure bias resulting from the explainability, and use it
as a basis to propose an unbiased estimator for the ideal EBPR loss. The result
is a ranking model that aptly captures both debiased and explainable user
preferences. Finally, we perform an empirical study on three real-world
datasets that demonstrate the advantages of our proposed models. | [
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] |
Neural network pruning is useful for discovering efficient, high-performing
subnetworks within pre-trained, dense network architectures. More often than
not, it involves a three-step process -- pre-training, pruning, and re-training
-- that is computationally expensive, as the dense model must be fully
pre-trained. While previous work has revealed through experiments the
relationship between the amount of pre-training and the performance of the
pruned network, a theoretical characterization of such dependency is still
missing. Aiming to mathematically analyze the amount of dense network
pre-training needed for a pruned network to perform well, we discover a simple
theoretical bound in the number of gradient descent pre-training iterations on
a two-layer, fully-connected network, beyond which pruning via greedy forward
selection [61] yields a subnetwork that achieves good training error.
Interestingly, this threshold is shown to be logarithmically dependent upon the
size of the dataset, meaning that experiments with larger datasets require more
pre-training for subnetworks obtained via pruning to perform well. Lastly, we
empirically validate our theoretical results on a multi-layer perceptron
trained on MNIST. | [
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] |
Deep learning-based methods are growing prominence for planning purposes. In
this paper, we present a hybrid planner that combines a graph machine learning
model and an optimal solver based on branch and bound tree search for
path-planning tasks. More specifically, a graph neural network is used to
assist the branch and bound algorithm in handling constraints associated with a
desired solution path. There are multiple downstream practical applications,
such as Autonomous Unmanned Ground Vehicles (AUGV), typically deployed in
disaster relief or search and rescue operations. In off-road environments,
AUGVs must dynamically optimize a source-destination path under various
operational constraints, out of which several are difficult to predict in
advance and need to be addressed online. We conduct experiments on realistic
scenarios and show that graph neural network support enables substantial
speedup and smoother scaling to harder path-planning problems. Additionally,
information provided by the graph neural network enables the approach to
outperform problem-specific handcrafted heuristics, highlighting the potential
graph neural networks hold for path-planning tasks. | [
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] |
Learning-based methods are increasingly popular for search algorithms in
single-criterion optimization problems. In contrast, for multiple-criteria
optimization there are significantly fewer approaches despite the existence of
numerous applications. Constrained path-planning for Autonomous Ground Vehicles
(AGV) is one such application, where an AGV is typically deployed in disaster
relief or search and rescue applications in off-road environments. The agent
can be faced with the following dilemma : optimize a source-destination path
according to a known criterion and an uncertain criterion under operational
constraints. The known criterion is associated to the cost of the path,
representing the distance. The uncertain criterion represents the feasibility
of driving through the path without requiring human intervention. It depends on
various external parameters such as the physics of the vehicle, the state of
the explored terrains or weather conditions. In this work, we leverage
knowledge acquired through offline simulations by training a neural network
model to predict the uncertain criterion. We integrate this model inside a
path-planner which can solve problems online. Finally, we conduct experiments
on realistic AGV scenarios which illustrate that the proposed framework
requires human intervention less frequently, trading for a limited increase in
the path distance. | [
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] |
Connected vehicles (CVs), because of the external connectivity with other CVs
and connected infrastructure, are vulnerable to cyberattacks that can instantly
compromise the safety of the vehicle itself and other connected vehicles and
roadway infrastructure. One such cyberattack is the false information attack,
where an external attacker injects inaccurate information into the connected
vehicles and eventually can cause catastrophic consequences by compromising
safety-critical applications like the forward collision warning. The occurrence
and target of such attack events can be very dynamic, making real-time and
near-real-time detection challenging. Change point models, can be used for
real-time anomaly detection caused by the false information attack. In this
paper, we have evaluated three change point-based statistical models;
Expectation Maximization, Cumulative Summation, and Bayesian Online Change
Point Algorithms for cyberattack detection in the CV data. Also, data-driven
artificial intelligence (AI) models, which can be used to detect known and
unknown underlying patterns in the dataset, have the potential of detecting a
real-time anomaly in the CV data. We have used six AI models to detect false
information attacks and compared the performance for detecting the attacks with
our developed change point models. Our study shows that change points models
performed better in real-time false information attack detection compared to
the performance of the AI models. Change point models having the advantage of
no training requirements can be a feasible and computationally efficient
alternative to AI models for false information attack detection in connected
vehicles. | [
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] |
Manifold ranking has been successfully applied in query-oriented
multi-document summarization. It not only makes use of the relationships among
the sentences, but also the relationships between the given query and the
sentences. However, the information of original query is often insufficient. So
we present a query expansion method, which is combined in the manifold ranking
to resolve this problem. Our method not only utilizes the information of the
query term itself and the knowledge base WordNet to expand it by synonyms, but
also uses the information of the document set itself to expand the query in
various ways (mean expansion, variance expansion and TextRank expansion).
Compared with the previous query expansion methods, our method combines
multiple query expansion methods to better represent query information, and at
the same time, it makes a useful attempt on manifold ranking. In addition, we
use the degree of word overlap and the proximity between words to calculate the
similarity between sentences. We performed experiments on the datasets of DUC
2006 and DUC2007, and the evaluation results show that the proposed query
expansion method can significantly improve the system performance and make our
system comparable to the state-of-the-art systems. | [
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] |
Accounting for the fact that users have different sequential patterns, the
main drawback of state-of-the-art recommendation strategies is that a fixed
sequence length of user-item interactions is required as input to train the
models. This might limit the recommendation accuracy, as in practice users
follow different trends on the sequential recommendations. Hence, baseline
strategies might ignore important sequential interactions or add noise to the
models with redundant interactions, depending on the variety of users'
sequential behaviours. To overcome this problem, in this study we propose the
SAR model, which not only learns the sequential patterns but also adjusts the
sequence length of user-item interactions in a personalized manner. We first
design an actor-critic framework, where the RL agent tries to compute the
optimal sequence length as an action, given the user's state representation at
a certain time step. In addition, we optimize a joint loss function to align
the accuracy of the sequential recommendations with the expected cumulative
rewards of the critic network, while at the same time we adapt the sequence
length with the actor network in a personalized manner. Our experimental
evaluation on four real-world datasets demonstrates the superiority of our
proposed model over several baseline approaches. Finally, we make our
implementation publicly available at https://github.com/stefanosantaris/sar. | [
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] |
Deep Generative Models (DGMs) are a popular class of deep learning models
which find widespread use because of their ability to synthesize data from
complex, high-dimensional manifolds. However, even with their increasing
industrial adoption, they haven't been subject to rigorous security and privacy
analysis. In this work we examine one such aspect, namely backdoor attacks on
DGMs which can significantly limit the applicability of pre-trained models
within a model supply chain and at the very least cause massive reputation
damage for companies outsourcing DGMs form third parties.
While similar attacks scenarios have been studied in the context of classical
prediction models, their manifestation in DGMs hasn't received the same
attention. To this end we propose novel training-time attacks which result in
corrupted DGMs that synthesize regular data under normal operations and
designated target outputs for inputs sampled from a trigger distribution. These
attacks are based on an adversarial loss function that combines the dual
objectives of attack stealth and fidelity. We systematically analyze these
attacks, and show their effectiveness for a variety of approaches like
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as
well as different data domains including images and audio. Our experiments show
that - even for large-scale industry-grade DGMs (like StyleGAN) - our attacks
can be mounted with only modest computational effort. We also motivate suitable
defenses based on static/dynamic model and output inspections, demonstrate
their usefulness, and prescribe a practical and comprehensive defense strategy
that paves the way for safe usage of DGMs. | [
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Although the recent rise and uptake of COVID-19 vaccines in the United States
has been encouraging, there continues to be significant vaccine hesitancy in
various geographic and demographic clusters of the adult population. Surveys,
such as the one conducted by Gallup over the past year, can be useful in
determining vaccine hesitancy, but can be expensive to conduct and do not
provide real-time data. At the same time, the advent of social media suggests
that it may be possible to get vaccine hesitancy signals at an aggregate level
(such as at the level of zip codes) by using machine learning models and
socioeconomic (and other) features from publicly available sources. It is an
open question at present whether such an endeavor is feasible, and how it
compares to baselines that only use constant priors. To our knowledge, a proper
methodology and evaluation results using real data has also not been presented.
In this article, we present such a methodology and experimental study, using
publicly available Twitter data collected over the last year. Our goal is not
to devise novel machine learning algorithms, but to evaluate existing and
established models in a comparative framework. We show that the best models
significantly outperform constant priors, and can be set up using open-source
tools. | [
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] |
Graph clustering and community detection are central problems in modern data
mining. The increasing need for analyzing billion-scale data calls for faster
and more scalable algorithms for these problems. There are certain trade-offs
between the quality and speed of such clustering algorithms. In this paper, we
design scalable algorithms that achieve high quality when evaluated based on
ground truth. We develop a generalized sequential and shared-memory parallel
framework based on the LambdaCC objective (introduced by Veldt et al.), which
encompasses modularity and correlation clustering. Our framework consists of
highly-optimized implementations that scale to large data sets of billions of
edges and that obtain high-quality clusters compared to ground-truth data, on
both unweighted and weighted graphs. Our empirical evaluation shows that this
framework improves the state-of-the-art trade-offs between speed and quality of
scalable community detection. For example, on a 30-core machine with two-way
hyper-threading, our implementations achieve orders of magnitude speedups over
other correlation clustering baselines, and up to 28.44x speedups over our own
sequential baselines while maintaining or improving quality. | [
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] |
Machine learning (ML) models are known to be vulnerable to adversarial
examples. Applications of ML to voice biometrics authentication are no
exception. Yet, the implications of audio adversarial examples on these
real-world systems remain poorly understood given that most research targets
limited defenders who can only listen to the audio samples. Conflating
detectability of an attack with human perceptibility, research has focused on
methods that aim to produce imperceptible adversarial examples which humans
cannot distinguish from the corresponding benign samples. We argue that this
perspective is coarse for two reasons: 1. Imperceptibility is impossible to
verify; it would require an experimental process that encompasses variations in
listener training, equipment, volume, ear sensitivity, types of background
noise etc, and 2. It disregards pipeline-based detection clues that realistic
defenders leverage. This results in adversarial examples that are ineffective
in the presence of knowledgeable defenders. Thus, an adversary only needs an
audio sample to be plausible to a human. We thus introduce surreptitious
adversarial examples, a new class of attacks that evades both human and
pipeline controls. In the white-box setting, we instantiate this class with a
joint, multi-stage optimization attack. Using an Amazon Mechanical Turk user
study, we show that this attack produces audio samples that are more
surreptitious than previous attacks that aim solely for imperceptibility.
Lastly we show that surreptitious adversarial examples are challenging to
develop in the black-box setting. | [
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Vision videos are established for soliciting feedback and stimulating
discussions in requirements engineering (RE) practices, such as focus groups.
Different researchers motivated the transfer of these benefits into crowd-based
RE (CrowdRE) by using vision videos on social media platforms. So far, however,
little research explored the potential of using vision videos for CrowdRE in
detail. In this paper, we analyze and assess this potential, in particular,
focusing on video comments as a source of feedback. In a case study, we
analyzed 4505 comments on a vision video from YouTube. We found that the video
solicited 2770 comments from 2660 viewers in four days. This is more than 50%
of all comments the video received in four years. Even though only a certain
fraction of these comments are relevant to RE, the relevant comments address
typical intentions and topics of user feedback, such as feature request or
problem report. Besides the typical user feedback categories, we found more
than 300 comments that address the topic safety, which has not appeared in
previous analyses of user feedback. In an automated analysis, we compared the
performance of three machine learning algorithms on classifying the video
comments. Despite certain differences, the algorithms classified the video
comments well. Based on these findings, we conclude that the use of vision
videos for CrowdRE has a large potential. Despite the preliminary nature of the
case study, we are optimistic that vision videos can motivate stakeholders to
actively participate in a crowd and solicit numerous of video comments as a
valuable source of feedback. | [
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] |
Deep learning for recommendation data is one of the most pervasive and
challenging AI workload in recent times. State-of-the-art recommendation models
are one of the largest models matching the likes of GPT-3 and Switch
Transformer. Challenges in deep learning recommendation models (DLRM) stem from
learning dense embeddings for each of the categorical tokens. These embedding
tables in industrial scale models can be as large as hundreds of terabytes.
Such large models lead to a plethora of engineering challenges, not to mention
prohibitive communication overheads, and slower training and inference times.
Of these, slower inference time directly impacts user experience. Model
compression for DLRM is gaining traction and the community has recently shown
impressive compression results. In this paper, we present Random Offset Block
Embedding Array (ROBE) as a low memory alternative to embedding tables which
provide orders of magnitude reduction in memory usage while maintaining
accuracy and boosting execution speed. ROBE is a simple fundamental approach in
improving both cache performance and the variance of randomized hashing, which
could be of independent interest in itself. We demonstrate that we can
successfully train DLRM models with same accuracy while using $1000 \times$
less memory. A $1000\times$ compressed model directly results in faster
inference without any engineering effort. In particular, we show that we can
train DLRM model using ROBE array of size 100MB on a single GPU to achieve AUC
of 0.8025 or higher as required by official MLPerf CriteoTB benchmark DLRM
model of 100GB while achieving about $3.1\times$ (209\%) improvement in
inference throughput. | [
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] |
Modern hardware design trends have shifted towards specialized hardware
acceleration for computationally intensive tasks like machine learning and
computer vision. While these complex workloads can be accelerated by commercial
GPUs, domain-specific hardware is far more optimal when needing to meet the
stringent memory, throughput, and power constraints of mobile and embedded
devices. This paper proposes and evaluates a Binary-Enabled Architecture for
Neural Network Acceleration (BEANNA), a neural network hardware accelerator
capable of processing both floating point and binary network layers. Through
the use of a novel 16x16 systolic array based matrix multiplier with processing
elements that compute both floating point and binary multiply-adds, BEANNA
seamlessly switches between high precision floating point and binary neural
network layers. Running at a clock speed of 100MHz, BEANNA achieves a peak
throughput of 52.8 GigaOps/second when operating in high precision mode, and
820 GigaOps/second when operating in binary mode. Evaluation of BEANNA was
performed by comparing a hybrid network with floating point outer layers and
binary hidden layers to a network with only floating point layers. The hybrid
network accelerated using BEANNA achieved a 194% throughput increase, a 68%
memory usage decrease, and a 66% energy consumption decrease per inference, all
this at the cost of a mere 0.23% classification accuracy decrease on the MNIST
dataset. | [
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Federated Learning (FL) has evolved as a promising technique to handle
distributed machine learning across edge devices. A single neural network (NN)
that optimises a global objective is generally learned in most work in FL,
which could be suboptimal for edge devices. Although works finding a NN
personalised for edge device specific tasks exist, they lack generalisation
and/or convergence guarantees. In this paper, a novel communication efficient
FL algorithm for personalised learning in a wireless setting with guarantees is
presented. The algorithm relies on finding a ``better`` empirical estimate of
losses at each device, using a weighted average of the losses across different
devices. It is devised from a Probably Approximately Correct (PAC) bound on the
true loss in terms of the proposed empirical loss and is bounded by (i) the
Rademacher complexity, (ii) the discrepancy, (iii) and a penalty term. Using a
signed gradient feedback to find a personalised NN at each device, it is also
proven to converge in a Rayleigh flat fading (in the uplink) channel, at a rate
of the order max{1/SNR,1/sqrt(T)} Experimental results show that the proposed
algorithm outperforms locally trained devices as well as the conventionally
used FedAvg and FedSGD algorithms under practical SNR regimes. | [
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] |
Machine learning (ML) is increasingly applied to Electronic Health Records
(EHRs) to solve clinical prediction tasks. Although many ML models perform
promisingly, issues with model transparency and interpretability limit their
adoption in clinical practice. Directly using existing explainable ML
techniques in clinical settings can be challenging. Through literature surveys
and collaborations with six clinicians with an average of 17 years of clinical
experience, we identified three key challenges, including clinicians'
unfamiliarity with ML features, lack of contextual information, and the need
for cohort-level evidence. Following an iterative design process, we further
designed and developed VBridge, a visual analytics tool that seamlessly
incorporates ML explanations into clinicians' decision-making workflow. The
system includes a novel hierarchical display of contribution-based feature
explanations and enriched interactions that connect the dots between ML
features, explanations, and data. We demonstrated the effectiveness of VBridge
through two case studies and expert interviews with four clinicians, showing
that visually associating model explanations with patients' situational records
can help clinicians better interpret and use model predictions when making
clinician decisions. We further derived a list of design implications for
developing future explainable ML tools to support clinical decision-making. | [
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] |
Institutional investors have been increasing the allocation of the illiquid
alternative assets such as private equity funds in their portfolios, yet there
exists a very limited literature on cash flow forecasting of illiquid
alternative assets. The net cash flow of private equity funds typically follow
a J-curve pattern, however the timing and the size of the contributions and
distributions depend on the investment opportunities. In this paper, we develop
a benchmark model and present two novel approaches (direct vs. indirect) to
predict the cash flows of private equity funds. We introduce a sliding window
approach to apply on our cash flow data because different vintage year funds
contain different lengths of cash flow information. We then pass the data to an
LSTM/ GRU model to predict the future cash flows either directly or indirectly
(based on the benchmark model). We further integrate macroeconomic indicators
into our data, which allows us to consider the impact of market environment on
cash flows and to apply stress testing. Our results indicate that the direct
model is easier to implement compared to the benchmark model and the indirect
model, but still the predicted cash flows align better with the actual cash
flows. We also show that macroeconomic variables improve the performance of the
direct model whereas the impact is not obvious on the indirect model. | [
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The need of predictive maintenance comes with an increasing number of
incidents reported by monitoring systems and equipment/software users. In the
front line, on-call engineers (OCEs) have to quickly assess the degree of
severity of an incident and decide which service to contact for corrective
actions. To automate these decisions, several predictive models have been
proposed, but the most efficient models are opaque (say, black box), strongly
limiting their adoption. In this paper, we propose an efficient black box model
based on 170K incidents reported to our company over the last 7 years and
emphasize on the need of automating triage when incidents are massively
reported on thousands of servers running our product, an ERP. Recent
developments in eXplainable Artificial Intelligence (XAI) help in providing
global explanations to the model, but also, and most importantly, with local
explanations for each model prediction/outcome. Sadly, providing a human with
an explanation for each outcome is not conceivable when dealing with an
important number of daily predictions. To address this problem, we propose an
original data-mining method rooted in Subgroup Discovery, a pattern mining
technique with the natural ability to group objects that share similar
explanations of their black box predictions and provide a description for each
group. We evaluate this approach and present our preliminary results which give
us good hope towards an effective OCE's adoption. We believe that this approach
provides a new way to address the problem of model agnostic outcome
explanation. | [
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] |
Random reconstruction of three-dimensional (3D) digital rocks from
two-dimensional (2D) slices is crucial for elucidating the microstructure of
rocks and its effects on pore-scale flow in terms of numerical modeling, since
massive samples are usually required to handle intrinsic uncertainties. Despite
remarkable advances achieved by traditional process-based methods, statistical
approaches and recently famous deep learning-based models, few works have
focused on producing several kinds of rocks with one trained model and allowing
the reconstructed samples to satisfy certain given properties, such as
porosity. To fill this gap, we propose a new framework, named RockGPT, which is
composed of VQ-VAE and conditional GPT, to synthesize 3D samples based on a
single 2D slice from the perspective of video generation. The VQ-VAE is
utilized to compress high-dimensional input video, i.e., the sequence of
continuous rock slices, to discrete latent codes and reconstruct them. In order
to obtain diverse reconstructions, the discrete latent codes are modeled using
conditional GPT in an autoregressive manner, while incorporating conditional
information from a given slice, rock type, and porosity. We conduct two
experiments on five kinds of rocks, and the results demonstrate that RockGPT
can produce different kinds of rocks with the same model, and the reconstructed
samples can successfully meet certain specified porosities. In a broader sense,
through leveraging the proposed conditioning scheme, RockGPT constitutes an
effective way to build a general model to produce multiple kinds of rocks
simultaneously that also satisfy user-defined properties. | [
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] |
Imitating human demonstrations is a promising approach to endow robots with
various manipulation capabilities. While recent advances have been made in
imitation learning and batch (offline) reinforcement learning, a lack of
open-source human datasets and reproducible learning methods make assessing the
state of the field difficult. In this paper, we conduct an extensive study of
six offline learning algorithms for robot manipulation on five simulated and
three real-world multi-stage manipulation tasks of varying complexity, and with
datasets of varying quality. Our study analyzes the most critical challenges
when learning from offline human data for manipulation. Based on the study, we
derive a series of lessons including the sensitivity to different algorithmic
design choices, the dependence on the quality of the demonstrations, and the
variability based on the stopping criteria due to the different objectives in
training and evaluation. We also highlight opportunities for learning from
human datasets, such as the ability to learn proficient policies on
challenging, multi-stage tasks beyond the scope of current reinforcement
learning methods, and the ability to easily scale to natural, real-world
manipulation scenarios where only raw sensory signals are available. We have
open-sourced our datasets and all algorithm implementations to facilitate
future research and fair comparisons in learning from human demonstration data.
Codebase, datasets, trained models, and more available at
https://arise-initiative.github.io/robomimic-web/ | [
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The core network architecture of telecommunication systems has undergone a
paradigm shift in the fifth-generation (5G)networks. 5G networks have
transitioned to software-defined infrastructures, thereby reducing their
dependence on hardware-based network functions. New technologies, like network
function virtualization and software-defined networking, have been incorporated
in the 5G core network (5GCN) architecture to enable this transition. This has
resulted in significant improvements in efficiency, performance, and robustness
of the networks. However, this has also made the core network more vulnerable,
as software systems are generally easier to compromise than hardware systems.
In this article, we present a comprehensive security analysis framework for the
5GCN. The novelty of this approach lies in the creation and analysis of attack
graphs of the software-defined and virtualized 5GCN through machine learning.
This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate
that these possible exploits of 5GCN vulnerabilities generate five novel
attacks on the 5G Authentication and Key Agreement protocol. We combine the
attacks at the network, protocol, and the application layers to generate
complex attack vectors. In a case study, we use these attack vectors to find
four novel security loopholes in WhatsApp running on a 5G network. | [
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] |
Code Smell, similar to a bad smell, is a surface indication of something
tainted but in terms of software writing practices. This metric is an
indication of a deeper problem lies within the code and is associated with an
issue which is prominent to experienced software developers with acceptable
coding practices. Recent studies have often observed that codes having code
smells are often prone to a higher probability of change in the software
development cycle. In this paper, we developed code smell prediction models
with the help of features extracted from source code to predict eight types of
code smell. Our work also presents the application of data sampling techniques
to handle class imbalance problem and feature selection techniques to find
relevant feature sets. Previous studies had made use of techniques such as
Naive - Bayes and Random forest but had not explored deep learning methods to
predict code smell. A total of 576 distinct Deep Learning models were trained
using the features and datasets mentioned above. The study concluded that the
deep learning models which used data from Synthetic Minority Oversampling
Technique gave better results in terms of accuracy, AUC with the accuracy of
some models improving from 88.47 to 96.84. | [
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] |
Adversarial patch attacks create adversarial examples by injecting arbitrary
distortions within a bounded region of the input to fool deep neural networks
(DNNs). These attacks are robust (i.e., physically-realizable) and universally
malicious, and hence represent a severe security threat to real-world DNN-based
systems.
We propose Jujutsu, a two-stage technique to detect and mitigate robust and
universal adversarial patch attacks. We first observe that adversarial patches
are crafted as localized features that yield large influence on the prediction
output, and continue to dominate the prediction on any input. Jujutsu leverages
this observation for accurate attack detection with low false positives. Patch
attacks corrupt only a localized region of the input, while the majority of the
input remains unperturbed. Therefore, Jujutsu leverages generative adversarial
networks (GAN) to perform localized attack recovery by synthesizing the
semantic contents of the input that are corrupted by the attacks, and
reconstructs a ``clean'' input for correct prediction.
We evaluate Jujutsu on four diverse datasets spanning 8 different DNN models,
and find that it achieves superior performance and significantly outperforms
four existing defenses. We further evaluate Jujutsu against physical-world
attacks, as well as adaptive attacks. | [
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] |
Recommender systems aim to provide personalized services to users and are
playing an increasingly important role in our daily lives. The key of
recommender systems is to predict how likely users will interact with items
based on their historical online behaviors, e.g., clicks, add-to-cart,
purchases, etc. To exploit these user-item interactions, there are increasing
efforts on considering the user-item interactions as a user-item bipartite
graph and then performing information propagation in the graph via Graph Neural
Networks (GNNs). Given the power of GNNs in graph representation learning,
these GNNs-based recommendation methods have remarkably boosted the
recommendation performance. Despite their success, most existing GNNs-based
recommender systems overlook the existence of interactions caused by unreliable
behaviors (e.g., random/bait clicks) and uniformly treat all the interactions,
which can lead to sub-optimal and unstable performance. In this paper, we
investigate the drawbacks (e.g., non-adaptive propagation and non-robustness)
of existing GNN-based recommendation methods. To address these drawbacks, we
introduce a principled graph trend collaborative filtering method and propose
the Graph Trend Filtering Networks for recommendations (GTN) that can capture
the adaptive reliability of the interactions. Comprehensive experiments and
ablation studies are presented to verify and understand the effectiveness of
the proposed framework. Our implementation based on PyTorch is available at
https://github.com/wenqifan03/GTN-SIGIR2022. | [
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] |
Given a Boolean specification between a set of inputs and outputs, the
problem of Boolean functional synthesis is to synthesise each output as a
function of inputs such that the specification is met. Although the past few
years have witnessed intense algorithmic development, accomplishing scalability
remains the holy grail. The state-of-the-art approach combines machine learning
and automated reasoning to efficiently synthesise Boolean functions. In this
paper, we propose four algorithmic improvements for a data-driven framework for
functional synthesis: using a dependency-driven multi-classifier to learn
candidate function, extracting uniquely defined functions by interpolation,
variables retention, and using lexicographic MaxSAT to repair candidates. We
implement these improvements in the state-of-the-art framework, called Manthan.
The proposed framework is called Manthan2. Manthan2 shows significantly
improved runtime performance compared to Manthan. In an extensive experimental
evaluation on 609 benchmarks, Manthan2 is able to synthesise a Boolean function
vector for 509 instances compared to 356 instances solved by Manthan--- an
increment of 153 instances over the state-of-the-art. To put this into
perspective, Manthan improved on the prior state-of-the-art by only 76
instances. | [
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] |
Collaborative filtering (CF) is a long-standing problem of recommender
systems. Many novel methods have been proposed, ranging from classical matrix
factorization to recent graph convolutional network-based approaches. After
recent fierce debates, researchers started to focus on linear graph
convolutional networks (GCNs) with a layer combination, which show
state-of-the-art accuracy in many datasets. In this work, we extend them based
on neural ordinary differential equations (NODEs), because the linear GCN
concept can be interpreted as a differential equation, and present the method
of Learnable-Time ODE-based Collaborative Filtering (LT-OCF). The main novelty
in our method is that after redesigning linear GCNs on top of the NODE regime,
i) we learn the optimal architecture rather than relying on manually designed
ones, ii) we learn smooth ODE solutions that are considered suitable for CF,
and iii) we test with various ODE solvers that internally build a diverse set
of neural network connections. We also present a novel training method
specialized to our method. In our experiments with three benchmark datasets,
Gowalla, Yelp2018, and Amazon-Book, our method consistently shows better
accuracy than existing methods, e.g., a recall of 0.0411 by LightGCN vs. 0.0442
by LT-OCF and an NDCG of 0.0315 by LightGCN vs. 0.0341 by LT-OCF in
Amazon-Book. One more important discovery in our experiments that is worth
mentioning is that our best accuracy was achieved by dense connections rather
than linear connections. | [
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] |
In today's tech-savvy world every industry is trying to formulate methods for
recommending products by combining several techniques and algorithms to form a
pool that would bring forward the most enhanced models for making the
predictions. Building on these lines is our paper focused on the application of
sentiment analysis for recommendation in the insurance domain. We tried
building the following Machine Learning models namely, Logistic Regression,
Multinomial Naive Bayes, and the mighty Random Forest for analyzing the
polarity of a given feedback line given by a customer. Then we used this
polarity along with other attributes like Age, Gender, Locality, Income, and
the list of other products already purchased by our existing customers as input
for our recommendation model. Then we matched the polarity score along with the
user's profiles and generated the list of insurance products to be recommended
in descending order. Despite our model's simplicity and the lack of the key
data sets, the results seemed very logical and realistic. So, by developing the
model with more enhanced methods and with access to better and true data
gathered from an insurance industry may be the sector could be very well
benefitted from the amalgamation of sentiment analysis with a recommendation. | [
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] |
In our previous work, we designed a systematic policy to prioritize sampling
locations to lead significant accuracy improvement in spatial interpolation by
using the prediction uncertainty of Gaussian Process Regression (GPR) as
"attraction force" to deployed robots in path planning. Although the
integration with Traveling Salesman Problem (TSP) solvers was also shown to
produce relatively short travel distance, we here hypothesise several factors
that could decrease the overall prediction precision as well because
sub-optimal locations may eventually be included in their paths. To address
this issue, in this paper, we first explore "local planning" approaches
adopting various spatial ranges within which next sampling locations are
prioritized to investigate their effects on the prediction performance as well
as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level
controllers are trained to adaptively produce blended plans from a particular
set of local planners to inherit unique strengths from that selection depending
on latest prediction states. Our experiments on use cases of temperature
monitoring robots demonstrate that the dynamic mixtures of planners can not
only generate sophisticated, informative plans that a single planner could not
create alone but also ensure significantly reduced travel distances at no cost
of prediction reliability without any assist of additional modules for shortest
path calculation. | [
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] |
In order to model the evolution of user preference, we should learn user/item
embeddings based on time-ordered item purchasing sequences, which is defined as
Sequential Recommendation (SR) problem. Existing methods leverage sequential
patterns to model item transitions. However, most of them ignore crucial
temporal collaborative signals, which are latent in evolving user-item
interactions and coexist with sequential patterns. Therefore, we propose to
unify sequential patterns and temporal collaborative signals to improve the
quality of recommendation, which is rather challenging. Firstly, it is hard to
simultaneously encode sequential patterns and collaborative signals. Secondly,
it is non-trivial to express the temporal effects of collaborative signals.
Hence, we design a new framework Temporal Graph Sequential Recommender
(TGSRec) upon our defined continuous-time bi-partite graph. We propose a novel
Temporal Collaborative Trans-former (TCT) layer in TGSRec, which advances the
self-attention mechanism by adopting a novel collaborative attention. TCT layer
can simultaneously capture collaborative signals from both users and items, as
well as considering temporal dynamics inside sequential patterns. We propagate
the information learned fromTCTlayerover the temporal graph to unify sequential
patterns and temporal collaborative signals. Empirical results on five datasets
show that TGSRec significantly outperforms other baselines, in average up to
22.5% and 22.1%absolute improvements in Recall@10and MRR, respectively. | [
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Many researchers have studied student academic performance in supervised and
unsupervised learning using numerous data mining techniques. Neural networks
often need a greater collection of observations to achieve enough predictive
ability. Due to the increase in the rate of poor graduates, it is necessary to
design a system that helps to reduce this menace as well as reduce the
incidence of students having to repeat due to poor performance or having to
drop out of school altogether in the middle of the pursuit of their career. It
is therefore necessary to study each one as well as their advantages and
disadvantages, so as to determine which is more efficient in and in what case
one should be preferred over the other. The study aims to develop a system to
predict student performance with Artificial Neutral Network using the student
demographic traits so as to assist the university in selecting candidates
(students) with a high prediction of success for admission using previous
academic records of students granted admissions which will eventually lead to
quality graduates of the institution. The model was developed based on certain
selected variables as the input. It achieved an accuracy of over 92.3 percent,
showing Artificial Neural Network potential effectiveness as a predictive tool
and a selection criterion for candidates seeking admission to a university. | [
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] |
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have
been proposed to improve the recommendation accuracy in a target dataset
(domain/system) with the help of a source one with relatively richer
information. However, most existing CDR and CSR approaches are single-target,
namely, there is a single target dataset, which can only help the target
dataset and thus cannot benefit the source dataset. In this paper, we focus on
three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR),
and CDR+CSR, and aim to improve the recommendation accuracy in all datasets
simultaneously for all scenarios. To do this, we propose a unified framework,
called GA (based on Graph embedding and Attention techniques), for all three
scenarios. In GA, we first construct separate heterogeneous graphs to generate
more representative user and item embeddings. Then, we propose an element-wise
attention mechanism to effectively combine the embeddings of common entities
(users/items) learned from different datasets. Moreover, to avoid negative
transfer, we further propose a Personalized training strategy to minimize the
embedding difference of common entities between a richer dataset and a sparser
dataset, deriving three new models, i.e., GA-DTCDR-P, GA-MTCDR-P, and
GA-CDR+CSR-P, for the three scenarios respectively. Extensive experiments
conducted on four real-world datasets demonstrate that our proposed GA models
significantly outperform the state-of-the-art approaches. | [
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] |
We are interested in learning generative models for complex geometries
described via manifolds, such as spheres, tori, and other implicit surfaces.
Current extensions of existing (Euclidean) generative models are restricted to
specific geometries and typically suffer from high computational costs. We
introduce Moser Flow (MF), a new class of generative models within the family
of continuous normalizing flows (CNF). MF also produces a CNF via a solution to
the change-of-variable formula, however differently from other CNF methods, its
model (learned) density is parameterized as the source (prior) density minus
the divergence of a neural network (NN). The divergence is a local, linear
differential operator, easy to approximate and calculate on manifolds.
Therefore, unlike other CNFs, MF does not require invoking or backpropagating
through an ODE solver during training. Furthermore, representing the model
density explicitly as the divergence of a NN rather than as a solution of an
ODE facilitates learning high fidelity densities. Theoretically, we prove that
MF constitutes a universal density approximator under suitable assumptions.
Empirically, we demonstrate for the first time the use of flow models for
sampling from general curved surfaces and achieve significant improvements in
density estimation, sample quality, and training complexity over existing CNFs
on challenging synthetic geometries and real-world benchmarks from the earth
and climate sciences. | [
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] |
Virtual Reality (VR) games that feature physical activities have been shown
to increase players' motivation to do physical exercise. However, for such
exercises to have a positive healthcare effect, they have to be repeated
several times a week. To maintain player motivation over longer periods of
time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the
game's challenge according to the player's capabilities. For exercise games,
this is mostly done by tuning specific in-game parameters like the speed of
objects. In this work, we propose to use experience-driven Procedural Content
Generation for DDA in VR exercise games by procedurally generating levels that
match the player's current capabilities. Not only finetuning specific
parameters but creating completely new levels has the potential to decrease
repetition over longer time periods and allows for the simultaneous adaptation
of the cognitive and physical challenge of the exergame. As a proof-of-concept,
we implement an initial prototype in which the player must traverse a maze that
includes several exercise rooms, whereby the generation of the maze is realized
by a neural network. Passing those exercise rooms requires the player to
perform physical activities. To match the player's capabilities, we use Deep
Reinforcement Learning to adjust the structure of the maze and to decide which
exercise rooms to include in the maze. We evaluate our prototype in an
exploratory user study utilizing both biodata and subjective questionnaires. | [
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] |
Recommender system plays a crucial role in modern E-commerce platform. Due to
the lack of historical interactions between users and items, cold-start
recommendation is a challenging problem. In order to alleviate the cold-start
issue, most existing methods introduce content and contextual information as
the auxiliary information. Nevertheless, these methods assume the recommended
items behave steadily over time, while in a typical E-commerce scenario, items
generally have very different performances throughout their life period. In
such a situation, it would be beneficial to consider the long-term return from
the item perspective, which is usually ignored in conventional methods.
Reinforcement learning (RL) naturally fits such a long-term optimization
problem, in which the recommender could identify high potential items,
proactively allocate more user impressions to boost their growth, therefore
improve the multi-period cumulative gains. Inspired by this idea, we model the
process as a Partially Observable and Controllable Markov Decision Process
(POC-MDP), and propose an actor-critic RL framework (RL-LTV) to incorporate the
item lifetime values (LTV) into the recommendation. In RL-LTV, the critic
studies historical trajectories of items and predict the future LTV of fresh
item, while the actor suggests a score-based policy which maximizes the future
LTV expectation. Scores suggested by the actor are then combined with classical
ranking scores in a dual-rank framework, therefore the recommendation is
balanced with the LTV consideration. Our method outperforms the strong live
baseline with a relative improvement of 8.67% and 18.03% on IPV and GMV of
cold-start items, on one of the largest E-commerce platform. | [
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In S&P '21, Jia et al. proposed a new concept/mechanism named
proof-of-learning (PoL), which allows a prover to demonstrate ownership of a
machine learning model by proving integrity of the training procedure. It
guarantees that an adversary cannot construct a valid proof with less cost (in
both computation and storage) than that made by the prover in generating the
proof.
A PoL proof includes a set of intermediate models recorded during training,
together with the corresponding data points used to obtain each recorded model.
Jia et al. claimed that an adversary merely knowing the final model and
training dataset cannot efficiently find a set of intermediate models with
correct data points.
In this paper, however, we show that PoL is vulnerable to ``adversarial
examples''! Specifically, in a similar way as optimizing an adversarial
example, we could make an arbitrarily-chosen data point ``generate'' a given
model, hence efficiently generating intermediate models with correct data
points. We demonstrate, both theoretically and empirically, that we are able to
generate a valid proof with significantly less cost than generating a proof by
the prover. | [
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] |
Recent systems applying Machine Learning (ML) to solve the Traveling Salesman
Problem (TSP) exhibit issues when they try to scale up to real case scenarios
with several hundred vertices. The use of Candidate Lists (CLs) has been
brought up to cope with the issues. The procedure allows to restrict the search
space during solution creation, consequently reducing the solver computational
burden. So far, ML were engaged to create CLs and values on the edges of these
CLs expressing ML preferences at solution insertion. Although promising, these
systems do not clearly restrict what the ML learns and does to create
solutions, bringing with them some generalization issues. Therefore, motivated
by exploratory and statistical studies, in this work we instead use a machine
learning model to confirm the addition in the solution just for high probable
edges. CLs of the high probable edge are employed as input, and the ML is in
charge of distinguishing cases where such edges are in the optimal solution
from those where they are not. . This strategy enables a better generalization
and creates an efficient balance between machine learning and searching
techniques. Our ML-Constructive heuristic is trained on small instances. Then,
it is able to produce solutions, without losing quality, to large problems as
well. We compare our results with classic constructive heuristics, showing good
performances for TSPLIB instances up to 1748 cities. Although our heuristic
exhibits an expensive constant time operation, we proved that the computational
complexity in worst-case scenario, for the solution construction after
training, is $O(n^2 \log n^2)$, being $n$ the number of vertices in the TSP
instance. | [
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] |
Machine learning methods can detect complex relationships between variables,
but usually do not exploit domain knowledge. This is a limitation because in
many scientific disciplines, such as systems biology, domain knowledge is
available in the form of graphs or networks, and its use can improve model
performance. We need network-based algorithms that are versatile and applicable
in many research areas. In this work, we demonstrate subnetwork detection based
on multi-modal node features using a novel Greedy Decision Forest with inherent
interpretability. The latter will be a crucial factor to retain experts and
gain their trust in such algorithms. To demonstrate a concrete application
example, we focus on bioinformatics, systems biology and particularly
biomedicine, but the presented methodology is applicable in many other domains
as well. Systems biology is a good example of a field in which statistical
data-driven machine learning enables the analysis of large amounts of
multi-modal biomedical data. This is important to reach the future goal of
precision medicine, where the complexity of patients is modeled on a system
level to best tailor medical decisions, health practices and therapies to the
individual patient. Our proposed approach can help to uncover disease-causing
network modules from multi-omics data to better understand complex diseases
such as cancer. | [
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] |
Network troubleshooting is still a heavily human-intensive process. To reduce
the time spent by human operators in the diagnosis process, we present a system
based on (i) unsupervised learning methods for detecting anomalies in the time
domain, (ii) an attention mechanism to rank features in the feature space and
finally (iii) an expert knowledge module able to seamlessly incorporate
previously collected domain-knowledge. In this paper, we thoroughly evaluate
the performance of the full system and of its individual building blocks:
particularly, we consider (i) 10 anomaly detection algorithms as well as (ii)
10 attention mechanisms, that comprehensively represent the current state of
the art in the respective fields. Leveraging a unique collection of
expert-labeled datasets worth several months of real router telemetry data, we
perform a thorough performance evaluation contrasting practical results in
constrained stream-mode settings, with the results achievable by an ideal
oracle in academic settings. Our experimental evaluation shows that (i) the
proposed system is effective in achieving high levels of agreement with the
expert, and (ii) that even a simple statistical approach is able to extract
useful information from expert knowledge gained in past cases, significantly
improving troubleshooting performance. | [
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] |
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the
performance of hypothetical policies leveraging only offline log data. It is
particularly useful in applications where the online interaction involves high
stakes and expensive setting such as precision medicine and recommender
systems. Since many OPE estimators have been proposed and some of them have
hyperparameters to be tuned, there is an emerging challenge for practitioners
to select and tune OPE estimators for their specific application.
Unfortunately, identifying a reliable estimator from results reported in
research papers is often difficult because the current experimental procedure
evaluates and compares the estimators' performance on a narrow set of
hyperparameters and evaluation policies. Therefore, it is difficult to know
which estimator is safe and reliable to use. In this work, we develop
Interpretable Evaluation for Offline Evaluation (IEOE), an experimental
procedure to evaluate OPE estimators' robustness to changes in hyperparameters
and/or evaluation policies in an interpretable manner. Then, using the IEOE
procedure, we perform extensive evaluation of a wide variety of existing
estimators on Open Bandit Dataset, a large-scale public real-world dataset for
OPE. We demonstrate that our procedure can evaluate the estimators' robustness
to the hyperparamter choice, helping us avoid using unsafe estimators. Finally,
we apply IEOE to real-world e-commerce platform data and demonstrate how to use
our protocol in practice. | [
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] |
The training of sparse neural networks is becoming an increasingly important
tool for reducing the computational footprint of models at training and
evaluation, as well enabling the effective scaling up of models. Whereas much
work over the years has been dedicated to specialised pruning techniques,
little attention has been paid to the inherent effect of gradient based
training on model sparsity. In this work, we introduce Powerpropagation, a new
weight-parameterisation for neural networks that leads to inherently sparse
models. Exploiting the behaviour of gradient descent, our method gives rise to
weight updates exhibiting a "rich get richer" dynamic, leaving low-magnitude
parameters largely unaffected by learning. Models trained in this manner
exhibit similar performance, but have a distribution with markedly higher
density at zero, allowing more parameters to be pruned safely. Powerpropagation
is general, intuitive, cheap and straight-forward to implement and can readily
be combined with various other techniques. To highlight its versatility, we
explore it in two very different settings: Firstly, following a recent line of
work, we investigate its effect on sparse training for resource-constrained
settings. Here, we combine Powerpropagation with a traditional weight-pruning
technique as well as recent state-of-the-art sparse-to-sparse algorithms,
showing superior performance on the ImageNet benchmark. Secondly, we advocate
the use of sparsity in overcoming catastrophic forgetting, where compressed
representations allow accommodating a large number of tasks at fixed model
capacity. In all cases our reparameterisation considerably increases the
efficacy of the off-the-shelf methods. | [
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] |
Predicting molecular properties with data-driven methods has drawn much
attention in recent years. Particularly, Graph Neural Networks (GNNs) have
demonstrated remarkable success in various molecular generation and prediction
tasks. In cases where labeled data is scarce, GNNs can be pre-trained on
unlabeled molecular data to first learn the general semantic and structural
information before being fine-tuned for specific tasks. However, most existing
self-supervised pre-training frameworks for GNNs only focus on node-level or
graph-level tasks. These approaches cannot capture the rich information in
subgraphs or graph motifs. For example, functional groups (frequently-occurred
subgraphs in molecular graphs) often carry indicative information about the
molecular properties. To bridge this gap, we propose Motif-based Graph
Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif
generation framework for GNNs. First, for motif extraction from molecular
graphs, we design a molecule fragmentation method that leverages a
retrosynthesis-based algorithm BRICS and additional rules for controlling the
size of motif vocabulary. Second, we design a general motif-based generative
pre-training framework in which GNNs are asked to make topological and label
predictions. This generative framework can be implemented in two different
ways, i.e., breadth-first or depth-first. Finally, to take the multi-scale
information in molecular graphs into consideration, we introduce a multi-level
self-supervised pre-training. Extensive experiments on various downstream
benchmark tasks show that our methods outperform all state-of-the-art
baselines. | [
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