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Recently, utilizing reinforcement learning (RL) to generate molecules with
desired properties has been highlighted as a promising strategy for drug
design. A molecular docking program - a physical simulation that estimates
protein-small molecule binding affinity - can be an ideal reward scoring
function for RL, as it is a straightforward proxy of the therapeutic potential.
Still, two imminent challenges exist for this task. First, the models often
fail to generate chemically realistic and pharmacochemically acceptable
molecules. Second, the docking score optimization is a difficult exploration
problem that involves many local optima and less smooth surfaces with respect
to molecular structure. To tackle these challenges, we propose a novel RL
framework that generates pharmacochemically acceptable molecules with large
docking scores. Our method - Fragment-based generative RL with Explorative
Experience replay for Drug design (FREED) - constrains the generated molecules
to a realistic and qualified chemical space and effectively explores the space
to find drugs by coupling our fragment-based generation method and a novel
error-prioritized experience replay (PER). We also show that our model performs
well on both de novo and scaffold-based schemes. Our model produces molecules
of higher quality compared to existing methods while achieving state-of-the-art
performance on two of three targets in terms of the docking scores of the
generated molecules. We further show with ablation studies that our method,
predictive error-PER (FREED(PE)), significantly improves the model performance. | [
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] |
Easy-to-use programming interfaces paired with cloud-scale processing engines
have enabled big data system users to author arbitrarily complex analytical
jobs over massive volumes of data. However, as the complexity and scale of
analytical jobs increase, they encounter a number of unforeseen problems,
hotspots with large intermediate data on temporary storage, longer job recovery
time after failures, and worse query optimizer estimates being examples of
issues that we are facing at Microsoft.
To address these issues, we propose Phoebe, an efficient learning-based
checkpoint optimizer. Given a set of constraints and an objective function at
compile-time, Phoebe is able to determine the decomposition of job plans, and
the optimal set of checkpoints to preserve their outputs to durable global
storage. Phoebe consists of three machine learning predictors and one
optimization module. For each stage of a job, Phoebe makes accurate predictions
for: (1) the execution time, (2) the output size, and (3) the start/end time
taking into account the inter-stage dependencies. Using these predictions, we
formulate checkpoint optimization as an integer programming problem and propose
a scalable heuristic algorithm that meets the latency requirement of the
production environment.
We demonstrate the effectiveness of Phoebe in production workloads, and show
that we can free the temporary storage on hotspots by more than 70% and restart
failed jobs 68% faster on average with minimum performance impact. Phoebe also
illustrates that adding multiple sets of checkpoints is not cost-efficient,
which dramatically reduces the complexity of the optimization. | [
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] |
There is a widespread intuition that model-based control methods should be
able to surpass the data efficiency of model-free approaches. In this paper we
attempt to evaluate this intuition on various challenging locomotion tasks. We
take a hybrid approach, combining model predictive control (MPC) with a learned
model and model-free policy learning; the learned policy serves as a proposal
for MPC. We find that well-tuned model-free agents are strong baselines even
for high DoF control problems but MPC with learned proposals and models
(trained on the fly or transferred from related tasks) can significantly
improve performance and data efficiency in hard multi-task/multi-goal settings.
Finally, we show that it is possible to distil a model-based planner into a
policy that amortizes the planning computation without any loss of performance.
Videos of agents performing different tasks can be seen at
https://sites.google.com/view/mbrl-amortization/home. | [
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] |
In the last years, XAI research has mainly been concerned with developing new
technical approaches to explain deep learning models. Just recent research has
started to acknowledge the need to tailor explanations to different contexts
and requirements of stakeholders. Explanations must not only suit developers of
models, but also domain experts as well as end users. Thus, in order to satisfy
different stakeholders, explanation methods need to be combined. While
multi-modal explanations have been used to make model predictions more
transparent, less research has focused on treating explanation as a process,
where users can ask for information according to the level of understanding
gained at a certain point in time. Consequently, an opportunity to explore
explanations on different levels of abstraction should be provided besides
multi-modal explanations. We present a process-based approach that combines
multi-level and multi-modal explanations. The user can ask for textual
explanations or visualizations through conversational interaction in a
drill-down manner. We use Inductive Logic Programming, an interpretable machine
learning approach, to learn a comprehensible model. Further, we present an
algorithm that creates an explanatory tree for each example for which a
classifier decision is to be explained. The explanatory tree can be navigated
by the user to get answers of different levels of detail. We provide a
proof-of-concept implementation for concepts induced from a semantic net about
living beings. | [
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] |
Understanding the functional (dis)-similarity of source code is significant
for code modeling tasks such as software vulnerability and code clone
detection. We present DISCO(DIS-similarity of COde), a novel self-supervised
model focusing on identifying (dis)similar functionalities of source code.
Different from existing works, our approach does not require a huge amount of
randomly collected datasets. Rather, we design structure-guided code
transformation algorithms to generate synthetic code clones and inject
real-world security bugs, augmenting the collected datasets in a targeted way.
We propose to pre-train the Transformer model with such automatically generated
program contrasts to better identify similar code in the wild and differentiate
vulnerable programs from benign ones. To better capture the structural features
of source code, we propose a new cloze objective to encode the local tree-based
context (e.g., parents or sibling nodes). We pre-train our model with a much
smaller dataset, the size of which is only 5% of the state-of-the-art models'
training datasets, to illustrate the effectiveness of our data augmentation and
the pre-training approach. The evaluation shows that, even with much less data,
DISCO can still outperform the state-of-the-art models in vulnerability and
code clone detection tasks. | [
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] |
Social recommendation which aims to leverage social connections among users
to enhance the recommendation performance. With the revival of deep learning
techniques, many efforts have been devoted to developing various neural
network-based social recommender systems, such as attention mechanisms and
graph-based message passing frameworks. However, two important challenges have
not been well addressed yet: (i) Most of existing social recommendation models
fail to fully explore the multi-type user-item interactive behavior as well as
the underlying cross-relational inter-dependencies. (ii) While the learned
social state vector is able to model pair-wise user dependencies, it still has
limited representation capacity in capturing the global social context across
users. To tackle these limitations, we propose a new Social Recommendation
framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we
first design a relation-aware reconstructed graph neural network to inject the
cross-type collaborative semantics into the recommendation framework. In
addition, we further augment SR-HGNN with a social relation encoder based on
the mutual information learning paradigm between low-level user embeddings and
high-level global representation, which endows SR-HGNN with the capability of
capturing the global social contextual signals. Empirical results on three
public benchmarks demonstrate that SR-HGNN significantly outperforms
state-of-the-art recommendation methods. Source codes are available at:
https://github.com/xhcdream/SR-HGNN. | [
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] |
For effective planning and management of water resources and implementation
of the related strategies, it is important to ensure proper estimation of
evaporation losses, especially in regions that are prone to drought. Changes in
climatic factors, such as changes in temperature, wind speed, sunshine hours,
humidity, and solar radiation can have a significant impact on the evaporation
process. As such, evaporation is a highly non-linear, non-stationary process,
and can be difficult to be modeled based on climatic factors, especially in
different agro-climatic conditions. The aim of this study, therefore, is to
investigate the feasibility of several machines learning (ML) models
(conditional random forest regression, Multivariate Adaptive Regression
Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K-
nearest neighbor, and the weighted K- nearest neighbor) for modeling the
monthly pan evaporation estimation. This study proposes the development of
newly explored ML models for modeling evaporation losses in three different
locations over the Iraq region based on the available climatic data in such
areas. The evaluation of the performance of the proposed model based on various
evaluation criteria showed the capability of the proposed weighted K- nearest
neighbor model in modeling the monthly evaporation losses in the studies areas
with better accuracy when compared with the other existing models used as a
benchmark in this study. | [
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] |
Prime factorization is a difficult problem with classical computing, whose
exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA)
cryptography. With programmable quantum devices, adiabatic quantum computing
has been proposed as a plausible approach to solve prime factorization, having
promising advantage over classical computing. Here, we find there are certain
hard instances that are consistently intractable for both classical simulated
annealing and un-configured adiabatic quantum computing (AQC). Aiming at an
automated architecture for optimal configuration of quantum adiabatic
factorization, we apply a deep reinforcement learning (RL) method to configure
the AQC algorithm. By setting the success probability of the worst-case problem
instances as the reward to RL, we show the AQC performance on the hard
instances is dramatically improved by RL configuration. The success probability
also becomes more evenly distributed over different problem instances, meaning
the configured AQC is more stable as compared to the un-configured case.
Through a technique of transfer learning, we find prominent evidence that the
framework of AQC configuration is scalable -- the configured AQC as trained on
five qubits remains working efficiently on nine qubits with a minimal amount of
additional training cost. | [
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] |
In the genome biology research, regulatory genome modeling is an important
topic for many regulatory downstream tasks, such as promoter classification,
transaction factor binding sites prediction. The core problem is to model how
regulatory elements interact with each other and its variability across
different cell types. However, current deep learning methods often focus on
modeling genome sequences of a fixed set of cell types and do not account for
the interaction between multiple regulatory elements, making them only perform
well on the cell types in the training set and lack the generalizability
required in biological applications. In this work, we propose a simple yet
effective approach for pre-training genome data in a multi-modal and
self-supervised manner, which we call GeneBERT. Specifically, we simultaneously
take the 1d sequence of genome data and a 2d matrix of (transcription factors x
regions) as the input, where three pre-training tasks are proposed to improve
the robustness and generalizability of our model. We pre-train our model on the
ATAC-seq dataset with 17 million genome sequences. We evaluate our GeneBERT on
regulatory downstream tasks across different cell types, including promoter
classification, transaction factor binding sites prediction, disease risk
estimation, and splicing sites prediction. Extensive experiments demonstrate
the effectiveness of multi-modal and self-supervised pre-training for
large-scale regulatory genomics data. | [
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] |
Our goal is to recover time-delayed latent causal variables and identify
their relations from measured temporal data. Estimating causally-related latent
variables from observations is particularly challenging as the latent variables
are not uniquely recoverable in the most general case. In this work, we
consider both a nonparametric, nonstationary setting and a parametric setting
for the latent processes and propose two provable conditions under which
temporally causal latent processes can be identified from their nonlinear
mixtures. We propose LEAP, a theoretically-grounded framework that extends
Variational AutoEncoders (VAEs) by enforcing our conditions through proper
constraints in causal process prior. Experimental results on various datasets
demonstrate that temporally causal latent processes are reliably identified
from observed variables under different dependency structures and that our
approach considerably outperforms baselines that do not properly leverage
history or nonstationarity information. This demonstrates that using temporal
information to learn latent processes from their invertible nonlinear mixtures
in an unsupervised manner, for which we believe our work is one of the first,
seems promising even without sparsity or minimality assumptions. | [
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] |
Twitter is a social network that offers a rich and interesting source of
information challenging to retrieve and analyze. Twitter data can be accessed
using a REST API. The available operations allow retrieving tweets on the basis
of a set of keywords but with limitations such as the number of calls per
minute and the size of results. Besides, there is no control on retrieved
results and finding tweets which are relevant to a specific topic is a big
issue. Given these limitations, it is important that the query keywords cover
unambiguously the topic of interest in order to both reach the relevant answers
and decrease the number of API calls. In this paper, we introduce a new
crawling algorithm called "SmartTwitter Crawling" (STiC) that retrieves a set
of tweets related to a target topic. In this algorithm, we take an initial
keyword query and enrich it using a set of additional keywords that come from
different data sources. STiC algorithm relies on a DFS search in Twittergraph
where each reached tweet is considered if it is relevant with the query
keywords using a scoring, updated throughout the whole crawling process. This
scoring takes into account the tweet text, hashtags and the users who have
posted the tweet, replied to the tweet, been mentioned in the tweet or
retweeted the tweet. Given this score, STiC is able to select relevant tweets
in each iteration and continue by adding the related valuable tweets. Several
experiments have been achieved for different kinds of queries, the results
showedthat the precision increases compared to a simple BFS search. | [
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] |
Representation learning on temporal interaction graphs (TIG) is to model
complex networks with the dynamic evolution of interactions arising in a broad
spectrum of problems. Existing dynamic embedding methods on TIG discretely
update node embeddings merely when an interaction occurs. They fail to capture
the continuous dynamic evolution of embedding trajectories of nodes. In this
paper, we propose a two-module framework named ConTIG, a continuous
representation method that captures the continuous dynamic evolution of node
embedding trajectories. With two essential modules, our model exploit
three-fold factors in dynamic networks which include latest interaction,
neighbor features and inherent characteristics. In the first update module, we
employ a continuous inference block to learn the nodes' state trajectories by
learning from time-adjacent interaction patterns between node pairs using
ordinary differential equations. In the second transform module, we introduce a
self-attention mechanism to predict future node embeddings by aggregating
historical temporal interaction information. Experiments results demonstrate
the superiority of ConTIG on temporal link prediction, temporal node
recommendation and dynamic node classification tasks compared with a range of
state-of-the-art baselines, especially for long-interval interactions
prediction. | [
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] |
Humans have mental models that allow them to plan, experiment, and reason in
the physical world. How should an intelligent agent go about learning such
models? In this paper, we will study if models of the world learned in an
open-ended physics environment, without any specific tasks, can be reused for
downstream physics reasoning tasks. To this end, we build a benchmark
Open-ended Physics ENvironment (OPEn) and also design several tasks to test
learning representations in this environment explicitly. This setting reflects
the conditions in which real agents (i.e. rolling robots) find themselves,
where they may be placed in a new kind of environment and must adapt without
any teacher to tell them how this environment works. This setting is
challenging because it requires solving an exploration problem in addition to a
model building and representation learning problem. We test several existing
RL-based exploration methods on this benchmark and find that an agent using
unsupervised contrastive learning for representation learning, and
impact-driven learning for exploration, achieved the best results. However, all
models still fall short in sample efficiency when transferring to the
downstream tasks. We expect that OPEn will encourage the development of novel
rolling robot agents that can build reusable mental models of the world that
facilitate many tasks. | [
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] |
Food-choices and eating-habits directly contribute to our long-term health.
This makes the food recommender system a potential tool to address the global
crisis of obesity and malnutrition. Over the past decade,
artificial-intelligence and medical researchers became more invested in
researching tools that can guide and help people make healthy and thoughtful
decisions around food and diet. In many typical (Recommender System) RS
domains, smart nudges have been proven effective in shaping users' consumption
patterns. In recent years, knowledgeable nudging and incentifying choices
started getting attention in the food domain as well. To develop smart nudging
for promoting healthier food choices, we combined Machine Learning and RS
technology with food-healthiness guidelines from recognized health
organizations, such as the World Health Organization, Food Standards Agency,
and the National Health Service United Kingdom. In this paper, we discuss our
research on, persuasive visualization for making users aware of the healthiness
of the recommended recipes. Here, we propose three novel nudging technology,
the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage
users to choose healthier recipes. We also propose a Topic Modeling based
portion-size recommendation algorithm. To evaluate our proposed smart-nudges,
we conducted an online user study with 96 participants and 92250 recipes.
Results showed that, during the food decision-making process, appropriate
healthiness cues make users more likely to click, browse, and choose healthier
recipes over less healthy ones. | [
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] |
Nowadays, using vibration data in conjunction with pattern recognition
methods is one of the most common fault detection strategies for structures.
However, their performances depend on the features extracted from vibration
data, the features selected to train the classifier, and the classifier used
for pattern recognition. Deep learning facilitates the fault detection
procedure by automating the feature extraction and selection, and
classification procedure. Though, deep learning approaches have challenges in
designing its structure and tuning its hyperparameters, which may result in a
low generalization capability. Therefore, this study proposes an ensemble deep
learning framework based on a convolutional neural network (CNN) and
Dempster-Shafer theory (DST), called CNN-DST. In this framework, several CNNs
with the proposed structure are first trained, and then, the outputs of the
CNNs selected by the proposed technique are combined by using an improved
DST-based method. To validate the proposed CNN-DST framework, it is applied to
an experimental dataset created by the broadband vibrational responses of
polycrystalline Nickel alloy first-stage turbine blades with different types
and severities of damage. Through statistical analysis, it is shown that the
proposed CNN-DST framework classifies the turbine blades with an average
prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked
with other state-of-the-art classification methods, demonstrating its high
performance. The robustness of the proposed CNN-DST framework with respect to
measurement noise is investigated, showing its high noise-resistance. Further,
bandwidth analysis reveals that most of the required information for detecting
faulty samples is available in a small frequency range. | [
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] |
Search systems control the exposure of ranked content to searchers. In many
cases, creators value not only the exposure of their content but, moreover, an
understanding of the specific searches where the content is surfaced. The
problem of identifying which queries expose a given piece of content in the
ranking results is an important and relatively under-explored search
transparency challenge. Exposing queries are useful for quantifying various
issues of search bias, privacy, data protection, security, and search engine
optimization.
Exact identification of exposing queries in a given system is computationally
expensive, especially in dynamic contexts such as web search. We explore the
feasibility of approximate exposing query identification (EQI) as a retrieval
task by reversing the role of queries and documents in two classes of search
systems: dense dual-encoder models and traditional BM25 models. We then propose
how this approach can be improved through metric learning over the retrieval
embedding space. We further derive an evaluation metric to measure the quality
of a ranking of exposing queries, as well as conducting an empirical analysis
focusing on various practical aspects of approximate EQI. Overall, our work
contributes a novel conception of transparency in search systems and
computational means of achieving it. | [
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] |
We consider the problem of distributed downlink beam scheduling and power
allocation for millimeter-Wave (mmWave) cellular networks where multiple base
stations (BSs) belonging to different service operators share the same
unlicensed spectrum with no central coordination or cooperation among them. Our
goal is to design efficient distributed beam scheduling and power allocation
algorithms such that the network-level payoff, defined as the weighted sum of
the total throughput and a power penalization term, can be maximized. To this
end, we propose a distributed scheduling approach to power allocation and
adaptation for efficient interference management over the shared spectrum by
modeling each BS as an independent Q-learning agent. As a baseline, we compare
the proposed approach to the state-of-the-art non-cooperative game-based
approach which was previously developed for the same problem. We conduct
extensive experiments under various scenarios to verify the effect of multiple
factors on the performance of both approaches. Experiment results show that the
proposed approach adapts well to different interference situations by learning
from experience and can achieve higher payoff than the game-based approach. The
proposed approach can also be integrated into our previously developed Lyapunov
stochastic optimization framework for the purpose of network utility
maximization with optimality guarantee. As a result, the weights in the payoff
function can be automatically and optimally determined by the virtual queue
values from the sub-problems derived from the Lyapunov optimization framework. | [
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Many modern machine learning applications, such as multi-task learning,
require finding optimal model parameters to trade-off multiple objective
functions that may conflict with each other. The notion of the Pareto set
allows us to focus on the set of (often infinite number of) models that cannot
be strictly improved. But it does not provide an actionable procedure for
picking one or a few special models to return to practical users. In this
paper, we consider \emph{optimization in Pareto set (OPT-in-Pareto)}, the
problem of finding Pareto models that optimize an extra reference criterion
function within the Pareto set. This function can either encode a specific
preference from the users, or represent a generic diversity measure for
obtaining a set of diversified Pareto models that are representative of the
whole Pareto set. Unfortunately, despite being a highly useful framework,
efficient algorithms for OPT-in-Pareto have been largely missing, especially
for large-scale, non-convex, and non-linear objectives in deep learning. A
naive approach is to apply Riemannian manifold gradient descent on the Pareto
set, which yields a high computational cost due to the need for
eigen-calculation of Hessian matrices. We propose a first-order algorithm that
approximately solves OPT-in-Pareto using only gradient information, with both
high practical efficiency and theoretically guaranteed convergence property.
Empirically, we demonstrate that our method works efficiently for a variety of
challenging multi-task-related problems. | [
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] |
When predicting trajectories of road agents, motion predictors usually
approximate the future distribution by a limited number of samples. This
constraint requires the predictors to generate samples that best support the
task given task specifications. However, existing predictors are often
optimized and evaluated via task-agnostic measures without accounting for the
use of predictions in downstream tasks, and thus could result in sub-optimal
task performance.
In this paper, we propose a task-informed motion prediction model that better
supports the tasks through its predictions, by jointly reasoning about
prediction accuracy and the utility of the downstream tasks, which is commonly
used to evaluate the task performance. The task utility function does not
require the full task information, but rather a specification of the utility of
the task, resulting in predictors that serve a wide range of downstream tasks.
We demonstrate our approach on two use cases of common decision making tasks
and their utility functions, in the context of autonomous driving and parallel
autonomy. Experiment results show that our predictor produces accurate
predictions that improve the task performance by a large margin in both tasks
when compared to task-agnostic baselines on the Waymo Open Motion dataset. | [
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] |
Due to the proliferation of renewable energy and its intrinsic intermittency
and stochasticity, current power systems face severe operational challenges.
Data-driven decision-making algorithms from reinforcement learning (RL) offer a
solution towards efficiently operating a clean energy system. Although RL
algorithms achieve promising performance compared to model-based control
models, there has been limited investigation of RL robustness in
safety-critical physical systems. In this work, we first show that several
competition-winning, state-of-the-art RL agents proposed for power system
control are vulnerable to adversarial attacks. Specifically, we use an
adversary Markov Decision Process to learn an attack policy, and demonstrate
the potency of our attack by successfully attacking multiple winning agents
from the Learning To Run a Power Network (L2RPN) challenge, under both
white-box and black-box attack settings. We then propose to use adversarial
training to increase the robustness of RL agent against attacks and avoid
infeasible operational decisions. To the best of our knowledge, our work is the
first to highlight the fragility of grid control RL algorithms, and contribute
an effective defense scheme towards improving their robustness and security. | [
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There is a growing interest in integrating machine learning techniques and
optimization to solve challenging optimization problems. In this work, we
propose a deep reinforcement learning methodology for the job shop scheduling
problem (JSSP). The aim is to build up a greedy-like heuristic able to learn on
some distribution of JSSP instances, different in the number of jobs and
machines. The need for fast scheduling methods is well known, and it arises in
many areas, from transportation to healthcare. We model the JSSP as a Markov
Decision Process and then we exploit the efficacy of reinforcement learning to
solve the problem. We adopt an actor-critic scheme, where the action taken by
the agent is influenced by policy considerations on the state-value function.
The procedures are adapted to take into account the challenging nature of JSSP,
where the state and the action space change not only for every instance but
also after each decision. To tackle the variability in the number of jobs and
operations in the input, we modeled the agent using two incident LSTM models, a
special type of deep neural network. Experiments show the algorithm reaches
good solutions in a short time, proving that is possible to generate new greedy
heuristics just from learning-based methodologies. Benchmarks have been
generated in comparison with the commercial solver CPLEX. As expected, the
model can generalize, to some extent, to larger problems or instances
originated by a different distribution from the one used in training. | [
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] |
In recent years, transformer models have revolutionized Natural Language
Processing (NLP) and shown promising performance on Computer Vision (CV) tasks.
Despite their effectiveness, transformers' attention operations are hard to
accelerate due to the complicated data movement and quadratic computational
complexity, prohibiting the real-time inference on resource-constrained
edge-computing platforms.
To tackle this challenge, we propose Energon, an algorithm-architecture
co-design approach that accelerates various transformers using dynamic sparse
attention. With the observation that attention results only depend on a few
important query-key pairs, we propose a Mix-Precision Multi-Round Filtering
(MP-MRF) algorithm to dynamically identify such pairs at runtime. We adopt low
bitwidth in each filtering round and only use high-precision tensors in the
attention stage to reduce overall complexity. By this means, we significantly
mitigate the computational cost with negligible accuracy loss. To enable such
an algorithm with lower latency and better energy efficiency, we also propose
an Energon co-processor architecture. Elaborated pipelines and specialized
optimizations jointly boost the performance and reduce power consumption.
Extensive experiments on both NLP and CV benchmarks demonstrate that Energon
achieves $168\times$ and $8.7\times$ geo-mean speedup and up to $10^4\times$
and $10^3\times$ energy reduction compared with Intel Xeon 5220 CPU and NVIDIA
V100 GPU. Compared to state-of-the-art attention accelerators SpAtten and
$A^3$, Energon also achieves $1.7\times, 1.25\times$ speedup and $1.6 \times,
1.5\times $ higher energy efficiency. | [
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] |
Single-cell sequencing has a significant role to explore biological processes
such as embryonic development, cancer evolution, and cell differentiation.
These biological properties can be presented by a two-dimensional scatter plot.
However, single-cell sequencing data generally has very high dimensionality.
Therefore, dimensionality reduction should be used to process the high
dimensional sequencing data for 2D visualization and subsequent biological
analysis. The traditional dimensionality reduction methods, which do not
consider the structure characteristics of single-cell sequencing data, are
difficult to reveal the data structure in the 2D representation. In this paper,
we develop a 2D feature representation method based on graph convolutional
networks (GCN) for the visualization of single-cell data, termed single-cell
sequencing graph embedding networks (SGEN). This method constructs the graph by
the similarity relationship between cells and adopts GCN to analyze the
neighbor embedding information of samples, which makes the similar cell closer
to each other on the 2D scatter plot. The results show SGEN achieves obvious 2D
distribution and preserves the high-dimensional relationship of different
cells. Meanwhile, similar cell clusters have spatial continuity rather than
relying heavily on random initialization, which can reflect the trajectory of
cell development in this scatter plot. | [
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Automating arrhythmia detection from ECG requires a robust and trusted system
that retains high accuracy under electrical disturbances. Many machine learning
approaches have reached human-level performance in classifying arrhythmia from
ECGs. However, these architectures are vulnerable to adversarial attacks, which
can misclassify ECG signals by decreasing the model's accuracy. Adversarial
attacks are small crafted perturbations injected in the original data which
manifest the out-of-distribution shifts in signal to misclassify the correct
class. Thus, security concerns arise for false hospitalization and insurance
fraud abusing these perturbations. To mitigate this problem, we introduce the
first novel Conditional Generative Adversarial Network (GAN), robust against
adversarial attacked ECG signals and retaining high accuracy. Our architecture
integrates a new class-weighted objective function for adversarial perturbation
identification and new blocks for discerning and combining out-of-distribution
shifts in signals in the learning process for accurately classifying various
arrhythmia types. Furthermore, we benchmark our architecture on six different
white and black-box attacks and compare them with other recently proposed
arrhythmia classification models on two publicly available ECG arrhythmia
datasets. The experiment confirms that our model is more robust against such
adversarial attacks for classifying arrhythmia with high accuracy. | [
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] |
Python has become a dominant programming language for emerging areas like
Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive
feature of Python is that it provides easy-to-use programming interface while
allowing library developers to enhance performance of their applications by
harnessing the computing power offered by High Performance Computing (HPC)
platforms. Efficient communication is key to scaling applications on parallel
systems, which is typically enabled by the Message Passing Interface (MPI)
standard and compliant libraries on HPC hardware. mpi4py is a Python-based
communication library that provides an MPI-like interface for Python
applications allowing application developers to utilize parallel processing
elements including GPUs. However, there is currently no benchmark suite to
evaluate communication performance of mpi4py -- and Python MPI codes in general
-- on modern HPC systems. In order to bridge this gap, we propose OMB-Py --
Python extensions to the open-source OSU Micro-Benchmark (OMB) suite -- aimed
to evaluate communication performance of MPI-based parallel applications in
Python. To the best of our knowledge, OMB-Py is the first communication
benchmark suite for parallel Python applications. OMB-Py consists of a variety
of point-to-point and collective communication benchmark tests that are
implemented for a range of popular Python libraries including NumPy, CuPy,
Numba, and PyCUDA. Our evaluation reveals that mpi4py introduces a small
overhead when compared to native MPI libraries. We plan to publicly release
OMB-Py to benefit the Python HPC community. | [
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] |
As more and more AI agents are used in practice, it is time to think about
how to make these agents fully autonomous so that they can learn by themselves
in a self-motivated and self-supervised manner rather than being retrained
periodically on the initiation of human engineers using expanded training data.
As the real-world is an open environment with unknowns or novelties, detecting
novelties or unknowns, characterizing them, accommodating or adapting to them,
gathering ground-truth training data, and incrementally learning the
unknowns/novelties are critical to making the agent more and more knowledgeable
and powerful over time. The key challenge is how to automate the process so
that it is carried out on the agent's own initiative and through its own
interactions with humans and the environment. Since an AI agent usually has a
performance task, characterizing each novelty becomes critical and necessary so
that the agent can formulate an appropriate response to adapt its behavior to
accommodate the novelty and to learn from it to improve the agent's adaptation
capability and task performance. The process goes continually without
termination. This paper proposes a theoretic framework for this learning
paradigm to promote the research of building Self-initiated Open world Learning
(SOL) agents. An example SOL agent is also described. | [
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] |
We consider the classical problem of scheduling task graphs corresponding to
complex applications on distributed computing systems. A number of heuristics
have been previously proposed to optimize task scheduling with respect to
metrics such as makespan and throughput. However, they tend to be slow to run,
particularly for larger problem instances, limiting their applicability in more
dynamic systems. Motivated by the goal of solving these problems more rapidly,
we propose, for the first time, a graph convolutional network-based scheduler
(GCNScheduler). By carefully integrating an inter-task data dependency
structure with network settings into an input graph and feeding it to an
appropriate GCN, the GCNScheduler can efficiently schedule tasks of complex
applications for a given objective. We evaluate our scheme with baselines
through simulations. We show that not only can our scheme quickly and
efficiently learn from existing scheduling schemes, but also it can easily be
applied to large-scale settings where current scheduling schemes fail to
handle. We show that it achieves better makespan than the classic HEFT
algorithm, and almost the same throughput as throughput-oriented HEFT
(TP-HEFT), while providing several orders of magnitude faster scheduling times
in both cases. For example, for makespan minimization, GCNScheduler schedules
50-node task graphs in about 4 milliseconds while HEFT takes more than 1500
seconds; and for throughput maximization, GCNScheduler schedules 100-node task
graphs in about 3.3 milliseconds, compared to about 6.9 seconds for TP-HEFT. | [
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] |
Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this
direction, various types of quantum machine learning models have been
introduced and studied extensively. Yet, our understanding of how these models
compare, both mutually and to classical models, remains limited. In this work,
we identify a constructive framework that captures all standard models based on
parametrized quantum circuits: that of linear quantum models. In particular, we
show using tools from quantum information theory how data re-uploading
circuits, an apparent outlier of this framework, can be efficiently mapped into
the simpler picture of linear models in quantum Hilbert spaces. Furthermore, we
analyze the experimentally-relevant resource requirements of these models in
terms of qubit number and amount of data needed to learn. Based on recent
results from classical machine learning, we prove that linear quantum models
must utilize exponentially more qubits than data re-uploading models in order
to solve certain learning tasks, while kernel methods additionally require
exponentially more data points. Our results provide a more comprehensive view
of quantum machine learning models as well as insights on the compatibility of
different models with NISQ constraints. | [
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] |
Driving styles summarize different driving behaviors that reflect in the
movements of the vehicles. These behaviors may indicate a tendency to perform
riskier maneuvers, consume more fuel or energy, break traffic rules, or drive
carefully. Therefore, this paper presents a driving style recognition using
Interval Type-2 Fuzzy Inference System with Multiple Experts Decision-Making
for classifying drivers into calm, moderate and aggressive. This system
receives as input features longitudinal and lateral kinematic parameters of the
vehicle motion. The type-2 fuzzy sets are more robust than type-1 fuzzy sets
when handling noisy data, because their membership function are also fuzzy
sets. In addition, a multiple experts approach can reduce the bias and
imprecision while building the fuzzy rulebase, which stores the knowledge of
the fuzzy system. The proposed approach was evaluated using descriptive
statistics analysis, and compared with clustering algorithms and a type-1 fuzzy
inference system. The results show the tendency to associate lower kinematic
profiles for the driving styles classified with the type-2 fuzzy inference
system when compared to other algorithms, which is in line with the more
conservative approach adopted in the aggregation of the experts' opinions. | [
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] |
Current maritime communications mainly rely on satellites having meager
transmission resources, hence suffering from poorer performance than modern
terrestrial wireless networks. With the growth of transcontinental air traffic,
the promising concept of aeronautical ad hoc networking relying on commercial
passenger airplanes is potentially capable of enhancing satellite-based
maritime communications via air-to-ground and multi-hop air-to-air links. In
this article, we conceive space-air-ground integrated networks (SAGINs) for
supporting ubiquitous maritime communications, where the low-earth-orbit
satellite constellations, passenger airplanes, terrestrial base stations,
ships, respectively, serve as the space-, air-, ground- and sea-layer. To meet
heterogeneous service requirements, and accommodate the time-varying and
self-organizing nature of SAGINs, we propose a deep learning (DL) aided
multi-objective routing algorithm, which exploits the quasi-predictable network
topology and operates in a distributed manner. Our simulation results based on
real satellite, flight, and shipping data in the North Atlantic region show
that the integrated network enhances the coverage quality by reducing the
end-to-end (E2E) delay and by boosting the E2E throughput as well as improving
the path-lifetime. The results demonstrate that our DL-aided multi-objective
routing algorithm is capable of achieving near Pareto-optimal performance. | [
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] |
Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging
due to their high-dynamic topology. In this paper, we invoke deep learning (DL)
to assist routing in AANETs. We set out from the single objective of minimizing
the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is
conceived for mapping the local geographic information observed by the
forwarding node into the information required for determining the optimal next
hop. The DNN is trained by exploiting the regular mobility pattern of
commercial passenger airplanes from historical flight data. After training, the
DNN is stored by each airplane for assisting their routing decisions during
flight relying solely on local geographic information. Furthermore, we extend
the DL-aided routing algorithm to a multi-objective scenario, where we aim for
simultaneously minimizing the delay, maximizing the path capacity, and
maximizing the path lifetime. Our simulation results based on real flight data
show that the proposed DL-aided routing outperforms existing position-based
routing protocols in terms of its E2E delay, path capacity as well as path
lifetime, and it is capable of approaching the Pareto front that is obtained
using global link information. | [
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] |
Background: Deep learning models have shown great success in automating tasks
in sleep medicine by learning from carefully annotated Electroencephalogram
(EEG) data. However, effectively utilizing a large amount of raw EEG remains a
challenge.
Objective: In this paper, we aim to learn robust vector representations from
massive unlabeled EEG signals, such that the learned vectorized features (1)
are expressive enough to replace the raw signals in the sleep staging task; and
(2) provide better predictive performance than supervised models in scenarios
of fewer labels and noisy samples.
Methods: We propose a self-supervised model, named Contrast with the World
Representation (ContraWR), for EEG signal representation learning, which uses
global statistics from the dataset to distinguish signals associated with
different sleep stages. The ContraWR model is evaluated on three real-world EEG
datasets that include both at-home and in-lab EEG recording settings.
Results: ContraWR outperforms 4 recent self-supervised learning methods on
the sleep staging task across 3 large EEG datasets. ContraWR also beats
supervised learning when fewer training labels are available (e.g., 4% accuracy
improvement when less than 2% data is labeled). Moreover, the model provides
informative representative feature structures in 2D projection.
Conclusions: We show that ContraWR is robust to noise and can provide
high-quality EEG representations for downstream prediction tasks. The proposed
model can be generalized to other unsupervised physiological signal learning
tasks. Future directions include exploring task-specific data augmentations and
combining self-supervised with supervised methods, building upon the initial
success of self-supervised learning in this paper. | [
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In recent decades, biomedical signals have been used for communication in
Human-Computer Interfaces (HCI) for medical applications; an instance of these
signals are the myoelectric signals (MES), which are generated in the muscles
of the human body as unidimensional patterns. Because of this, the methods and
algorithms developed for pattern recognition in signals can be applied for
their analyses once these signals have been sampled and turned into
electromyographic (EMG) signals. Additionally, in recent years, many
researchers have dedicated their efforts to studying prosthetic control
utilizing EMG signal classification, that is, by logging a set of MES in a
proper range of frequencies to classify the corresponding EMG signals. The
feature classification can be carried out on the time domain or by using other
domains such as the frequency domain (also known as the spectral domain), time
scale, and time-frequency, amongst others. One of the main methods used for
pattern recognition in myoelectric signals is the Support Vector Machines (SVM)
technique whose primary function is to identify an n-dimensional hyperplane to
separate a set of input feature points into different classes. This technique
has the potential to recognize complex patterns and on several occasions, it
has proven its worth when compared to other classifiers such as Artificial
Neural Network (ANN), Linear Discriminant Analysis (LDA), and Principal
Component Analysis(PCA). The key concepts underlying the SVM are (a) the
hyperplane separator; (b) the kernel function; (c) the optimal separation
hyperplane; and (d) a soft margin (hyperplane tolerance). | [
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] |
For Bayesian optimization (BO) on high-dimensional data with complex
structure, neural network-based kernels for Gaussian processes (GPs) have been
used to learn flexible surrogate functions by the high representation power of
deep learning. However, existing methods train neural networks by maximizing
the marginal likelihood, which do not directly improve the BO performance. In
this paper, we propose a meta-learning method for BO with neural network-based
kernels that minimizes the expected gap between the true optimum value and the
best value found by BO. We model a policy, which takes the current evaluated
data points as input and outputs the next data point to be evaluated, by a
neural network, where neural network-based kernels, GPs, and mutual
information-based acquisition functions are used as its layers. With our model,
the neural network-based kernel is trained to be appropriate for the
acquisition function by backpropagating the gap through the acquisition
function and GP. Our model is trained by a reinforcement learning framework
from multiple tasks. Since the neural network is shared across different tasks,
we can gather knowledge on BO from multiple training tasks, and use the
knowledge for unseen test tasks. In experiments using three text document
datasets, we demonstrate that the proposed method achieves better BO
performance than the existing methods. | [
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] |
Although expected utility theory has proven a fruitful and elegant theory in
the finite realm, attempts to generalize it to infinite values have resulted in
many paradoxes. In this paper, we argue that the use of John Conway's surreal
numbers shall provide a firm mathematical foundation for transfinite decision
theory. To that end, we prove a surreal representation theorem and show that
our surreal decision theory respects dominance reasoning even in the case of
infinite values. We then bring our theory to bear on one of the more venerable
decision problems in the literature: Pascal's Wager. Analyzing the wager
showcases our theory's virtues and advantages. To that end, we analyze two
objections against the wager: Mixed Strategies and Many Gods. After formulating
the two objections in the framework of surreal utilities and probabilities, our
theory correctly predicts that (1) the pure Pascalian strategy beats all mixed
strategies, and (2) what one should do in a Pascalian decision problem depends
on what one's credence function is like. Our analysis therefore suggests that
although Pascal's Wager is mathematically coherent, it does not deliver what it
purports to, a rationally compelling argument that people should lead a
religious life regardless of how confident they are in theism and its
alternatives. | [
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] |
We propose HASHTAG, the first framework that enables high-accuracy detection
of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds
on detection performance. Recent literature in fault-injection attacks shows
the severe DNN accuracy degradation caused by bit flips. In this scenario, the
attacker changes a few weight bits during DNN execution by tampering with the
program's DRAM memory. To detect runtime bit flips, HASHTAG extracts a unique
signature from the benign DNN prior to deployment. The signature is later used
to validate the integrity of the DNN and verify the inference output on the
fly. We propose a novel sensitivity analysis scheme that accurately identifies
the most vulnerable DNN layers to the fault-injection attack. The DNN signature
is then constructed by encoding the underlying weights in the vulnerable layers
using a low-collision hash function. When the DNN is deployed, new hashes are
extracted from the target layers during inference and compared against the
ground-truth signatures. HASHTAG incorporates a lightweight methodology that
ensures a low-overhead and real-time fault detection on embedded platforms.
Extensive evaluations with the state-of-the-art bit-flip attack on various DNNs
demonstrate the competitive advantage of HASHTAG in terms of both attack
detection and execution overhead. | [
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] |
Graph neural networks, a popular class of models effective in a wide range of
graph-based learning tasks, have been shown to be vulnerable to adversarial
attacks. While the majority of the literature focuses on such vulnerability in
node-level classification tasks, little effort has been dedicated to analysing
adversarial attacks on graph-level classification, an important problem with
numerous real-life applications such as biochemistry and social network
analysis. The few existing methods often require unrealistic setups, such as
access to internal information of the victim models, or an impractically-large
number of queries. We present a novel Bayesian optimisation-based attack method
for graph classification models. Our method is black-box, query-efficient and
parsimonious with respect to the perturbation applied. We empirically validate
the effectiveness and flexibility of the proposed method on a wide range of
graph classification tasks involving varying graph properties, constraints and
modes of attack. Finally, we analyse common interpretable patterns behind the
adversarial samples produced, which may shed further light on the adversarial
robustness of graph classification models. | [
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] |
The reconstruction of missing information in epidemic spreading on contact
networks can be essential in the prevention and containment strategies. The
identification and warning of infectious but asymptomatic individuals (i.e.,
contact tracing), the well-known patient-zero problem, or the inference of the
infectivity values in structured populations are examples of significant
epidemic inference problems. As the number of possible epidemic cascades grows
exponentially with the number of individuals involved and only an almost
negligible subset of them is compatible with the observations (e.g., medical
tests), epidemic inference in contact networks poses incredible computational
challenges. We present a new generative neural networks framework that learns
to generate the most probable infection cascades compatible with observations.
The proposed method achieves better (in some cases, significantly better) or
comparable results with existing methods in all problems considered both in
synthetic and real contact networks. Given its generality, clear Bayesian and
variational nature, the presented framework paves the way to solve fundamental
inference epidemic problems with high precision in small and medium-sized real
case scenarios such as the spread of infections in workplaces and hospitals. | [
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] |
A good data visualization is not only a distortion-free graphical
representation of data but also a way to reveal underlying statistical
properties of the data. Despite its common use across various stages of data
analysis, selecting a good visualization often is a manual process involving
many iterations. Recently there has been interest in reducing this effort by
developing models that can recommend visualizations, but they are of limited
use since they require large training samples (data and visualization pairs)
and focus primarily on the design aspects rather than on assessing the
effectiveness of the selected visualization.
In this paper, we present VizAI, a generative-discriminative framework that
first generates various statistical properties of the data from a number of
alternative visualizations of the data. It is linked to a discriminative model
that selects the visualization that best matches the true statistics of the
data being visualized. VizAI can easily be trained with minimal supervision and
adapts to settings with varying degrees of supervision easily. Using
crowd-sourced judgements and a large repository of publicly available
visualizations, we demonstrate that VizAI outperforms the state of the art
methods that learn to recommend visualizations. | [
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] |
Variational quantum circuits are used in quantum machine learning and
variational quantum simulation tasks. Designing good variational circuits or
predicting how well they perform for given learning or optimization tasks is
still unclear. Here we discuss these problems, analyzing variational quantum
circuits using the theory of neural tangent kernels. We define quantum neural
tangent kernels, and derive dynamical equations for their associated loss
function in optimization and learning tasks. We analytically solve the dynamics
in the frozen limit, or lazy training regime, where variational angles change
slowly and a linear perturbation is good enough. We extend the analysis to a
dynamical setting, including quadratic corrections in the variational angles.
We then consider hybrid quantum-classical architecture and define a large-width
limit for hybrid kernels, showing that a hybrid quantum-classical neural
network can be approximately Gaussian. The results presented here show limits
for which analytical understandings of the training dynamics for variational
quantum circuits, used for quantum machine learning and optimization problems,
are possible. These analytical results are supported by numerical simulations
of quantum machine learning experiments. | [
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] |
Benefiting from expanding cloud infrastructure, deep neural networks (DNNs)
today have increasingly high performance when trained in the cloud. Researchers
spend months of effort competing for an extra few percentage points of model
accuracy. However, when these models are actually deployed on edge devices in
practice, very often, the performance can abruptly drop over 10% without
obvious reasons. The key challenge is that there is not much visibility into ML
inference execution on edge devices, and very little awareness of potential
issues during the edge deployment process. We present ML-EXray, an end-to-end
framework, which provides visibility into layer-level details of the ML
execution, and helps developers analyze and debug cloud-to-edge deployment
issues. More often than not, the reason for sub-optimal edge performance does
not only lie in the model itself, but every operation throughout the data flow
and the deployment process. Evaluations show that ML-EXray can effectively
catch deployment issues, such as pre-processing bugs, quantization issues,
suboptimal kernels, etc. Using ML-EXray, users need to write less than 15 lines
of code to fully examine the edge deployment pipeline. Eradicating these
issues, ML-EXray can correct model performance by up to 30%, pinpoint
error-prone layers, and guide users to optimize kernel execution latency by two
orders of magnitude. Code and APIs will be released as an open-source
multi-lingual instrumentation library and a Python deployment validation
library. | [
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AI methods have been proven to yield impressive performance on Android
malware detection. However, most AI-based methods make predictions of
suspicious samples in a black-box manner without transparency on models'
inference. The expectation on models' explainability and transparency by cyber
security and AI practitioners to assure the trustworthiness increases. In this
article, we present a novel model-agnostic explanation method for AI models
applied for Android malware detection. Our proposed method identifies and
quantifies the data features relevance to the predictions by two steps: i) data
perturbation that generates the synthetic data by manipulating features'
values; and ii) optimization of features attribution values to seek significant
changes of prediction scores on the perturbed data with minimal feature values
changes. The proposed method is validated by three experiments. We firstly
demonstrate that our proposed model explanation method can aid in discovering
how AI models are evaded by adversarial samples quantitatively. In the
following experiments, we compare the explainability and fidelity of our
proposed method with state-of-the-arts, respectively. | [
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] |
Individual device loads and energy consumption feedback is one of the
important approaches for pursuing users to save energy in residences. This can
help in identifying faulty devices and wasted energy by devices when left On
unused. The main challenge is to identity and estimate the energy consumption
of individual devices without intrusive sensors on each device. Non-intrusive
load monitoring (NILM) or energy disaggregation, is a blind source separation
problem which requires a system to estimate the electricity usage of individual
appliances from the aggregated household energy consumption. In this paper, we
propose a novel deep neural network-based approach for performing load
disaggregation on low frequency power data obtained from residential
households. We combine a series of one-dimensional Convolutional Neural
Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can
identify active appliances and retrieve their power consumption given the
aggregated household power value. We used CNNs to extract features from main
readings in a given time frame and then used those features to classify if a
given appliance is active at that time period or not. Following that, the
extracted features are used to model a generation problem using LSTM. We train
the LSTM to generate the disaggregated energy consumption of a particular
appliance. Our neural network is capable of generating detailed feedback of
demand-side, providing vital insights to the end-user about their electricity
consumption. The algorithm was designed for low power offline devices such as
ESP32. Empirical calculations show that our model outperforms the
state-of-the-art on the Reference Energy Disaggregation Dataset (REDD). | [
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] |
When robots operate in human environments, it's critical that humans can
quickly teach them new concepts: object-centric properties of the environment
that they care about (e.g. objects near, upright, etc). However, teaching a new
perceptual concept from high-dimensional robot sensor data (e.g. point clouds)
is demanding, requiring an unrealistic amount of human labels. To address this,
we propose a framework called Perceptual Concept Bootstrapping (PCB). First, we
leverage the inherently lower-dimensional privileged information, e.g., object
poses and bounding boxes, available from a simulator only at training time to
rapidly learn a low-dimensional, geometric concept from minimal human input.
Second, we treat this low-dimensional concept as an automatic labeler to
synthesize a large-scale high-dimensional data set with the simulator. With
these two key ideas, PCB alleviates human label burden while still learning
perceptual concepts that work with real sensor input where no privileged
information is available. We evaluate PCB for learning spatial concepts that
describe object state or multi-object relationships, and show it achieves
superior performance compared to baseline methods. We also demonstrate the
utility of the learned concepts in motion planning tasks on a 7-DoF Franka
Panda robot. | [
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] |
The existing methods to calculate the Eigenvector Centrality(EC) tend to not
be robust enough for determination of EC in low time complexity or not
well-scalable for large networks, hence rendering them practically unreliable/
computationally expensive. So, it is of the essence to develop a method that is
scalable in low computational time. Hence, we propose a deep learning model for
the identification of nodes with high Eigenvector Centrality. There have been a
few previous works in identifying the high ranked nodes with supervised
learning methods, but in real-world cases, the graphs are not labelled and
hence deployment of supervised learning methods becomes a hazard and its usage
becomes impractical. So, we devise CUL(Centrality with Unsupervised Learning)
method to learn the relative EC scores in a network in an unsupervised manner.
To achieve this, we develop an Encoder-Decoder based framework that maps the
nodes to their respective estimated EC scores. Extensive experiments were
conducted on different synthetic and real-world networks. We compared CUL
against a baseline supervised method for EC estimation similar to some of the
past works. It was observed that even with training on a minuscule number of
training datasets, CUL delivers a relatively better accuracy score when
identifying the higher ranked nodes than its supervised counterpart. We also
show that CUL is much faster and has a smaller runtime than the conventional
baseline method for EC computation. The code is available at
https://github.com/codexhammer/CUL. | [
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] |
Exploration is one of the most important tasks in Reinforcement Learning, but
it is not well-defined beyond finite problems in the Dynamic Programming
paradigm (see Subsection 2.4). We provide a reinterpretation of exploration
which can be applied to any online learning method. We come to this definition
by approaching exploration from a new direction. After finding that concepts of
exploration created to solve simple Markov decision processes with Dynamic
Programming are no longer broadly applicable, we reexamine exploration. Instead
of extending the ends of dynamic exploration procedures, we extend their means.
That is, rather than repeatedly sampling every state-action pair possible in a
process, we define the act of modifying an agent to itself be explorative. The
resulting definition of exploration can be applied in infinite problems and
non-dynamic learning methods, which the dynamic notion of exploration cannot
tolerate. To understand the way that modifications of an agent affect learning,
we describe a novel structure on the set of agents: a collection of distances
(see footnote 7) $d_{a} \in A$, which represent the perspectives of each agent
possible in the process. Using these distances, we define a topology and show
that many important structures in Reinforcement Learning are well behaved under
the topology induced by convergence in the agent space. | [
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] |
Machine learning (ML) is increasingly often used to inform high-stakes
decisions. As complex ML models (e.g., deep neural networks) are often
considered black boxes, a wealth of procedures has been developed to shed light
on their inner workings and the ways in which their predictions come about,
defining the field of 'explainable AI' (XAI). Saliency methods rank input
features according to some measure of 'importance'. Such methods are difficult
to validate since a formal definition of feature importance is, thus far,
lacking. It has been demonstrated that some saliency methods can highlight
features that have no statistical association with the prediction target
(suppressor variables). To avoid misinterpretations due to such behavior, we
propose the actual presence of such an association as a necessary condition and
objective preliminary definition for feature importance. We carefully crafted a
ground-truth dataset in which all statistical dependencies are well-defined and
linear, serving as a benchmark to study the problem of suppressor variables. We
evaluate common explanation methods including LRP, DTD, PatternNet,
PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with
respect to our objective definition. We show that most of these methods are
unable to distinguish important features from suppressors in this setting. | [
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] |
As \emph{artificial intelligence} (AI) systems are increasingly involved in
decisions affecting our lives, ensuring that automated decision-making is fair
and ethical has become a top priority. Intuitively, we feel that akin to human
decisions, judgments of artificial agents should necessarily be grounded in
some moral principles. Yet a decision-maker (whether human or artificial) can
only make truly ethical (based on any ethical theory) and fair (according to
any notion of fairness) decisions if full information on all the relevant
factors on which the decision is based are available at the time of
decision-making. This raises two problems: (1) In settings, where we rely on AI
systems that are using classifiers obtained with supervised learning, some
induction/generalization is present and some relevant attributes may not be
present even during learning. (2) Modeling such decisions as games reveals that
any -- however ethical -- pure strategy is inevitably susceptible to
exploitation.
Moreover, in many games, a Nash Equilibrium can only be obtained by using
mixed strategies, i.e., to achieve mathematically optimal outcomes, decisions
must be randomized. In this paper, we argue that in supervised learning
settings, there exist random classifiers that perform at least as well as
deterministic classifiers, and may hence be the optimal choice in many
circumstances. We support our theoretical results with an empirical study
indicating a positive societal attitude towards randomized artificial
decision-makers, and discuss some policy and implementation issues related to
the use of random classifiers that relate to and are relevant for current AI
policy and standardization initiatives. | [
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] |
While machine learning (ML) has made tremendous progress during the past
decade, recent research has shown that ML models are vulnerable to various
security and privacy attacks. So far, most of the attacks in this field focus
on discriminative models, represented by classifiers. Meanwhile, little
attention has been paid to the security and privacy risks of generative models,
such as generative adversarial networks (GANs). In this paper, we propose the
first set of training dataset property inference attacks against GANs.
Concretely, the adversary aims to infer the macro-level training dataset
property, i.e., the proportion of samples used to train a target GAN with
respect to a certain attribute. A successful property inference attack can
allow the adversary to gain extra knowledge of the target GAN's training
dataset, thereby directly violating the intellectual property of the target
model owner. Also, it can be used as a fairness auditor to check whether the
target GAN is trained with a biased dataset. Besides, property inference can
serve as a building block for other advanced attacks, such as membership
inference. We propose a general attack pipeline that can be tailored to two
attack scenarios, including the full black-box setting and partial black-box
setting. For the latter, we introduce a novel optimization framework to
increase the attack efficacy. Extensive experiments over four representative
GAN models on five property inference tasks show that our attacks achieve
strong performance. In addition, we show that our attacks can be used to
enhance the performance of membership inference against GANs. | [
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] |
Allocating physical layer resources to users based on channel quality, buffer
size, requirements and constraints represents one of the central optimization
problems in the management of radio resources. The solution space grows
combinatorially with the cardinality of each dimension making it hard to find
optimal solutions using an exhaustive search or even classical optimization
algorithms given the stringent time requirements. This problem is even more
pronounced in MU-MIMO scheduling where the scheduler can assign multiple users
to the same time-frequency physical resources. Traditional approaches thus
resort to designing heuristics that trade optimality in favor of feasibility of
execution. In this work we treat the MU-MIMO scheduling problem as a
tree-structured combinatorial problem and, borrowing from the recent successes
of AlphaGo Zero, we investigate the feasibility of searching for the best
performing solutions using a combination of Monte Carlo Tree Search and
Reinforcement Learning. To cater to the nature of the problem at hand, like the
lack of an intrinsic ordering of the users as well as the importance of
dependencies between combinations of users, we make fundamental modifications
to the neural network architecture by introducing the self-attention mechanism.
We then demonstrate that the resulting approach is not only feasible but vastly
outperforms state-of-the-art heuristic-based scheduling approaches in the
presence of measurement uncertainties and finite buffers. | [
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] |
Objectives-Geriatric clinical care is a multidisciplinary assessment designed
to evaluate older patients (age 65 years and above) functional ability,
physical health, and cognitive wellbeing. The majority of these patients suffer
from multiple chronic conditions and require special attention. Recently,
hospitals utilize various artificial intelligence (AI) systems to improve care
for elderly patients. The purpose of this systematic literature review is to
understand the current use of AI systems, particularly machine learning (ML),
in geriatric clinical care for chronic diseases. Materials and Methods-We
restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE,
ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research
articles published in English between January 2010 and June 2019. We focused on
studies that used ML algorithms in the care of geriatrics patients with chronic
conditions. Results-We identified 35 eligible studies and classified in three
groups-psychological disorder (n=22), eye diseases (n=6), and others (n=7).
This review identified the lack of standardized ML evaluation metrics and the
need for data governance specific to health care applications. Conclusion- More
studies and ML standardization tailored to health care applications are
required to confirm whether ML could aid in improving geriatric clinical care. | [
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] |
In this study, we present a meta-learning model to adapt the predictions of
the network's capacity between viewers who participate in a live video
streaming event. We propose the MELANIE model, where an event is formulated as
a Markov Decision Process, performing meta-learning on reinforcement learning
tasks. By considering a new event as a task, we design an actor-critic learning
scheme to compute the optimal policy on estimating the viewers' high-bandwidth
connections. To ensure fast adaptation to new connections or changes among
viewers during an event, we implement a prioritized replay memory buffer based
on the Kullback-Leibler divergence of the reward/throughput of the viewers'
connections. Moreover, we adopt a model-agnostic meta-learning framework to
generate a global model from past events. As viewers scarcely participate in
several events, the challenge resides on how to account for the low structural
similarity of different events. To combat this issue, we design a graph
signature buffer to calculate the structural similarities of several streaming
events and adjust the training of the global model accordingly. We evaluate the
proposed model on the link weight prediction task on three real-world datasets
of live video streaming events. Our experiments demonstrate the effectiveness
of our proposed model, with an average relative gain of 25% against
state-of-the-art strategies. For reproduction purposes, our evaluation datasets
and implementation are publicly available at
https://github.com/stefanosantaris/melanie | [
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Internet users have been exposing an increasing amount of Personally
Identifiable Information (PII) on social media. Such exposed PII can cause
severe losses to the users, and informing users of their PII exposure is
crucial to raise their privacy awareness and encourage them to take protective
measures. To this end, advanced automatic techniques are needed. While
Information Extraction (IE) techniques can be used to extract the PII
automatically, Deep Learning (DL)-based IE models alleviate the need for
feature engineering and further improve the efficiency. However, DL-based IE
models often require large-scale labeled data for training, but PII-labeled
social media posts are difficult to obtain due to privacy concerns. Also, these
models rely heavily on pre-trained word embeddings, while PII in social media
often varies in forms and thus has no fixed representations in pre-trained word
embeddings. In this study, we propose the Deep Transfer Learning for PII
Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE
transfers knowledge learned from publicly available PII data to social media to
address the problem of rare PII-labeled data. Moreover, our framework leverages
Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide
PIIE without relying on pre-trained word embeddings. Evaluation against
benchmark IE models indicates that our approach outperforms state-of-the-art
DL-based IE models. Our framework can facilitate various applications, such as
PII misuse prediction and privacy risk assessment, protecting the privacy of
internet users. | [
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] |
The aviation industry as well as the industries that benefit and are linked
to it are ripe for innovation in the form of Big Data analytics. The number of
available big data technologies is constantly growing, while at the same time
the existing ones are rapidly evolving and empowered with new features.
However, the Big Data era imposes the crucial challenge of how to effectively
handle information security while managing massive and rapidly evolving data
from heterogeneous data sources. While multiple technologies have emerged,
there is a need to find a balance between multiple security requirements,
privacy obligations, system performance and rapid dynamic analysis on large
datasets. The current paper aims to introduce the ICARUS Secure Experimentation
Sandbox of the ICARUS platform. The ICARUS platform aims to provide a big
data-enabled platform that aspires to become an 'one-stop shop' for aviation
data and intelligence marketplace that provides a trusted and secure
'sandboxed' analytics workspace, allowing the exploration, integration and deep
analysis of original and derivative data in a trusted and fair manner. Towards
this end, a Secure Experimentation Sandbox has been designed and integrated in
the ICARUS platform offering, that enables the provisioning of a sophisticated
environment that can completely guarantee the safety and confidentiality of
data, allowing to any interested party to utilise the platform to conduct
analytical experiments in closed-lab conditions. | [
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] |
The unprecedented volume, diversity and richness of aviation data that can be
acquired, generated, stored, and managed provides unique capabilities for the
aviation-related industries and pertains value that remains to be unlocked with
the adoption of the innovative Big Data Analytics technologies. Despite the
large efforts and investments on research and innovation, the Big Data
technologies introduce a number of challenges to its adopters. Besides the
effective storage and access to the underlying big data, efficient data
integration and data interoperability should be considered, while at the same
time multiple data sources should be effectively combined by performing data
exchange and data sharing between the different stakeholders. However, this
reveals additional challenges for the crucial preservation of the information
security of the collected data, the trusted and secure data exchange and data
sharing, as well as the robust data access control. The current paper aims to
introduce the ICARUS big data-enabled platform that aims provide a multi-sided
platform that offers a novel aviation data and intelligence marketplace
accompanied by a trusted and secure analytics workspace. It holistically
handles the complete big data lifecycle from the data collection, data curation
and data exploration to the data integration and data analysis of data
originating from heterogeneous data sources with different velocity, variety
and volume in a trusted and secure manner. | [
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] |
Predicting a structure of an antibody from its sequence is important since it
allows for a better design process of synthetic antibodies that play a vital
role in the health industry. Most of the structure of an antibody is
conservative. The most variable and hard-to-predict part is the third
complementarity-determining region of the antibody heavy chain (CDR H3).
Lately, deep learning has been employed to solve the task of CDR H3 prediction.
However, current state-of-the-art methods are not end-to-end, but rather they
output inter-residue distances and orientations to the RosettaAntibody package
that uses this additional information alongside statistical and physics-based
methods to predict the 3D structure. This does not allow a fast screening
process and, therefore, inhibits the development of targeted synthetic
antibodies. In this work, we present an end-to-end model to predict CDR H3 loop
structure, that performs on par with state-of-the-art methods in terms of
accuracy but an order of magnitude faster. We also raise an issue with a
commonly used RosettaAntibody benchmark that leads to data leaks, i.e., the
presence of identical sequences in the train and test datasets. | [
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] |
Offline reinforcement learning (RL) provides a framework for learning
decision-making from offline data and therefore constitutes a promising
approach for real-world applications as automated driving. Self-driving
vehicles (SDV) learn a policy, which potentially even outperforms the behavior
in the sub-optimal data set. Especially in safety-critical applications as
automated driving, explainability and transferability are key to success. This
motivates the use of model-based offline RL approaches, which leverage
planning. However, current state-of-the-art methods often neglect the influence
of aleatoric uncertainty arising from the stochastic behavior of multi-agent
systems. This work proposes a novel approach for Uncertainty-aware Model-Based
Offline REinforcement Learning Leveraging plAnning (UMBRELLA), which solves the
prediction, planning, and control problem of the SDV jointly in an
interpretable learning-based fashion. A trained action-conditioned stochastic
dynamics model captures distinctively different future evolutions of the
traffic scene. The analysis provides empirical evidence for the effectiveness
of our approach in challenging automated driving simulations and based on a
real-world public dataset. | [
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] |
Thanks to rapid development of artificial intelligence technology in recent
years, the current artificial intelligence technology is contributing to many
part of society. Education, environment, medical care, military, tourism,
economy, politics, etc. are having a very large impact on society as a whole.
For example, in the field of education, there is an artificial intelligence
tutoring system that automatically assigns tutors based on student's level. In
the field of economics, there are quantitative investment methods that
automatically analyze large amounts of data to find investment laws to create
investment models or predict changes in financial markets. As such, artificial
intelligence technology is being used in various fields. So, it is very
important to know exactly what factors have an important influence on each
field of artificial intelligence technology and how the relationship between
each field is connected. Therefore, it is necessary to analyze artificial
intelligence technology in each field. In this paper, we analyze patent
documents related to artificial intelligence technology. We propose a method
for keyword analysis within factors using artificial intelligence patent data
sets for artificial intelligence technology analysis. This is a model that
relies on feature engineering based on deep learning model named KeyBERT, and
using vector space model. A case study of collecting and analyzing artificial
intelligence patent data was conducted to show how the proposed model can be
applied to real world problems. | [
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] |
Advances in biomedicine are largely fueled by exploring uncharted territories
of human biology. Machine learning can both enable and accelerate discovery,
but faces a fundamental hurdle when applied to unseen data with distributions
that differ from previously observed ones -- a common dilemma in scientific
inquiry. We have developed a new deep learning framework, called
{\textit{Portal Learning}}, to explore dark chemical and biological space.
Three key, novel components of our approach include: (i) end-to-end, step-wise
transfer learning, in recognition of biology's sequence-structure-function
paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection.
Portal Learning provides a practical solution to the out-of-distribution (OOD)
problem in statistical machine learning. Here, we have implemented Portal
Learning to predict chemical-protein interactions on a genome-wide scale.
Systematic studies demonstrate that Portal Learning can effectively assign
ligands to unexplored gene families (unknown functions), versus existing
state-of-the-art methods, thereby allowing us to target previously
"undruggable" proteins and design novel polypharmacological agents for
disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning
is general-purpose and can be further applied to other areas of scientific
inquiry. | [
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] |
Nowadays, malware and malware incidents are increasing daily, even with
various antivirus systems and malware detection or classification
methodologies. Machine learning techniques have been the main focus of the
security experts to detect malware and determine their families. Many static,
dynamic, and hybrid techniques have been presented for that purpose. In this
study, the static analysis technique has been applied to malware samples to
extract API calls, which is one of the most used features in machine/deep
learning models as it represents the behavior of malware samples.
Since the rapid increase and continuous evolution of malware affect the
detection capacity of antivirus scanners, recent and updated datasets of
malicious software became necessary to overcome this drawback. This paper
introduces two new datasets: One with 14,616 samples obtained and compiled from
VirusShare and one with 9,795 samples from VirusSample. In addition, benchmark
results based on static API calls of malware samples are presented using
several machine and deep learning models on these datasets. We believe that
these two datasets and benchmark results enable researchers to test and
validate their methods and approaches in this field. | [
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] |
Background: Twelve lead ECGs are a core diagnostic tool for cardiovascular
diseases. Here, we describe and analyse an ensemble deep neural network
architecture to classify 24 cardiac abnormalities from 12-lead ECGs.
Method: We proposed a squeeze and excite ResNet to automatically learn deep
features from 12-lead ECGs, in order to identify 24 cardiac conditions. The
deep features were augmented with age and gender features in the final fully
connected layers. Output thresholds for each class were set using a constrained
grid search. To determine why the model made incorrect predictions, two expert
clinicians independently interpreted a random set of 100 misclassified ECGs
concerning Left Axis Deviation.
Results: Using the bespoke weighted accuracy metric, we achieved a 5-fold
cross validation score of 0.684, and sensitivity and specificity of 0.758 and
0.969, respectively. We scored 0.520 on the full test data, and ranked 2nd out
of 41 in the official challenge rankings. On a random set of misclassified
ECGs, agreement between two clinicians and training labels was poor (clinician
1: kappa = -0.057, clinician 2: kappa = -0.159). In contrast, agreement between
the clinicians was very high (kappa = 0.92).
Discussion: The proposed prediction model performed well on the validation
and hidden test data in comparison to models trained on the same data. We also
discovered considerable inconsistency in training labels, which is likely to
hinder development of more accurate models. | [
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] |
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve
workflow and enable correct diagnoses; FLL detection is the first step in such
a computer-aided diagnosis. Despite the recent success of deep-learning-based
approaches in detecting FLLs, current methods are not sufficiently robust for
assessing misaligned multiphase data. By introducing an attention-guided
multiphase alignment in feature space, this study presents a fully automated,
end-to-end learning framework for detecting FLLs from multiphase computed
tomography (CT) images. Our method is robust to misaligned multiphase images
owing to its complete learning-based approach, which reduces the sensitivity of
the model's performance to the quality of registration and enables a standalone
deployment of the model in clinical practice. Evaluation on a large-scale
dataset with 280 patients confirmed that our method outperformed previous
state-of-the-art methods and significantly reduced the performance degradation
for detecting FLLs using misaligned multiphase CT images. The robustness of the
proposed method can enhance the clinical adoption of the deep-learning-based
computer-aided detection system. | [
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] |
In the online (time-series) search problem, a player is presented with a
sequence of prices which are revealed in an online manner. In the standard
definition of the problem, for each revealed price, the player must decide
irrevocably whether to accept or reject it, without knowledge of future prices
(other than an upper and a lower bound on their extreme values), and the
objective is to minimize the competitive ratio, namely the worst-case ratio
between the maximum price in the sequence and the one selected by the player.
The problem formulates several applications of decision-making in the face of
uncertainty on the revealed samples.
Previous work on this problem has largely assumed extreme scenarios in which
either the player has almost no information about the input, or the player is
provided with some powerful, and error-free advice. In this work, we study
learning-augmented algorithms, in which there is a potentially erroneous
prediction concerning the input. Specifically, we consider two different
settings: the setting in which the prediction is related to the maximum price
in the sequence, as well as the setting in which the prediction is obtained as
a response to a number of binary queries. For both settings, we provide tight,
or near-tight upper and lower bounds on the worst-case performance of search
algorithms as a function of the prediction error. We also provide experimental
results on data obtained from stock exchange markets that confirm the
theoretical analysis, and explain how our techniques can be applicable to other
learning-augmented applications. | [
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] |
Deep learning (DL) has been widely applied to many domains. Unique challenges
in engineering DL systems are posed by the programming paradigm shift from
traditional systems to DL systems, and performance is one of the challenges.
Performance problems (PPs) in DL systems can cause severe consequences such as
excessive resource consumption and financial loss. While bugs in DL systems
have been extensively investigated, PPs in DL systems have hardly been
explored. To bridge this gap, we present the first comprehensive study to i)
characterize symptoms, root causes, and introducing and exposing stages of PPs
in DL systems developed in TensorFLow and Keras, with 224 PPs collected from
210 StackOverflow posts, and to ii) assess the capability of existing
performance analysis approaches in tackling PPs, with a constructed benchmark
of 58 PPs in DL systems. Our findings shed light on the implications on
developing high-performance DL systems, and detecting and localizing PPs in DL
systems. To demonstrate the usefulness of our findings, we develop a static
checker Deep-Perf to detect three types of PPs. It has detected 488 new PPs in
130 GitHub projects. 105 and 27 PPs have been confirmed and fixed. | [
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Finding high-quality solutions to mixed-integer linear programming problems
(MILPs) is of great importance for many practical applications. In this
respect, the refinement heuristic local branching (LB) has been proposed to
produce improving solutions and has been highly influential for the development
of local search methods in MILP. The algorithm iteratively explores a sequence
of solution neighborhoods defined by the so-called local branching constraint,
namely, a linear inequality limiting the distance from a reference solution.
For a LB algorithm, the choice of the neighborhood size is critical to
performance. In this work, we study the relation between the size of the search
neighborhood and the behavior of the underlying LB algorithm, and we devise a
leaning based framework for predicting the best size for the specific instance
to be solved. Furthermore, we have also investigated the relation between the
time limit for exploring the LB neighborhood and the actual performance of LB
scheme, and devised a strategy for adapting the time limit. We computationally
show that the neighborhood size and time limit can indeed be learned, leading
to improved performances and that the overall algorithm generalizes well both
with respect to the instance size and, remarkably, across instances. | [
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] |
Social media, professional sports, and video games are driving rapid growth
in live video streaming, on platforms such as Twitch and YouTube Live. Live
streaming experience is very susceptible to short-time-scale network congestion
since client playback buffers are often no more than a few seconds.
Unfortunately, identifying such streams and measuring their QoE for network
management is challenging, since content providers largely use the same
delivery infrastructure for live and video-on-demand (VoD) streaming, and
packet inspection techniques (including SNI/DNS query monitoring) cannot always
distinguish between the two.
In this paper, we design, build, and deploy ReCLive: a machine learning
method for live video detection and QoE measurement based on network-level
behavioral characteristics. Our contributions are four-fold: (1) We analyze
about 23,000 video streams from Twitch and YouTube, and identify key features
in their traffic profile that differentiate live and on-demand streaming. We
release our traffic traces as open data to the public; (2) We develop an
LSTM-based binary classifier model that distinguishes live from on-demand
streams in real-time with over 95% accuracy across providers; (3) We develop a
method that estimates QoE metrics of live streaming flows in terms of
resolution and buffer stall events with overall accuracies of 93% and 90%,
respectively; and (4) Finally, we prototype our solution, train it in the lab,
and deploy it in a live ISP network serving more than 7,000 subscribers. Our
method provides ISPs with fine-grained visibility into live video streams,
enabling them to measure and improve user experience. | [
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] |
Recent advances in computationally efficient non-myopic Bayesian optimization
(BO) improve query efficiency over traditional myopic methods like expected
improvement while only modestly increasing computational cost. These advances
have been largely limited, however, to unconstrained optimization. For
constrained optimization, the few existing non-myopic BO methods require heavy
computation. For instance, one existing non-myopic constrained BO method [Lam
and Willcox, 2017] relies on computationally expensive unreliable brute-force
derivative-free optimization of a Monte Carlo rollout acquisition function.
Methods that use the reparameterization trick for more efficient
derivative-based optimization of non-myopic acquisition functions in the
unconstrained setting, like sample average approximation and infinitesimal
perturbation analysis, do not extend: constraints introduce discontinuities in
the sampled acquisition function surface that hinder its optimization.
Moreover, we argue here that being non-myopic is even more important in
constrained problems because fear of violating constraints pushes myopic
methods away from sampling the boundary between feasible and infeasible
regions, slowing the discovery of optimal solutions with tight constraints. In
this paper, we propose a computationally efficient two-step lookahead
constrained Bayesian optimization acquisition function (2-OPT-C) supporting
both sequential and batch settings. To enable fast acquisition function
optimization, we develop a novel likelihood-ratio-based unbiased estimator of
the gradient of the two-step optimal acquisition function that does not use the
reparameterization trick. In numerical experiments, 2-OPT-C typically improves
query efficiency by 2x or more over previous methods, and in some cases by 10x
or more. | [
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] |
Games have a long history as benchmarks for progress in artificial
intelligence. Approaches using search and learning produced strong performance
across many perfect information games, and approaches using game-theoretic
reasoning and learning demonstrated strong performance for specific imperfect
information poker variants. We introduce Student of Games, a general-purpose
algorithm that unifies previous approaches, combining guided search, self-play
learning, and game-theoretic reasoning. Student of Games achieves strong
empirical performance in large perfect and imperfect information games -- an
important step towards truly general algorithms for arbitrary environments. We
prove that Student of Games is sound, converging to perfect play as available
computation and approximation capacity increases. Student of Games reaches
strong performance in chess and Go, beats the strongest openly available agent
in heads-up no-limit Texas hold'em poker, and defeats the state-of-the-art
agent in Scotland Yard, an imperfect information game that illustrates the
value of guided search, learning, and game-theoretic reasoning. | [
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] |
While modern policy optimization methods can do complex manipulation from
sensory data, they struggle on problems with extended time horizons and
multiple sub-goals. On the other hand, task and motion planning (TAMP) methods
scale to long horizons but they are computationally expensive and need to
precisely track world state. We propose a method that draws on the strength of
both methods: we train a policy to imitate a TAMP solver's output. This
produces a feed-forward policy that can accomplish multi-step tasks from
sensory data. First, we build an asynchronous distributed TAMP solver that can
produce supervision data fast enough for imitation learning. Then, we propose a
hierarchical policy architecture that lets us use partially trained control
policies to speed up the TAMP solver. In robotic manipulation tasks with 7-DoF
joint control, the partially trained policies reduce the time needed for
planning by a factor of up to 2.6. Among these tasks, we can learn a policy
that solves the RoboSuite 4-object pick-place task 88% of the time from object
pose observations and a policy that solves the RoboDesk 9-goal benchmark 79% of
the time from RGB images (averaged across the 9 disparate tasks). | [
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] |
Sparse events, such as malign attacks in real-time network traffic, have
caused big organisations an immense hike in revenue loss. This is due to the
excessive growth of the network and its exposure to a plethora of people. The
standard methods used to detect intrusions are not promising and have
significant failure to identify new malware. Moreover, the challenges in
handling high volume data with sparsity, high false positives, fewer detection
rates in minor class, training time and feature engineering of the
dimensionality of data has promoted deep learning to take over the task with
less time and great results. The existing system needs improvement in solving
real-time network traffic issues along with feature engineering. Our proposed
work overcomes these challenges by giving promising results using deep-stacked
autoencoders in two stages. The two-stage deep learning combines with shallow
learning using the random forest for classification in the second stage. This
made the model get well with the latest Canadian Institute for Cybersecurity -
Intrusion Detection System 2017 (CICIDS-2017) dataset. Zero false positives
with admirable detection accuracy were achieved. | [
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More and more orgainizations and institutions make efforts on using external
data to improve the performance of AI services. To address the data privacy and
security concerns, federated learning has attracted increasing attention from
both academia and industry to securely construct AI models across multiple
isolated data providers. In this paper, we studied the efficiency problem of
adapting widely used XGBoost model in real-world applications to vertical
federated learning setting. State-of-the-art vertical federated XGBoost
frameworks requires large number of encryption operations and ciphertext
transmissions, which makes the model training much less efficient than training
XGBoost models locally. To bridge this gap, we proposed a novel batch
homomorphic encryption method to cut the cost of encryption-related computation
and transmission in nearly half. This is achieved by encoding the first-order
derivative and the second-order derivative into a single number for encryption,
ciphertext transmission, and homomorphic addition operations. The sum of
multiple first-order derivatives and second-order derivatives can be
simultaneously decoded from the sum of encoded values. We are motivated by the
batch idea in the work of BatchCrypt for horizontal federated learning, and
design a novel batch method to address the limitations of allowing quite few
number of negative numbers. The encode procedure of the proposed batch method
consists of four steps, including shifting, truncating, quantizing and
batching, while the decoding procedure consists of de-quantization and shifting
back. The advantages of our method are demonstrated through theoretical
analysis and extensive numerical experiments. | [
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Automated persuasion systems (APS) aim to persuade a user to believe
something by entering into a dialogue in which arguments and counterarguments
are exchanged. To maximize the probability that an APS is successful in
persuading a user, it can identify a global policy that will allow it to select
the best arguments it presents at each stage of the dialogue whatever arguments
the user presents. However, in real applications, such as for healthcare, it is
unlikely the utility of the outcome of the dialogue will be the same, or the
exact opposite, for the APS and user. In order to deal with this situation,
games in extended form have been harnessed for argumentation in Bi-party
Decision Theory. This opens new problems that we address in this paper: (1) How
can we use Machine Learning (ML) methods to predict utility functions for
different subpopulations of users? and (2) How can we identify for a new user
the best utility function from amongst those that we have learned? To this
extent, we develop two ML methods, EAI and EDS, that leverage information
coming from the users to predict their utilities. EAI is restricted to a fixed
amount of information, whereas EDS can choose the information that best detects
the subpopulations of a user. We evaluate EAI and EDS in a simulation setting
and in a realistic case study concerning healthy eating habits. Results are
promising in both cases, but EDS is more effective at predicting useful utility
functions. | [
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] |
In mobile manipulation (MM), robots can both navigate within and interact
with their environment and are thus able to complete many more tasks than
robots only capable of navigation or manipulation. In this work, we explore how
to apply imitation learning (IL) to learn continuous visuo-motor policies for
MM tasks. Much prior work has shown that IL can train visuo-motor policies for
either manipulation or navigation domains, but few works have applied IL to the
MM domain. Doing this is challenging for two reasons: on the data side, current
interfaces make collecting high-quality human demonstrations difficult, and on
the learning side, policies trained on limited data can suffer from covariate
shift when deployed. To address these problems, we first propose Mobile
Manipulation RoboTurk (MoMaRT), a novel teleoperation framework allowing
simultaneous navigation and manipulation of mobile manipulators, and collect a
first-of-its-kind large scale dataset in a realistic simulated kitchen setting.
We then propose a learned error detection system to address the covariate shift
by detecting when an agent is in a potential failure state. We train performant
IL policies and error detectors from this data, and achieve over 45% task
success rate and 85% error detection success rate across multiple multi-stage
tasks when trained on expert data. Codebase, datasets, visualization, and more
available at https://sites.google.com/view/il-for-mm/home. | [
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Nowadays on E-commerce platforms, products are presented to the customers
with multiple modalities. These multiple modalities are significant for a
retrieval system while providing attracted products for customers. Therefore,
how to take into account those multiple modalities simultaneously to boost the
retrieval performance is crucial. This problem is a huge challenge to us due to
the following reasons: (1) the way of extracting patch features with the
pre-trained image model (e.g., CNN-based model) has much inductive bias. It is
difficult to capture the efficient information from the product image in
E-commerce. (2) The heterogeneity of multimodal data makes it challenging to
construct the representations of query text and product including title and
image in a common subspace. We propose a novel Adversarial Cross-modal Enhanced
BERT (ACE-BERT) for efficient E-commerce retrieval. In detail, ACE-BERT
leverages the patch features and pixel features as image representation. Thus
the Transformer architecture can be applied directly to the raw image
sequences. With the pre-trained enhanced BERT as the backbone network, ACE-BERT
further adopts adversarial learning by adding a domain classifier to ensure the
distribution consistency of different modality representations for the purpose
of narrowing down the representation gap between query and product.
Experimental results demonstrate that ACE-BERT outperforms the state-of-the-art
approaches on the retrieval task. It is remarkable that ACE-BERT has already
been deployed in our E-commerce's search engine, leading to 1.46% increase in
revenue. | [
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] |
A major challenge in collaborative filtering methods is how to produce
recommendations for cold items (items with no ratings), or integrate cold item
into an existing catalog. Over the years, a variety of hybrid recommendation
models have been proposed to address this problem by utilizing items' metadata
and content along with their ratings or usage patterns. In this work, we wish
to revisit the cold start problem in order to draw attention to an overlooked
challenge: the ability to integrate and balance between (regular) warm items
and completely cold items. In this case, two different challenges arise: (1)
preserving high quality performance on warm items, while (2) learning to
promote cold items to relevant users. First, we show that these two objectives
are in fact conflicting, and the balance between them depends on the business
needs and the application at hand. Next, we propose a novel hybrid
recommendation algorithm that bridges these two conflicting objectives and
enables a harmonized balance between preserving high accuracy for warm items
while effectively promoting completely cold items. We demonstrate the
effectiveness of the proposed algorithm on movies, apps, and articles
recommendations, and provide an empirical analysis of the cold-warm trade-off. | [
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] |
Researchers have formalized reinforcement learning (RL) in different ways. If
an agent in one RL framework is to run within another RL framework's
environments, the agent must first be converted, or mapped, into that other
framework. Whether or not this is possible depends on not only the RL
frameworks in question and but also how intelligence itself is measured. In
this paper, we lay foundations for studying relative-intelligence-preserving
mappability between RL frameworks. We define two types of mappings, called weak
and strong translations, between RL frameworks and prove that existence of
these mappings enables two types of intelligence comparison according to the
mappings preserving relative intelligence. We investigate the existence or lack
thereof of these mappings between: (i) RL frameworks where agents go first and
RL frameworks where environments go first; and (ii) twelve different RL
frameworks differing in terms of whether or not agents or environments are
required to be deterministic. In the former case, we consider various natural
mappings between agent-first and environment-first RL and vice versa; we show
some positive results (some such mappings are strong or weak translations) and
some negative results (some such mappings are not). In the latter case, we
completely characterize which of the twelve RL-framework pairs admit weak
translations, under the assumption of integer-valued rewards and some
additional mild assumptions. | [
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Given the growing use of Artificial Intelligence (AI) and machine learning
(ML) methods across all aspects of environmental sciences, it is imperative
that we initiate a discussion about the ethical and responsible use of AI. In
fact, much can be learned from other domains where AI was introduced, often
with the best of intentions, yet often led to unintended societal consequences,
such as hard coding racial bias in the criminal justice system or increasing
economic inequality through the financial system. A common misconception is
that the environmental sciences are immune to such unintended consequences when
AI is being used, as most data come from observations, and AI algorithms are
based on mathematical formulas, which are often seen as objective. In this
article, we argue the opposite can be the case. Using specific examples, we
demonstrate many ways in which the use of AI can introduce similar consequences
in the environmental sciences. This article will stimulate discussion and
research efforts in this direction. As a community, we should avoid repeating
any foreseeable mistakes made in other domains through the introduction of AI.
In fact, with proper precautions, AI can be a great tool to help {\it reduce}
climate and environmental injustice. We primarily focus on weather and climate
examples but the conclusions apply broadly across the environmental sciences. | [
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] |
Currently, next location recommendation plays a vital role in location-based
social network applications and services. Although many methods have been
proposed to solve this problem, three important challenges have not been well
addressed so far: (1) most existing methods are based on recurrent network,
which is time-consuming to train long sequences due to not allowing for full
parallelism; (2) personalized preferences generally are not considered
reasonably; (3) existing methods rarely systematically studied how to
efficiently utilize various auxiliary information (e.g., user ID and timestamp)
in trajectory data and the spatio-temporal relations among non-consecutive
locations. To address the above challenges, we propose a novel method named
SanMove, a self-attention network based model, to predict the next location via
capturing the long- and short-term mobility patterns of users. Specifically,
SanMove introduces a long-term preference learning module, and it uses a
self-attention module to capture the users long-term mobility pattern which can
represent personalized location preferences of users. Meanwhile, SanMove uses a
spatial-temporal guided non-invasive self-attention (STNOVA) to exploit
auxiliary information to learn short-term preferences. We evaluate SanMove with
two real-world datasets, and demonstrate SanMove is not only faster than the
state-of-the-art RNN-based predict model but also outperforms the baselines for
next location prediction. | [
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] |
The inter/intra-subject variability of electroencephalography (EEG) makes the
practical use of the brain-computer interface (BCI) difficult. In general, the
BCI system requires a calibration procedure to tune the model every time the
system is used. This problem is recognized as a major obstacle to BCI, and to
overcome it, approaches based on transfer learning (TL) have recently emerged.
However, many BCI paradigms are limited in that they consist of a structure
that shows labels first and then measures "imagery", the negative effects of
source subjects containing data that do not contain control signals have been
ignored in many cases of the subject-to-subject TL process. The main purpose of
this paper is to propose a method of excluding subjects that are expected to
have a negative impact on subject-to-subject TL training, which generally uses
data from as many subjects as possible. In this paper, we proposed a BCI
framework using only high-confidence subjects for TL training. In our
framework, a deep neural network selects useful subjects for the TL process and
excludes noisy subjects, using a co-teaching algorithm based on the small-loss
trick. We experimented with leave-one-subject-out validation on two public
datasets (2020 international BCI competition track 4 and OpenBMI dataset). Our
experimental results showed that confidence-aware TL, which selects subjects
with small loss instances, improves the generalization performance of BCI. | [
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In order to classify the indeterministic termination behavior of the open
source SAT solver CryptoMiniSat in multi-threading mode while processing hard
to solve boolean satisfiability problem instances, internal solver runtime
parameters have been collected and analyzed. A subset of these parameters has
been selected and employed as features vector to successfully create a machine
learning model for the binary classification of the solver's termination
behavior with any single new solving run of a not yet solved instance. The
model can be used for the early estimation of a solving attempt as belonging or
not belonging to the class of candidates with good chances for a fast
termination. In this context a combination of active profiles of runtime
characteristics appear to mirror the influence of the solver's momentary
heuristics on the immediate quality of the solver's resolution process. Because
runtime parameters of already the first two solving iterations are enough to
forecast termination of the attempt with good success scores, the results of
the present work deliver a promising basis which can be further developed in
order to enrich CryptoMiniSat or generally any modern SAT solver with AI
abilities. | [
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] |
Intelligent Tutoring Systems have become critically important in future
learning environments. Knowledge Tracing (KT) is a crucial part of that system.
It is about inferring the skill mastery of students and predicting their
performance to adjust the curriculum accordingly. Deep Learning-based KT models
have shown significant predictive performance compared with traditional models.
However, it is difficult to extract psychologically meaningful explanations
from the tens of thousands of parameters in neural networks, that would relate
to cognitive theory. There are several ways to achieve high accuracy in student
performance prediction but diagnostic and prognostic reasoning is more critical
in learning sciences. Since KT problem has few observable features (problem ID
and student's correctness at each practice), we extract meaningful latent
features from students' response data by using machine learning and data mining
techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a
simple model that relies on three meaningful latent features: individual skill
mastery, ability profile (learning transfer across skills), and problem
difficulty. IKT's prediction of future student performance is made using a
Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are
easier to explain than deep learning-based student models. IKT also shows
better student performance prediction than deep learning-based student models
without requiring a huge amount of parameters. We conduct ablation studies on
each feature to examine their contribution to student performance prediction.
Thus, IKT has great potential for providing adaptive and personalized
instructions with causal reasoning in real-world educational systems. | [
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] |
As one of the most prevalent neurodegenerative disorders, Parkinson's disease
(PD) has a significant impact on the fine motor skills of patients. The complex
interplay of different articulators during speech production and realization of
required muscle tension become increasingly difficult, thus leading to a
dysarthric speech. Characteristic patterns such as vowel instability, slurred
pronunciation and slow speech can often be observed in the affected individuals
and were analyzed in previous studies to determine the presence and progression
of PD. In this work, we used a phonetic recognizer trained exclusively on
healthy speech data to investigate how PD affected the phonetic footprint of
patients. We rediscovered numerous patterns that had been described in previous
contributions although our system had never seen any pathological speech
previously. Furthermore, we could show that intermediate activations from the
neural network could serve as feature vectors encoding information related to
the disease state of individuals. We were also able to directly correlate the
expert-rated intelligibility of a speaker with the mean confidence of phonetic
predictions. Our results support the assumption that pathological data is not
necessarily required to train systems that are capable of analyzing PD speech. | [
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] |
Reinforcement learning has recently shown promise in learning quality
solutions in many combinatorial optimization problems. In particular, the
attention-based encoder-decoder models show high effectiveness on various
routing problems, including the Traveling Salesman Problem (TSP).
Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring
routing a heterogeneous fleet of vehicles in coordination -- a truck and a
drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at
a node for the other vehicle to join. State-less attention-based decoder fails
to make such coordination between vehicles. We propose a hybrid model that uses
an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in
which the decoder's hidden state can represent the sequence of actions made. We
empirically demonstrate that such a hybrid model improves upon a purely
attention-based model for both solution quality and computational efficiency.
Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP)
also confirm that the hybrid model is more suitable for the coordinated routing
of multiple vehicles than the attention-based model. The proposed model
demonstrates comparable results as the operations research baseline methods. | [
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] |
Classification of malware families is crucial for a comprehensive
understanding of how they can infect devices, computers, or systems. Thus,
malware identification enables security researchers and incident responders to
take precautions against malware and accelerate mitigation. API call sequences
made by malware are widely utilized features by machine and deep learning
models for malware classification as these sequences represent the behavior of
malware. However, traditional machine and deep learning models remain incapable
of capturing sequence relationships between API calls. On the other hand, the
transformer-based models process sequences as a whole and learn relationships
between API calls due to multi-head attention mechanisms and positional
embeddings. Our experiments demonstrate that the transformer model with one
transformer block layer surpassed the widely used base architecture, LSTM.
Moreover, BERT or CANINE, pre-trained transformer models, outperformed in
classifying highly imbalanced malware families according to evaluation metrics,
F1-score, and AUC score. Furthermore, the proposed bagging-based random
transformer forest (RTF), an ensemble of BERT or CANINE, has reached the
state-of-the-art evaluation scores on three out of four datasets, particularly
state-of-the-art F1-score of 0.6149 on one of the commonly used benchmark
dataset. | [
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] |
For supervised classification problems, this paper considers estimating the
query's label probability through local regression using observed covariates.
Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN)
estimator, which take label average over a ball around the query, are
consistent but asymptotically biased particularly for a large radius of the
ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale
$k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and
extrapolate it to the query itself. However, their theoretical optimality has
been shown for the limit of the infinite number of training samples. For
correcting the asymptotic bias with fewer observations, this paper proposes a
\emph{local radial regression (LRR)} and its logistic regression variant called
\emph{local radial logistic regression~(LRLR)}, by combining the advantages of
LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to
observed labels by taking only the radial distance as the explanatory variable
and then extrapolate the estimated label probability to zero distance. The
usefulness of the proposed method is shown theoretically and experimentally. We
prove the convergence rate of the $L^2$ risk for LRR with reference to
MS-$k$-NN, and our numerical experiments, including real-world datasets of
daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN. | [
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] |
Explainable artificial intelligence and interpretable machine learning are
research domains growing in importance. Yet, the underlying concepts remain
somewhat elusive and lack generally agreed definitions. While recent
inspiration from social sciences has refocused the work on needs and
expectations of human recipients, the field still misses a concrete
conceptualisation. We take steps towards addressing this challenge by reviewing
the philosophical and social foundations of human explainability, which we then
translate into the technological realm. In particular, we scrutinise the notion
of algorithmic black boxes and the spectrum of understanding determined by
explanatory processes and explainees' background knowledge. This approach
allows us to define explainability as (logical) reasoning applied to
transparent insights (into, possibly black-box, predictive systems) interpreted
under background knowledge and placed within a specific context -- a process
that engenders understanding in a selected group of explainees. We then employ
this conceptualisation to revisit strategies for evaluating explainability as
well as the much disputed trade-off between transparency and predictive power,
including its implications for ante-hoc and post-hoc techniques along with
fairness and accountability established by explainability. We furthermore
discuss components of the machine learning workflow that may be in need of
interpretability, building on a range of ideas from human-centred
explainability, with a particular focus on explainees, contrastive statements
and explanatory processes. Our discussion reconciles and complements current
research to help better navigate open questions -- rather than attempting to
address any individual issue -- thus laying a solid foundation for a grounded
discussion and future progress of explainable artificial intelligence and
interpretable machine learning. | [
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] |
Solving combinatorial optimization (CO) on graphs is among the fundamental
tasks for upper-stream applications in data mining, machine learning and
operations research. Despite the inherent NP-hard challenge for CO, heuristics,
branch-and-bound, learning-based solvers are developed to tackle CO problems as
accurately as possible given limited time budgets. However, a practical metric
for the sensitivity of CO solvers remains largely unexplored. Existing
theoretical metrics require the optimal solution which is infeasible, and the
gradient-based adversarial attack metric from deep learning is not compatible
with non-learning solvers that are usually non-differentiable. In this paper,
we develop the first practically feasible robustness metric for general
combinatorial optimization solvers. We develop a no worse optimal cost
guarantee thus do not require optimal solutions, and we tackle the
non-differentiable challenge by resorting to black-box adversarial attack
methods. Extensive experiments are conducted on 14 unique combinations of
solvers and CO problems, and we demonstrate that the performance of
state-of-the-art solvers like Gurobi can degenerate by over 20% under the given
time limit bound on the hard instances discovered by our robustness metric,
raising concerns about the robustness of combinatorial optimization solvers. | [
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] |
EEG decoding systems based on deep neural networks have been widely used in
decision making of brain computer interfaces (BCI). Their predictions, however,
can be unreliable given the significant variance and noise in EEG signals.
Previous works on EEG analysis mainly focus on the exploration of noise pattern
in the source signal, while the uncertainty during the decoding process is
largely unexplored. Automatically detecting and reducing such decoding
uncertainty is important for BCI motor imagery applications such as robotic arm
control etc. In this work, we proposed an uncertainty estimation and reduction
model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding
process. It utilized a combination of dropout oriented method and Bayesian
neural network for uncertainty estimation to incorporate both the uncertainty
in the input signal and the uncertainty in the model parameters. We further
proposed a data augmentation based approach for uncertainty reduction. The
model can be integrated into current widely used EEG neural decoders without
change of architecture. We performed extensive experiments for uncertainty
estimation and its reduction in both intra-subject EEG decoding and
cross-subject EEG decoding on two public motor imagery datasets, where the
proposed model achieves significant improvement both on the quality of
estimated uncertainty and the effectiveness of uncertainty reduction. | [
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] |
Recent years have witnessed the promise that reinforcement learning, coupled
with Graph Neural Network (GNN) architectures, could learn to solve hard
combinatorial optimization problems: given raw input data and an evaluator to
guide the process, the idea is to automatically learn a policy able to return
feasible and high-quality outputs. Recent work have shown promising results but
the latter were mainly evaluated on the travelling salesman problem (TSP) and
similar abstract variants such as Split Delivery Vehicle Routing Problem
(SDVRP). In this paper, we analyze how and whether recent neural architectures
can be applied to graph problems of practical importance. We thus set out to
systematically "transfer" these architectures to the Power and Channel
Allocation Problem (PCAP), which has practical relevance for, e.g., radio
resource allocation in wireless networks. Our experimental results suggest that
existing architectures (i) are still incapable of capturing graph structural
features and (ii) are not suitable for problems where the actions on the graph
change the graph attributes. On a positive note, we show that augmenting the
structural representation of problems with Distance Encoding is a promising
step towards the still-ambitious goal of learning multi-purpose autonomous
solvers. | [
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] |
Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and
AirBnB) can be thought of as two-sided markets with producers and customers of
goods and services. Traditionally, recommendation services in these platforms
have focused on maximizing customer satisfaction by tailoring the results
according to the personalized preferences of individual customers. However, our
investigation reinforces the fact that such customer-centric design of these
services may lead to unfair distribution of exposure to the producers, which
may adversely impact their well-being. On the other hand, a pure
producer-centric design might become unfair to the customers. As more and more
people are depending on such platforms to earn a living, it is important to
ensure fairness to both producers and customers. In this work, by mapping a
fair personalized recommendation problem to a constrained version of the
problem of fairly allocating indivisible goods, we propose to provide fairness
guarantees for both sides. Formally, our proposed {\em FairRec} algorithm
guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and
Envy-Free up to One Item (EF1) fairness for the customers. Extensive
evaluations over multiple real-world datasets show the effectiveness of {\em
FairRec} in ensuring two-sided fairness while incurring a marginal loss in
overall recommendation quality. Finally, we present a modification of FairRec
(named as FairRecPlus) that at the cost of additional computation time,
improves the recommendation performance for the customers, while maintaining
the same fairness guarantees. | [
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GPUs are widely used to accelerate the training of machine learning
workloads. As modern machine learning models become increasingly larger, they
require a longer time to train, leading to higher GPU energy consumption. This
paper presents GPOEO, an online GPU energy optimization framework for machine
learning training workloads. GPOEO dynamically determines the optimal energy
configuration by employing novel techniques for online measurement,
multi-objective prediction modeling, and search optimization. To characterize
the target workload behavior, GPOEO utilizes GPU performance counters. To
reduce the performance counter profiling overhead, it uses an analytical model
to detect the training iteration change and only collects performance counter
data when an iteration shift is detected. GPOEO employs multi-objective models
based on gradient boosting and a local search algorithm to find a trade-off
between execution time and energy consumption. We evaluate the GPOEO by
applying it to 71 machine learning workloads from two AI benchmark suites
running on an NVIDIA RTX3080Ti GPU. Compared with the NVIDIA default scheduling
strategy, GPOEO delivers a mean energy saving of 16.2% with a modest average
execution time increase of 5.1%. | [
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] |
As the deep learning techniques have expanded to real-world recommendation
tasks, many deep neural network based Collaborative Filtering (CF) models have
been developed to project user-item interactions into latent feature space,
based on various neural architectures, such as multi-layer perceptron,
auto-encoder and graph neural networks. However, the majority of existing
collaborative filtering systems are not well designed to handle missing data.
Particularly, in order to inject the negative signals in the training phase,
these solutions largely rely on negative sampling from unobserved user-item
interactions and simply treating them as negative instances, which brings the
recommendation performance degradation. To address the issues, we develop a
Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is
capable of exploring transferable knowledge from observed and unobserved
user-item interactions. The network architecture of CRANet is formed of an
integrative structure with a reflective receptor network and an information
fusion autoencoder module, which endows our recommendation framework with the
ability of encoding implicit user's pairwise preference on both interacted and
non-interacted items. Additionally, a parametric regularization-based
tied-weight scheme is designed to perform robust joint training of the
two-stage CRANet model. We finally experimentally validate CRANet on four
diverse benchmark datasets corresponding to two recommendation tasks, to show
that debiasing the negative signals of user-item interactions improves the
performance as compared to various state-of-the-art recommendation techniques.
Our source code is available at https://github.com/akaxlh/CRANet. | [
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] |
Community detection is a fundamental and important issue in network science,
but there are only a few community detection algorithms based on graph neural
networks, among which unsupervised algorithms are almost blank. By fusing the
high-order modularity information with network features, this paper proposes a
Variational Graph AutoEncoder Reconstruction based community detection VGAER
for the first time, and gives its non-probabilistic version. They do not need
any prior information. We have carefully designed corresponding input features,
decoder, and downstream tasks based on the community detection task and these
designs are concise, natural, and perform well (NMI values under our design are
improved by 59.1% - 565.9%). Based on a series of experiments with wide range
of datasets and advanced methods, VGAER has achieved superior performance and
shows strong competitiveness and potential with a simpler design. Finally, we
report the results of algorithm convergence analysis and t-SNE visualization,
which clearly depicted the stable performance and powerful network modularity
ability of VGAER. Our codes are available at https://github.com/qcydm/VGAER. | [
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] |
Tether-net launched from a chaser spacecraft provides a promising method to
capture and dispose of large space debris in orbit. This tether-net system is
subject to several sources of uncertainty in sensing and actuation that affect
the performance of its net launch and closing control. Earlier
reliability-based optimization approaches to design control actions however
remain challenging and computationally prohibitive to generalize over varying
launch scenarios and target (debris) state relative to the chaser. To search
for a general and reliable control policy, this paper presents a reinforcement
learning framework that integrates a proximal policy optimization (PPO2)
approach with net dynamics simulations. The latter allows evaluating the
episodes of net-based target capture, and estimate the capture quality index
that serves as the reward feedback to PPO2. Here, the learned policy is
designed to model the timing of the net closing action based on the state of
the moving net and the target, under any given launch scenario. A stochastic
state transition model is considered in order to incorporate synthetic
uncertainties in state estimation and launch actuation. Along with notable
reward improvement during training, the trained policy demonstrates capture
performance (over a wide range of launch/target scenarios) that is close to
that obtained with reliability-based optimization run over an individual
scenario. | [
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] |
Modern online services continuously generate data at very fast rates. This
continuous flow of data encompasses content - e.g., posts, news, products,
comments -, but also user feedback - e.g., ratings, views, reads, clicks -,
together with context data - user device, spatial or temporal data, user task
or activity, weather. This can be overwhelming for systems and algorithms
designed to train in batches, given the continuous and potentially fast change
of content, context and user preferences or intents. Therefore, it is important
to investigate online methods able to transparently adapt to the inherent
dynamics of online services. Incremental models that learn from data streams
are gaining attention in the recommender systems community, given their natural
ability to deal with the continuous flows of data generated in dynamic, complex
environments. User modeling and personalization can particularly benefit from
algorithms capable of maintaining models incrementally and online.
The objective of this workshop is to foster contributions and bring together
a growing community of researchers and practitioners interested in online,
adaptive approaches to user modeling, recommendation and personalization, and
their implications regarding multiple dimensions, such as evaluation,
reproducibility, privacy and explainability. | [
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] |
Artificial Intelligence (AI) is making a significant impact in multiple areas
like medical, military, industrial, domestic, law, arts as AI is capable to
perform several roles such as managing smart factories, driving autonomous
vehicles, creating accurate weather forecasts, detecting cancer and personal
assistants, etc. Software testing is the process of putting the software to
test for some abnormal behaviour of the software. Software testing is a
tedious, laborious and most time-consuming process. Automation tools have been
developed that help to automate some activities of the testing process to
enhance quality and timely delivery. Over time with the inclusion of continuous
integration and continuous delivery (CI/CD) pipeline, automation tools are
becoming less effective. The testing community is turning to AI to fill the gap
as AI is able to check the code for bugs and errors without any human
intervention and in a much faster way than humans. In this study, we aim to
recognize the impact of AI technologies on various software testing activities
or facets in the STLC. Further, the study aims to recognize and explain some of
the biggest challenges software testers face while applying AI to testing. The
paper also proposes some key contributions of AI in the future to the domain of
software testing. | [
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Automatic recognition of disordered speech remains a highly challenging task
to date. Sources of variability commonly found in normal speech including
accent, age or gender, when further compounded with the underlying causes of
speech impairment and varying severity levels, create large diversity among
speakers. To this end, speaker adaptation techniques play a vital role in
current speech recognition systems. Motivated by the spectro-temporal level
differences between disordered and normal speech that systematically manifest
in articulatory imprecision, decreased volume and clarity, slower speaking
rates and increased dysfluencies, novel spectro-temporal subspace basis
embedding deep features derived by SVD decomposition of speech spectrum are
proposed to facilitate both accurate speech intelligibility assessment and
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN and
end-to-end disordered speech recognition systems. Experiments conducted on the
UASpeech corpus suggest the proposed spectro-temporal deep feature adapted
systems consistently outperformed baseline i-Vector adaptation by up to 2.63%
absolute (8.6% relative) reduction in word error rate (WER) with or without
data augmentation. Learning hidden unit contribution (LHUC) based speaker
adaptation was further applied. The final speaker adapted system using the
proposed spectral basis embedding features gave an overall WER of 25.6% on the
UASpeech test set of 16 dysarthric speakers | [
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] |
Randomized controlled trials (RCTs) are considered as the gold standard for
testing causal hypotheses in the clinical domain. However, the investigation of
prognostic variables of patient outcome in a hypothesized cause-effect route is
not feasible using standard statistical methods. Here, we propose a new
automated causal inference method (AutoCI) built upon the invariant causal
prediction (ICP) framework for the causal re-interpretation of clinical trial
data. Compared to existing methods, we show that the proposed AutoCI allows to
efficiently determine the causal variables with a clear differentiation on two
real-world RCTs of endometrial cancer patients with mature outcome and
extensive clinicopathological and molecular data. This is achieved via
suppressing the causal probability of non-causal variables by a wide margin. In
ablation studies, we further demonstrate that the assignment of causal
probabilities by AutoCI remain consistent in the presence of confounders. In
conclusion, these results confirm the robustness and feasibility of AutoCI for
future applications in real-world clinical analysis. | [
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Despite the rapid progress of automatic speech recognition (ASR) technologies
in the past few decades, recognition of disordered speech remains a highly
challenging task to date. Disordered speech presents a wide spectrum of
challenges to current data intensive deep neural networks (DNNs) based ASR
technologies that predominantly target normal speech. This paper presents
recent research efforts at the Chinese University of Hong Kong (CUHK) to
improve the performance of disordered speech recognition systems on the largest
publicly available UASpeech dysarthric speech corpus. A set of novel modelling
techniques including neural architectural search, data augmentation using
spectra-temporal perturbation, model based speaker adaptation and cross-domain
generation of visual features within an audio-visual speech recognition (AVSR)
system framework were employed to address the above challenges. The combination
of these techniques produced the lowest published word error rate (WER) of
25.21% on the UASpeech test set 16 dysarthric speakers, and an overall WER
reduction of 5.4% absolute (17.6% relative) over the CUHK 2018 dysarthric
speech recognition system featuring a 6-way DNN system combination and cross
adaptation of out-of-domain normal speech data trained systems. Bayesian model
adaptation further allows rapid adaptation to individual dysarthric speakers to
be performed using as little as 3.06 seconds of speech. The efficacy of these
techniques were further demonstrated on a CUDYS Cantonese dysarthric speech
recognition task. | [
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] |
Sequential recommendation models the dynamics of a user's previous behaviors
in order to forecast the next item, and has drawn a lot of attention.
Transformer-based approaches, which embed items as vectors and use dot-product
self-attention to measure the relationship between items, demonstrate superior
capabilities among existing sequential methods. However, users' real-world
sequential behaviors are \textit{\textbf{uncertain}} rather than deterministic,
posing a significant challenge to present techniques. We further suggest that
dot-product-based approaches cannot fully capture \textit{\textbf{collaborative
transitivity}}, which can be derived in item-item transitions inside sequences
and is beneficial for cold start items. We further argue that BPR loss has no
constraint on positive and sampled negative items, which misleads the
optimization. We propose a novel \textbf{STO}chastic
\textbf{S}elf-\textbf{A}ttention~(STOSA) to overcome these issues. STOSA, in
particular, embeds each item as a stochastic Gaussian distribution, the
covariance of which encodes the uncertainty. We devise a novel Wasserstein
Self-Attention module to characterize item-item position-wise relationships in
sequences, which effectively incorporates uncertainty into model training.
Wasserstein attentions also enlighten the collaborative transitivity learning
as it satisfies triangle inequality. Moreover, we introduce a novel
regularization term to the ranking loss, which assures the dissimilarity
between positive and the negative items. Extensive experiments on five
real-world benchmark datasets demonstrate the superiority of the proposed model
over state-of-the-art baselines, especially on cold start items. The code is
available in \url{https://github.com/zfan20/STOSA}. | [
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