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The dynamic, real-time, and accurate inference of model parameters from
empirical data is of great importance in many scientific and engineering
disciplines that use computational models (such as a digital twin) for the
analysis and prediction of complex physical processes. However, fast and
accurate inference for processes with large and high dimensional datasets
cannot easily be achieved with state-of-the-art methods under noisy real-world
conditions. The primary reason is that the inference of model parameters with
traditional techniques based on optimisation or sampling often suffers from
computational and statistical challenges, resulting in a trade-off between
accuracy and deployment time. In this paper, we propose a novel framework for
inference of model parameters based on reinforcement learning. The contribution
of the paper is twofold: 1) We reformulate the inference problem as a tracking
problem with the objective of learning a policy that forces the response of the
physics-based model to follow the observations; 2) We propose the constrained
Lyapunov-based actor-critic (CLAC) algorithm to enable the robust and accurate
inference of physics-based model parameters in real time under noisy real-world
conditions. The proposed methodology is demonstrated and evaluated on two
model-based diagnostics test cases utilizing two different physics-based models
of turbofan engines. The performance of the methodology is compared to that of
two alternative approaches: a state update method (unscented Kalman filter) and
a supervised end-to-end mapping with deep neural networks. The experimental
results demonstrate that the proposed methodology outperforms all other tested
methods in terms of speed and robustness, with high inference accuracy. | [
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] |
Ridehailing applications that collect mobility data from individuals to
inform smart city planning predict each trip's fare pricing with automated
algorithms that rely on artificial intelligence (AI). This type of AI
algorithm, namely a price discrimination algorithm, is widely used in the
industry's black box systems for dynamic individualized pricing. Lacking
transparency, studying such AI systems for fairness and disparate impact has
not been possible without access to data used in generating the outcomes of
price discrimination algorithms. Recently, in an effort to enhance transparency
in city planning, the city of Chicago regulation mandated that transportation
providers publish anonymized data on ridehailing. As a result, we present the
first large-scale measurement of the disparate impact of price discrimination
algorithms used by ridehailing applications.
The application of random effects models from the meta-analysis literature
combines the city-level effects of AI bias on fare pricing from census tract
attributes, aggregated from the American Community Survey. An analysis of 100
million ridehailing samples from the city of Chicago indicates a significant
disparate impact in fare pricing of neighborhoods due to AI bias learned from
ridehailing utilization patterns associated with demographic attributes.
Neighborhoods with larger non-white populations, higher poverty levels, younger
residents, and high education levels are significantly associated with higher
fare prices, with combined effect sizes, measured in Cohen's d, of -0.32,
-0.28, 0.69, and 0.24 for each demographic, respectively. Further, our methods
hold promise for identifying and addressing the sources of disparate impact in
AI algorithms learning from datasets that contain U.S. geolocations. | [
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] |
The 'old world' instrument, survey, remains a tool of choice for firms to
obtain ratings of satisfaction and experience that customers realize while
interacting online with firms. While avenues for survey have evolved from
emails and links to pop-ups while browsing, the deficiencies persist. These
include - reliance on ratings of very few respondents to infer about all
customers' online interactions; failing to capture a customer's interactions
over time since the rating is a one-time snapshot; and inability to tie back
customers' ratings to specific interactions because ratings provided relate to
all interactions. To overcome these deficiencies we extract proxy ratings from
clickstream data, typically collected for every customer's online interactions,
by developing an approach based on Reinforcement Learning (RL). We introduce a
new way to interpret values generated by the value function of RL, as proxy
ratings. Our approach does not need any survey data for training. Yet, on
validation against actual survey data, proxy ratings yield reasonable
performance results. Additionally, we offer a new way to draw insights from
values of the value function, which allow associating specific interactions to
their proxy ratings. We introduce two new metrics to represent ratings - one,
customer-level and the other, aggregate-level for click actions across
customers. Both are defined around proportion of all pairwise, successive
actions that show increase in proxy ratings. This intuitive customer-level
metric enables gauging the dynamics of ratings over time and is a better
predictor of purchase than customer ratings from survey. The aggregate-level
metric allows pinpointing actions that help or hurt experience. In sum, proxy
ratings computed unobtrusively from clickstream, for every action, for each
customer, and for every session can offer interpretable and more insightful
alternative to surveys. | [
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] |
The objective of this work is to augment the basic abilities of a robot by
learning to use sensorimotor primitives to solve complex long-horizon
manipulation problems. This requires flexible generative planning that can
combine primitive abilities in novel combinations and thus generalize across a
wide variety of problems. In order to plan with primitive actions, we must have
models of the actions: under what circumstances will executing this primitive
successfully achieve some particular effect in the world?
We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
constraints on skill effectiveness from small numbers of expensive-to-collect
training examples. Additionally, we develop efficient adaptive sampling methods
for generating a comprehensive and diverse sequence of continuous candidate
control parameter values (such as pouring waypoints for a cup) during planning.
These values become end-effector goals for traditional motion planners that
then solve for a full robot motion that performs the skill. By using learning
and planning methods in conjunction, we take advantage of the strengths of each
and plan for a wide variety of complex dynamic manipulation tasks. We
demonstrate our approach in an integrated system, combining traditional
robotics primitives with our newly learned models using an efficient robot task
and motion planner. We evaluate our approach both in simulation and in the real
world through measuring the quality of the selected primitive actions. Finally,
we apply our integrated system to a variety of long-horizon simulated and
real-world manipulation problems. | [
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] |
Several behavioral, social, and public health interventions, such as
suicide/HIV prevention or community preparedness against natural disasters,
leverage social network information to maximize outreach. Algorithmic influence
maximization techniques have been proposed to aid with the choice of "peer
leaders" or "influencers" in such interventions. Yet, traditional algorithms
for influence maximization have not been designed with these interventions in
mind. As a result, they may disproportionately exclude minority communities
from the benefits of the intervention. This has motivated research on fair
influence maximization. Existing techniques come with two major drawbacks.
First, they require committing to a single fairness measure. Second, these
measures are typically imposed as strict constraints leading to undesirable
properties such as wastage of resources.
To address these shortcomings, we provide a principled characterization of
the properties that a fair influence maximization algorithm should satisfy. In
particular, we propose a framework based on social welfare theory, wherein the
cardinal utilities derived by each community are aggregated using the
isoelastic social welfare functions. Under this framework, the trade-off
between fairness and efficiency can be controlled by a single inequality
aversion design parameter. We then show under what circumstances our proposed
principles can be satisfied by a welfare function. The resulting optimization
problem is monotone and submodular and can be solved efficiently with
optimality guarantees. Our framework encompasses as special cases leximin and
proportional fairness. Extensive experiments on synthetic and real world
datasets including a case study on landslide risk management demonstrate the
efficacy of the proposed framework. | [
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] |
The broader ambition of this article is to popularize an approach for the
fair distribution of the quantity of a system's output to its subsystems, while
allowing for underlying complex subsystem level interactions. Particularly, we
present a data-driven approach to vehicle price modeling and its component
price estimation by leveraging a combination of concepts from machine learning
and game theory. We show an alternative to common teardown methodologies and
surveying approaches for component and vehicle price estimation at the
manufacturer's suggested retail price (MSRP) level that has the advantage of
bypassing the uncertainties involved in 1) the gathering of teardown data, 2)
the need to perform expensive and biased surveying, and 3) the need to perform
retail price equivalent (RPE) or indirect cost multiplier (ICM) adjustments to
mark up direct manufacturing costs to MSRP. This novel exercise not only
provides accurate pricing of the technologies at the customer level, but also
shows the, a priori known, large gaps in pricing strategies between
manufacturers, vehicle sizes, classes, market segments, and other factors.
There is also clear synergism or interaction between the price of certain
technologies and other specifications present in the same vehicle. Those
(unsurprising) results are indication that old methods of manufacturer-level
component costing, aggregation, and the application of a flat and rigid RPE or
ICM adjustment factor should be carefully examined. The findings are based on
an extensive database, developed by Argonne National Laboratory, that includes
more than 64,000 vehicles covering MY1990 to MY2020 over hundreds of vehicle
specs. | [
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In February 2020, the European Commission (EC) published a white paper
entitled, On Artificial Intelligence - A European approach to excellence and
trust. This paper outlines the EC's policy options for the promotion and
adoption of artificial intelligence (AI) in the European Union. The Montreal AI
Ethics Institute (MAIEI) reviewed this paper and published a response
addressing the EC's plans to build an "ecosystem of excellence" and an
"ecosystem of trust," as well as the safety and liability implications of AI,
the internet of things (IoT), and robotics.
MAIEI provides 15 recommendations in relation to the sections outlined above,
including: 1) focus efforts on the research and innovation community, member
states, and the private sector; 2) create alignment between trading partners'
policies and EU policies; 3) analyze the gaps in the ecosystem between
theoretical frameworks and approaches to building trustworthy AI; 4) focus on
coordination and policy alignment; 5) focus on mechanisms that promote private
and secure sharing of data; 6) create a network of AI research excellence
centres to strengthen the research and innovation community; 7) promote
knowledge transfer and develop AI expertise through Digital Innovation Hubs; 8)
add nuance to the discussion regarding the opacity of AI systems; 9) create a
process for individuals to appeal an AI system's decision or output; 10)
implement new rules and strengthen existing regulations; 11) ban the use of
facial recognition technology; 12) hold all AI systems to similar standards and
compulsory requirements; 13) ensure biometric identification systems fulfill
the purpose for which they are implemented; 14) implement a voluntary labelling
system for systems that are not considered high-risk; 15) appoint individuals
to the oversight process who understand AI systems well and are able to
communicate potential risks. | [
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] |
These past few months have been especially challenging, and the deployment of
technology in ways hitherto untested at an unrivalled pace has left the
internet and technology watchers aghast. Artificial intelligence has become the
byword for technological progress and is being used in everything from helping
us combat the COVID-19 pandemic to nudging our attention in different
directions as we all spend increasingly larger amounts of time online. It has
never been more important that we keep a sharp eye out on the development of
this field and how it is shaping our society and interactions with each other.
With this inaugural edition of the State of AI Ethics we hope to bring forward
the most important developments that caught our attention at the Montreal AI
Ethics Institute this past quarter. Our goal is to help you navigate this
ever-evolving field swiftly and allow you and your organization to make
informed decisions. This pulse-check for the state of discourse, research, and
development is geared towards researchers and practitioners alike who are
making decisions on behalf of their organizations in considering the societal
impacts of AI-enabled solutions. We cover a wide set of areas in this report
spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and
Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over
the past quarter surfacing signal from the noise so that you are equipped with
the right tools and knowledge to confidently tread this complex yet
consequential domain. | [
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] |
As a massive number of the Internet of Things (IoT) devices are deployed, the
security and privacy issues in IoT arouse more and more attention. The IoT
attacks are causing tremendous loss to the IoT networks and even threatening
human safety. Compared to traditional networks, IoT networks have unique
characteristics, which make the attack detection more challenging. First, the
heterogeneity of platforms, protocols, software, and hardware exposes various
vulnerabilities. Second, in addition to the traditional high-rate attacks, the
low-rate attacks are also extensively used by IoT attackers to obfuscate the
legitimate and malicious traffic. These low-rate attacks are challenging to
detect and can persist in the networks. Last, the attackers are evolving to be
more intelligent and can dynamically change their attack strategies based on
the environment feedback to avoid being detected, making it more challenging
for the defender to discover a consistent pattern to identify the attack.
In order to adapt to the new characteristics in IoT attacks, we propose a
reinforcement learning-based attack detection model that can automatically
learn and recognize the transformation of the attack pattern. Therefore, we can
continuously detect IoT attacks with less human intervention. In this paper, we
explore the crucial features of IoT traffics and utilize the entropy-based
metrics to detect both the high-rate and low-rate IoT attacks. Afterward, we
leverage the reinforcement learning technique to continuously adjust the attack
detection threshold based on the detection feedback, which optimizes the
detection and the false alarm rate. We conduct extensive experiments over a
real IoT attack dataset and demonstrate the effectiveness of our IoT attack
detection framework. | [
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We introduce a GP generalization of ResNets (including ResNets as a
particular case). We show that ResNets (and their GP generalization) converge,
in the infinite depth limit, to a generalization of image registration
variational algorithms. Whereas computational anatomy aligns images via warping
of the material space, this generalization aligns ideas (or abstract shapes as
in Plato's theory of forms) via the warping of the RKHS of functions mapping
the input space to the output space. While the Hamiltonian interpretation of
ResNets is not new, it was based on an Ansatz. We do not rely on this Ansatz
and present the first rigorous proof of convergence of ResNets with trained
weights and biases towards a Hamiltonian dynamics driven flow. Our constructive
proof reveals several remarkable properties of ResNets and their GP
generalization. ResNets regressors are kernel regressors with data-dependent
warping kernels. Minimizers of $L_2$ regularized ResNets satisfy a discrete
least action principle implying the near preservation of the norm of weights
and biases across layers. The trained weights of ResNets with $L^2$
regularization can be identified by solving an autonomous Hamiltonian system.
The trained ResNet parameters are unique up to the initial momentum whose
representation is generally sparse. The kernel regularization strategy provides
a provably robust alternative to Dropout for ANNs. We introduce a functional
generalization of GPs leading to error estimates for ResNets. We identify the
(EPDiff) mean fields limit of trained ResNet parameters. We show that the
composition of warping regression blocks with reduced equivariant multichannel
kernels (introduced here) recovers and generalizes CNNs to arbitrary spaces and
groups of transformations. | [
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] |
Artificial intelligence (AI) has been applied widely in our daily lives in a
variety of ways with numerous success stories. AI has also contributed to
dealing with the coronavirus disease (COVID-19) pandemic, which has been
happening around the globe. This paper presents a survey of AI methods being
used in various applications in the fight against the COVID-19 outbreak and
outlines the crucial role of AI research in this unprecedented battle. We touch
on areas where AI plays as an essential component, from medical image
processing, data analytics, text mining and natural language processing, the
Internet of Things, to computational biology and medicine. A summary of
COVID-19 related data sources that are available for research purposes is also
presented. Research directions on exploring the potential of AI and enhancing
its capability and power in the pandemic battle are thoroughly discussed. We
identify 13 groups of problems related to the COVID-19 pandemic and highlight
promising AI methods and tools that can be used to address these problems. It
is envisaged that this study will provide AI researchers and the wider
community with an overview of the current status of AI applications, and
motivate researchers to harness AI's potential in the fight against COVID-19. | [
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] |
AI systems that can capture human-like behavior are becoming increasingly
useful in situations where humans may want to learn from these systems,
collaborate with them, or engage with them as partners for an extended
duration. In order to develop human-oriented AI systems, the problem of
predicting human actions -- as opposed to predicting optimal actions -- has
received considerable attention. Existing work has focused on capturing human
behavior in an aggregate sense, which potentially limits the benefit any
particular individual could gain from interaction with these systems. We extend
this line of work by developing highly accurate predictive models of individual
human behavior in chess. Chess is a rich domain for exploring human-AI
interaction because it combines a unique set of properties: AI systems achieved
superhuman performance many years ago, and yet humans still interact with them
closely, both as opponents and as preparation tools, and there is an enormous
corpus of recorded data on individual player games. Starting with Maia, an
open-source version of AlphaZero trained on a population of human players, we
demonstrate that we can significantly improve prediction accuracy of a
particular player's moves by applying a series of fine-tuning methods.
Furthermore, our personalized models can be used to perform stylometry --
predicting who made a given set of moves -- indicating that they capture human
decision-making at an individual level. Our work demonstrates a way to bring AI
systems into better alignment with the behavior of individual people, which
could lead to large improvements in human-AI interaction. | [
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0.015019221231341362,
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] |
We present an automatic non-invasive way of detecting cough events based on
both accelerometer and audio signals.
The acceleration signals are captured by a smartphone firmly attached to the
patient's bed, using its integrated accelerometer.
The audio signals are captured simultaneously by the same smartphone using an
external microphone.
We have compiled a manually-annotated dataset containing such
simultaneously-captured acceleration and audio signals for approximately 6000
cough and 68000 non-cough events from 14 adult male patients in a tuberculosis
clinic.
LR, SVM and MLP are evaluated as baseline classifiers and compared with deep
architectures such as CNN, LSTM, and Resnet50 using a leave-one-out
cross-validation scheme.
We find that the studied classifiers can use either acceleration or audio
signals to distinguish between coughing and other activities including
sneezing, throat-clearing, and movement on the bed with high accuracy.
However, in all cases, the deep neural networks outperform the shallow
classifiers by a clear margin and the Resnet50 offers the best performance by
achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals
respectively.
While audio-based classification consistently offers a better performance
than acceleration-based classification, we observe that the difference is very
small for the best systems.
Since the acceleration signal requires less processing power, and since the
need to record audio is sidestepped and thus privacy is inherently secured, and
since the recording device is attached to the bed and not worn, an
accelerometer-based highly accurate non-invasive cough detector may represent a
more convenient and readily accepted method in long-term cough monitoring. | [
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] |
Interest in applying Artificial Intelligence (AI) techniques to compiler
optimizations is increasing rapidly, but compiler research has a high entry
barrier. Unlike in other domains, compiler and AI researchers do not have
access to the datasets and frameworks that enable fast iteration and
development of ideas, and getting started requires a significant engineering
investment. What is needed is an easy, reusable experimental infrastructure for
real world compiler optimization tasks that can serve as a common benchmark for
comparing techniques, and as a platform to accelerate progress in the field.
We introduce CompilerGym, a set of environments for real world compiler
optimization tasks, and a toolkit for exposing new optimization tasks to
compiler researchers. CompilerGym enables anyone to experiment on production
compiler optimization problems through an easy-to-use package, regardless of
their experience with compilers. We build upon the popular OpenAI Gym interface
enabling researchers to interact with compilers using Python and a familiar
API.
We describe the CompilerGym architecture and implementation, characterize the
optimization spaces and computational efficiencies of three included compiler
environments, and provide extensive empirical evaluations. Compared to prior
works, CompilerGym offers larger datasets and optimization spaces, is 27x more
computationally efficient, is fault-tolerant, and capable of detecting
reproducibility bugs in the underlying compilers.
In making it easy for anyone to experiment with compilers - irrespective of
their background - we aim to accelerate progress in the AI and compiler
research domains. | [
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] |
Being able to recommend links between users in online social networks is
important for users to connect with like-minded individuals as well as for the
platforms themselves and third parties leveraging social media information to
grow their business. Predictions are typically based on unsupervised or
supervised learning, often leveraging simple yet effective graph topological
information, such as the number of common neighbors. However, we argue that
richer information about personal social structure of individuals might lead to
better predictions. In this paper, we propose to leverage well-established
social cognitive theories to improve link prediction performance. According to
these theories, individuals arrange their social relationships along, on
average, five concentric circles of decreasing intimacy. We postulate that
relationships in different circles have different importance in predicting new
links. In order to validate this claim, we focus on popular feature-extraction
prediction algorithms (both unsupervised and supervised) and we extend them to
include social-circles awareness. We validate the prediction performance of
these circle-aware algorithms against several benchmarks (including their
baseline versions as well as node-embedding- and GNN-based link prediction),
leveraging two Twitter datasets comprising a community of video gamers and
generic users. We show that social-awareness generally provides significant
improvements in the prediction performance, beating also state-of-the-art
solutions like node2vec and SEAL, and without increasing the computational
complexity. Finally, we show that social-awareness can be used in place of
using a classifier (which may be costly or impractical) for targeting a
specific category of users. | [
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In this paper, we study the problem of learning a repertoire of low-level
skills from raw images that can be sequenced to complete long-horizon
visuomotor tasks. Reinforcement learning (RL) is a promising approach for
acquiring short-horizon skills autonomously. However, the focus of RL
algorithms has largely been on the success of those individual skills, more so
than learning and grounding a large repertoire of skills that can be sequenced
to complete extended multi-stage tasks. The latter demands robustness and
persistence, as errors in skills can compound over time, and may require the
robot to have a number of primitive skills in its repertoire, rather than just
one. To this end, we introduce EMBER, a model-based RL method for learning
primitive skills that are suitable for completing long-horizon visuomotor
tasks. EMBER learns and plans using a learned model, critic, and success
classifier, where the success classifier serves both as a reward function for
RL and as a grounding mechanism to continuously detect if the robot should
retry a skill when unsuccessful or under perturbations. Further, the learned
model is task-agnostic and trained using data from all skills, enabling the
robot to efficiently learn a number of distinct primitives. These visuomotor
primitive skills and their associated pre- and post-conditions can then be
directly combined with off-the-shelf symbolic planners to complete long-horizon
tasks. On a Franka Emika robot arm, we find that EMBER enables the robot to
complete three long-horizon visuomotor tasks at 85% success rate, such as
organizing an office desk, a file cabinet, and drawers, which require
sequencing up to 12 skills, involve 14 unique learned primitives, and demand
generalization to novel objects. | [
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] |
Advanced metamorphic malware and ransomware, by using obfuscation, could
alter their internal structure with every attack. If such malware could intrude
even into any of the IoT networks, then even if the original malware instance
gets detected, by that time it can still infect the entire network. It is
challenging to obtain training data for such evasive malware. Therefore, in
this paper, we present ADVERSARIALuscator, a novel system that uses specialized
Adversarial-DRL to obfuscate malware at the opcode level and create multiple
metamorphic instances of the same. To the best of our knowledge,
ADVERSARIALuscator is the first-ever system that adopts the Markov Decision
Process-based approach to convert and find a solution to the problem of
creating individual obfuscations at the opcode level. This is important as the
machine language level is the least at which functionality could be preserved
so as to mimic an actual attack effectively. ADVERSARIALuscator is also the
first-ever system to use efficient continuous action control capable of deep
reinforcement learning agents like the Proximal Policy Optimization in the area
of cyber security. Experimental results indicate that ADVERSARIALuscator could
raise the metamorphic probability of a corpus of malware by >0.45.
Additionally, more than 33% of metamorphic instances generated by
ADVERSARIALuscator were able to evade the most potent IDS. If such malware
could intrude even into any of the IoT networks, then even if the original
malware instance gets detected, by that time it can still infect the entire
network. Hence ADVERSARIALuscator could be used to generate data representative
of a swarm of very potent and coordinated AI-based metamorphic malware attacks.
The so generated data and simulations could be used to bolster the defenses of
an IDS against an actual AI-based metamorphic attack from advanced malware and
ransomware. | [
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] |
Fully Connected Neural Network (FCNN) is a class of Artificial Neural
Networks widely used in computer science and engineering, whereas the training
process can take a long time with large datasets in existing many-core systems.
Optical Network-on-Chip (ONoC), an emerging chip-scale optical interconnection
technology, has great potential to accelerate the training of FCNN with low
transmission delay, low power consumption, and high throughput. However,
existing methods based on Electrical Network-on-Chip (ENoC) cannot fit in ONoC
because of the unique properties of ONoC. In this paper, we propose a
fine-grained parallel computing model for accelerating FCNN training on ONoC
and derive the optimal number of cores for each execution stage with the
objective of minimizing the total amount of time to complete one epoch of FCNN
training. To allocate the optimal number of cores for each execution stage, we
present three mapping strategies and compare their advantages and disadvantages
in terms of hotspot level, memory requirement, and state transitions.
Simulation results show that the average prediction error for the optimal
number of cores in NN benchmarks is within 2.3%. We further carry out extensive
simulations which demonstrate that FCNN training time can be reduced by 22.28%
and 4.91% on average using our proposed scheme, compared with traditional
parallel computing methods that either allocate a fixed number of cores or
allocate as many cores as possible, respectively. Compared with ENoC,
simulation results show that under batch sizes of 64 and 128, on average ONoC
can achieve 21.02% and 12.95% on reducing training time with 47.85% and 39.27%
on saving energy, respectively. | [
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Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers. | [
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] |
Objective: Anemia is a frequent comorbidity in hemodialysis patients that can
be successfully treated by administering erythropoiesis-stimulating agents
(ESAs). ESAs dosing is currently based on clinical protocols that often do not
account for the high inter- and intra-individual variability in the patient's
response. As a result, the hemoglobin level of some patients oscillates around
the target range, which is associated with multiple risks and side-effects.
This work proposes a methodology based on reinforcement learning (RL) to
optimize ESA therapy.
Methods: RL is a data-driven approach for solving sequential decision-making
problems that are formulated as Markov decision processes (MDPs). Computing
optimal drug administration strategies for chronic diseases is a sequential
decision-making problem in which the goal is to find the best sequence of drug
doses. MDPs are particularly suitable for modeling these problems due to their
ability to capture the uncertainty associated with the outcome of the treatment
and the stochastic nature of the underlying process. The RL algorithm employed
in the proposed methodology is fitted Q iteration, which stands out for its
ability to make an efficient use of data.
Results: The experiments reported here are based on a computational model
that describes the effect of ESAs on the hemoglobin level. The performance of
the proposed method is evaluated and compared with the well-known Q-learning
algorithm and with a standard protocol. Simulation results show that the
performance of Q-learning is substantially lower than FQI and the protocol.
Conclusion: Although prospective validation is required, promising results
demonstrate the potential of RL to become an alternative to current protocols. | [
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A line of recent work provides welfare guarantees of simple combinatorial
auction formats, such as selling m items via simultaneous second price auctions
(SiSPAs) (Christodoulou et al. 2008, Bhawalkar and Roughgarden 2011, Feldman et
al. 2013). These guarantees hold even when the auctions are repeatedly executed
and players use no-regret learning algorithms. Unfortunately, off-the-shelf
no-regret algorithms for these auctions are computationally inefficient as the
number of actions is exponential. We show that this obstacle is insurmountable:
there are no polynomial-time no-regret algorithms for SiSPAs, unless
RP$\supseteq$ NP, even when the bidders are unit-demand. Our lower bound raises
the question of how good outcomes polynomially-bounded bidders may discover in
such auctions.
To answer this question, we propose a novel concept of learning in auctions,
termed "no-envy learning." This notion is founded upon Walrasian equilibrium,
and we show that it is both efficiently implementable and results in
approximately optimal welfare, even when the bidders have fractionally
subadditive (XOS) valuations (assuming demand oracles) or coverage valuations
(without demand oracles). No-envy learning outcomes are a relaxation of
no-regret outcomes, which maintain their approximate welfare optimality while
endowing them with computational tractability. Our results extend to other
auction formats that have been studied in the literature via the smoothness
paradigm.
Our results for XOS valuations are enabled by a novel
Follow-The-Perturbed-Leader algorithm for settings where the number of experts
is infinite, and the payoff function of the learner is non-linear. This
algorithm has applications outside of auction settings, such as in security
games. Our result for coverage valuations is based on a novel use of convex
rounding schemes and a reduction to online convex optimization. | [
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Bioinformatics tools have been developed to interpret gene expression data at
the gene set level, and these gene set based analyses improve the biologists'
capability to discover functional relevance of their experiment design. While
elucidating gene set individually, inter gene sets association is rarely taken
into consideration. Deep learning, an emerging machine learning technique in
computational biology, can be used to generate an unbiased combination of gene
set, and to determine the biological relevance and analysis consistency of
these combining gene sets by leveraging large genomic data sets. In this study,
we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
with the incorporation of a priori defined gene sets that retain the crucial
biological features in the latent layer. We introduced the concept of the gene
superset, an unbiased combination of gene sets with weights trained by the
autoencoder, where each node in the latent layer is a superset. Trained with
genomic data from TCGA and evaluated with their accompanying clinical
parameters, we showed gene supersets' ability of discriminating tumor subtypes
and their prognostic capability. We further demonstrated the biological
relevance of the top component gene sets in the significant supersets. Using
autoencoder model and gene superset at its latent layer, we demonstrated that
gene supersets retain sufficient biological information with respect to tumor
subtypes and clinical prognostic significance. Superset also provides high
reproducibility on survival analysis and accurate prediction for cancer
subtypes. | [
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Directed graphs have been widely used in Community Question Answering
services (CQAs) to model asymmetric relationships among different types of
nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is
an essential property of directed graphs, since it can play an important role
in downstream graph inference and analysis. Question difficulty and user
expertise follow the characteristic of asymmetric transitivity. Maintaining
such properties, while reducing the graph to a lower dimensional vector
embedding space, has been the focus of much recent research. In this paper, we
tackle the challenge of directed graph embedding with asymmetric transitivity
preservation and then leverage the proposed embedding method to solve a
fundamental task in CQAs: how to appropriately route and assign newly posted
questions to users with the suitable expertise and interest in CQAs. The
technique incorporates graph hierarchy and reachability information naturally
by relying on a non-linear transformation that operates on the core
reachability and implicit hierarchy within such graphs. Subsequently, the
methodology levers a factorization-based approach to generate two embedding
vectors for each node within the graph, to capture the asymmetric transitivity.
Extensive experiments show that our framework consistently and significantly
outperforms the state-of-the-art baselines on two diverse real-world tasks:
link prediction, and question difficulty estimation and expert finding in
online forums like Stack Exchange. Particularly, our framework can support
inductive embedding learning for newly posted questions (unseen nodes during
training), and therefore can properly route and assign these kinds of questions
to experts in CQAs. | [
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] |
In this paper we are interested in the prediction of preterm birth based on
diagnosis codes from longitudinal EHR. We formulate the prediction problem as a
supervised classification with noisy labels. Our base classifier is a Recurrent
Neural Network with an attention mechanism. We assume the availability of a
data subset with both noisy and clean labels. For the cohort definition, most
of the diagnosis codes on mothers' records related to pregnancy are ambiguous
for the definition of full-term and preterm classes. On the other hand,
diagnosis codes on babies' records provide fine-grained information on
prematurity. Due to data de-identification, the links between mothers and
babies are not available. We developed a heuristic based on admission and
discharge times to match babies to their mothers and hence enrich mothers'
records with additional information on delivery status. The obtained additional
dataset from the matching heuristic has noisy labels and was used to leverage
the training of the deep learning model. We propose an Alternating Loss
Correction (ALC) method to train deep models with both clean and noisy labels.
First, the label corruption matrix is estimated using the data subset with both
noisy and clean labels. Then it is used in the model as a dense output layer to
correct for the label noise. The network is alternately trained on epochs with
the clean dataset with a simple cross-entropy loss and on next epoch with the
noisy dataset and a loss corrected with the estimated corruption matrix. The
experiments for the prediction of preterm birth at 90 days before delivery
showed an improvement in performance compared with baseline and state
of-the-art methods. | [
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Programming a robot to deal with open-ended tasks remains a challenge, in
particular if the robot has to manipulate objects. Launching, grasping, pushing
or any other object interaction can be simulated but the corresponding models
are not reversible and the robot behavior thus cannot be directly deduced.
These behaviors are hard to learn without a demonstration as the search space
is large and the reward sparse. We propose a method to autonomously generate a
diverse repertoire of simple object interaction behaviors in simulation. Our
goal is to bootstrap a robot learning and development process with limited
information about what the robot has to achieve and how. This repertoire can be
exploited to solve different tasks in reality thanks to a proposed adaptation
method or could be used as a training set for data-hungry algorithms.
The proposed approach relies on the definition of a goal space and generates
a repertoire of trajectories to reach attainable goals, thus allowing the robot
to control this goal space. The repertoire is built with an off-the-shelf
simulation thanks to a quality diversity algorithm. The result is a set of
solutions tested in simulation only. It may result in two different problems:
(1) as the repertoire is discrete and finite, it may not contain the trajectory
to deal with a given situation or (2) some trajectories may lead to a behavior
in reality that differs from simulation because of a reality gap. We propose an
approach to deal with both issues by using a local linearization of the mapping
between the motion parameters and the observed effects. Furthermore, we present
an approach to update the existing solutions repertoire with the tests done on
the real robot. The approach has been validated on two different experiments on
the Baxter robot: a ball launching and a joystick manipulation tasks. | [
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] |
Humans make decisions and act alongside other humans to pursue both
short-term and long-term goals. As a result of ongoing progress in areas such
as computing science and automation, humans now also interact with non-human
agents of varying complexity as part of their day-to-day activities;
substantial work is being done to integrate increasingly intelligent machine
agents into human work and play. With increases in the cognitive, sensory, and
motor capacity of these agents, intelligent machinery for human assistance can
now reasonably be considered to engage in joint action with humans---i.e., two
or more agents adapting their behaviour and their understanding of each other
so as to progress in shared objectives or goals. The mechanisms, conditions,
and opportunities for skillful joint action in human-machine partnerships is of
great interest to multiple communities. Despite this, human-machine joint
action is as yet under-explored, especially in cases where a human and an
intelligent machine interact in a persistent way during the course of
real-time, daily-life experience. In this work, we contribute a virtual reality
environment wherein a human and an agent can adapt their predictions, their
actions, and their communication so as to pursue a simple foraging task. In a
case study with a single participant, we provide an example of human-agent
coordination and decision-making involving prediction learning on the part of
the human and the machine agent, and control learning on the part of the
machine agent wherein audio communication signals are used to cue its human
partner in service of acquiring shared reward. These comparisons suggest the
utility of studying human-machine coordination in a virtual reality
environment, and identify further research that will expand our understanding
of persistent human-machine joint action. | [
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] |
Machine learning (ML) used for static portable executable (PE) malware
detection typically employs per-file numerical feature vector representations
as input with one or more target labels during training. However, there is much
orthogonal information that can be gleaned from the \textit{context} in which
the file was seen. In this paper, we propose utilizing a static source of
contextual information -- the path of the PE file -- as an auxiliary input to
the classifier. While file paths are not malicious or benign in and of
themselves, they do provide valuable context for a malicious/benign
determination. Unlike dynamic contextual information, file paths are available
with little overhead and can seamlessly be integrated into a multi-view static
ML detector, yielding higher detection rates at very high throughput with
minimal infrastructural changes. Here we propose a multi-view neural network,
which takes feature vectors from PE file content as well as corresponding file
paths as inputs and outputs a detection score. To ensure realistic evaluation,
we use a dataset of approximately 10 million samples -- files and file paths
from user endpoints of an actual security vendor network. We then conduct an
interpretability analysis via LIME modeling to ensure that our classifier has
learned a sensible representation and see which parts of the file path most
contributed to change in the classifier's score. We find that our model learns
useful aspects of the file path for classification, while also learning
artifacts from customers testing the vendor's product, e.g., by downloading a
directory of malware samples each named as their hash. We prune these artifacts
from our test dataset and demonstrate reductions in false negative rate of
32.3% at a $10^{-3}$ false positive rate (FPR) and 33.1% at $10^{-4}$ FPR, over
a similar topology single input PE file content only model. | [
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] |
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings. | [
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] |
We focus on the important problem of emergency evacuation, which clearly
could benefit from reinforcement learning that has been largely unaddressed.
Emergency evacuation is a complex task which is difficult to solve with
reinforcement learning, since an emergency situation is highly dynamic, with a
lot of changing variables and complex constraints that makes it difficult to
train on. In this paper, we propose the first fire evacuation environment to
train reinforcement learning agents for evacuation planning. The environment is
modelled as a graph capturing the building structure. It consists of realistic
features like fire spread, uncertainty and bottlenecks. We have implemented the
environment in the OpenAI gym format, to facilitate future research. We also
propose a new reinforcement learning approach that entails pretraining the
network weights of a DQN based agents to incorporate information on the
shortest path to the exit. We achieved this by using tabular Q-learning to
learn the shortest path on the building model's graph. This information is
transferred to the network by deliberately overfitting it on the Q-matrix.
Then, the pretrained DQN model is trained on the fire evacuation environment to
generate the optimal evacuation path under time varying conditions. We perform
comparisons of the proposed approach with state-of-the-art reinforcement
learning algorithms like PPO, VPG, SARSA, A2C and ACKTR. The results show that
our method is able to outperform state-of-the-art models by a huge margin
including the original DQN based models. Finally, we test our model on a large
and complex real building consisting of 91 rooms, with the possibility to move
to any other room, hence giving 8281 actions. We use an attention based
mechanism to deal with large action spaces. Our model achieves near optimal
performance on the real world emergency environment. | [
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] |
In this work, we develop a novel framework to measure the similarity between
dynamic financial networks, i.e., time-varying financial networks.
Particularly, we explore whether the proposed similarity measure can be
employed to understand the structural evolution of the financial networks with
time. For a set of time-varying financial networks with each vertex
representing the individual time series of a different stock and each edge
between a pair of time series representing the absolute value of their Pearson
correlation, our start point is to compute the commute time matrix associated
with the weighted adjacency matrix of the network structures, where each
element of the matrix can be seen as the enhanced correlation value between
pairwise stocks. For each network, we show how the commute time matrix allows
us to identify a reliable set of dominant correlated time series as well as an
associated dominant probability distribution of the stock belonging to this
set. Furthermore, we represent each original network as a discrete dominant
Shannon entropy time series computed from the dominant probability
distribution. With the dominant entropy time series for each pair of financial
networks to hand, we develop a similarity measure based on the classical
dynamic time warping framework, for analyzing the financial time-varying
networks. We show that the proposed similarity measure is positive definite and
thus corresponds to a kernel measure on graphs. The proposed kernel bridges the
gap between graph kernels and the classical dynamic time warping framework for
multiple financial time series analysis. Experiments on time-varying networks
extracted through New York Stock Exchange (NYSE) database demonstrate the
effectiveness of the proposed approach. | [
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] |
A deep neural network (DNN) classifier represents a model owner's
intellectual property as training a DNN classifier often requires lots of
resource. Watermarking was recently proposed to protect the intellectual
property of DNN classifiers. However, watermarking suffers from a key
limitation: it sacrifices the utility/accuracy of the model owner's classifier
because it tampers the classifier's training or fine-tuning process. In this
work, we propose IPGuard, the first method to protect intellectual property of
DNN classifiers that provably incurs no accuracy loss for the classifiers. Our
key observation is that a DNN classifier can be uniquely represented by its
classification boundary. Based on this observation, IPGuard extracts some data
points near the classification boundary of the model owner's classifier and
uses them to fingerprint the classifier. A DNN classifier is said to be a
pirated version of the model owner's classifier if they predict the same labels
for most fingerprinting data points. IPGuard is qualitatively different from
watermarking. Specifically, IPGuard extracts fingerprinting data points near
the classification boundary of a classifier that is already trained, while
watermarking embeds watermarks into a classifier during its training or
fine-tuning process. We extensively evaluate IPGuard on CIFAR-10, CIFAR-100,
and ImageNet datasets. Our results show that IPGuard can robustly identify
post-processed versions of the model owner's classifier as pirated versions of
the classifier, and IPGuard can identify classifiers, which are not the model
owner's classifier nor its post-processed versions, as non-pirated versions of
the classifier. | [
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] |
Once users have shared their data online, it is generally difficult for them
to revoke access and ask for the data to be deleted. Machine learning (ML)
exacerbates this problem because any model trained with said data may have
memorized it, putting users at risk of a successful privacy attack exposing
their information. Yet, having models unlearn is notoriously difficult. We
introduce SISA training, a framework that expedites the unlearning process by
strategically limiting the influence of a data point in the training procedure.
While our framework is applicable to any learning algorithm, it is designed to
achieve the largest improvements for stateful algorithms like stochastic
gradient descent for deep neural networks. SISA training reduces the
computational overhead associated with unlearning, even in the worst-case
setting where unlearning requests are made uniformly across the training set.
In some cases, the service provider may have a prior on the distribution of
unlearning requests that will be issued by users. We may take this prior into
account to partition and order data accordingly, and further decrease overhead
from unlearning. Our evaluation spans several datasets from different domains,
with corresponding motivations for unlearning. Under no distributional
assumptions, for simple learning tasks, we observe that SISA training improves
time to unlearn points from the Purchase dataset by 4.63x, and 2.45x for the
SVHN dataset, over retraining from scratch. SISA training also provides a
speed-up of 1.36x in retraining for complex learning tasks such as ImageNet
classification; aided by transfer learning, this results in a small degradation
in accuracy. Our work contributes to practical data governance in machine
unlearning. | [
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Companies regularly spend millions of dollars producing electronically-stored
documents in legal matters. Recently, parties on both sides of the 'legal
aisle' are accepting the use of machine learning techniques like text
classification to cull massive volumes of data and to identify responsive
documents for use in these matters. While text classification is regularly used
to reduce the discovery costs in legal matters, it also faces a peculiar
perception challenge: amongst lawyers, this technology is sometimes looked upon
as a "black box", little information provided for attorneys to understand why
documents are classified as responsive. In recent years, a group of AI and ML
researchers have been actively researching Explainable AI, in which actions or
decisions are human understandable. In legal document review scenarios, a
document can be identified as responsive, if one or more of its text snippets
are deemed responsive. In these scenarios, if text classification can be used
to locate these snippets, then attorneys could easily evaluate the model's
classification decision. When deployed with defined and explainable results,
text classification can drastically enhance overall quality and speed of the
review process by reducing the review time. Moreover, explainable predictive
coding provides lawyers with greater confidence in the results of that
supervised learning task. This paper describes a framework for explainable text
classification as a valuable tool in legal services: for enhancing the quality
and efficiency of legal document review and for assisting in locating
responsive snippets within responsive documents. This framework has been
implemented in our legal analytics product, which has been used in hundreds of
legal matters. We also report our experimental results using the data from an
actual legal matter that used this type of document review. | [
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Traffic congestion at a signalized intersection greatly reduces the travel
time reliability in urban areas. Adaptive signal control system (ASCS) is the
most advanced traffic signal technology that regulates the signal phasing and
timings considering the patterns in real-time in order to reduce congestion.
Real-time prediction of queue lengths can be used to adjust the phasing and
timings for different movements at an intersection with ASCS. The accuracy of
the prediction varies based on the factors, such as the stochastic nature of
the vehicle arrival rates, time of the day, weather and driver characteristics.
In addition, accurate prediction for multilane, undersaturated and saturated
traffic scenarios is challenging. Thus, the objective of this study is to
develop queue length prediction models for signalized intersections that can be
leveraged by ASCS using four variations of Grey systems: (i) the first order
single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error
corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier
error corrections. The efficacy of the GM is that they facilitate fast
processing; as these models do not require a large amount of data; as would be
needed in artificial intelligence models; and they are able to adapt to
stochastic changes, unlike statistical models. We have conducted a case study
using queue length data from five intersections with ASCS on a calibrated
roadway network in Lexington, South Carolina. GM were compared with linear,
nonlinear time series models, and long short-term memory (LSTM) neural network.
Based on our analyses, we found that EGVM reduces the prediction error over
closest competing models (i.e., LSTM and time series models) in predicting
average and maximum queue lengths by 40% and 42%, respectively, in terms of
Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean
Absolute Error. | [
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] |
Graph representation learning has achieved a remarkable success in many
graph-based applications, such as node classification, link prediction, and
community detection. These models are usually designed to preserve the vertex
information at different granularity and reduce the problems in discrete space
to some machine learning tasks in continuous space. However, regardless of the
fruitful progress, for some kind of graph applications, such as graph
compression and edge partition, it is very hard to reduce them to some graph
representation learning tasks. Moreover, these problems are closely related to
reformulating a global layout for a specific graph, which is an important
NP-hard combinatorial optimization problem: graph ordering. In this paper, we
propose to attack the graph ordering problem behind such applications by a
novel learning approach. Distinguished from greedy algorithms based on
predefined heuristics, we propose a neural network model: Deep Order Network
(DON) to capture the hidden locality structure from partial vertex order sets.
Supervised by sampled partial order, DON has the ability to infer unseen
combinations. Furthermore, to alleviate the combinatorial explosion in the
training space of DON and make the efficient partial vertex order sampling , we
employ a reinforcement learning model: the Policy Network, to adjust the
partial order sampling probabilities during the training phase of DON
automatically. To this end, the Policy Network can improve the training
efficiency and guide DON to evolve towards a more effective model
automatically. Comprehensive experiments on both synthetic and real data
validate that DON-RL outperforms the current state-of-the-art heuristic
algorithm consistently. Two case studies on graph compression and edge
partitioning demonstrate the potential power of DON-RL in real applications. | [
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] |
The wide adoption of Machine Learning technologies has created a rapidly
growing demand for people who can train ML models. Some advocated the term
"machine teacher" to refer to the role of people who inject domain knowledge
into ML models. One promising learning paradigm is Active Learning (AL), by
which the model intelligently selects instances to query the machine teacher
for labels. However, in current AL settings, the human-AI interface remains
minimal and opaque. We begin considering AI explanations as a core element of
the human-AI interface for teaching machines. When a human student learns, it
is a common pattern to present one's own reasoning and solicit feedback from
the teacher. When a ML model learns and still makes mistakes, the human teacher
should be able to understand the reasoning underlying the mistakes. When the
model matures, the machine teacher should be able to recognize its progress in
order to trust and feel confident about their teaching outcome. Toward this
vision, we propose a novel paradigm of explainable active learning (XAL), by
introducing techniques from the recently surging field of explainable AI (XAI)
into an AL setting. We conducted an empirical study comparing the model
learning outcomes, feedback content and experience with XAL, to that of
traditional AL and coactive learning (providing the model's prediction without
the explanation). Our study shows benefits of AI explanation as interfaces for
machine teaching--supporting trust calibration and enabling rich forms of
teaching feedback, and potential drawbacks--anchoring effect with the model
judgment and cognitive workload. Our study also reveals important individual
factors that mediate a machine teacher's reception to AI explanations,
including task knowledge, AI experience and need for cognition. By reflecting
on the results, we suggest future directions and design implications for XAL. | [
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Inspired by recent breakthroughs in predictive modeling, practitioners in
both industry and government have turned to machine learning with hopes of
operationalizing predictions to drive automated decisions. Unfortunately, many
social desiderata concerning consequential decisions, such as justice or
fairness, have no natural formulation within a purely predictive framework. In
efforts to mitigate these problems, researchers have proposed a variety of
metrics for quantifying deviations from various statistical parities that we
might expect to observe in a fair world and offered a variety of algorithms in
attempts to satisfy subsets of these parities or to trade off the degree to
which they are satisfied against utility. In this paper, we connect this
approach to \emph{fair machine learning} to the literature on ideal and
non-ideal methodological approaches in political philosophy. The ideal approach
requires positing the principles according to which a just world would operate.
In the most straightforward application of ideal theory, one supports a
proposed policy by arguing that it closes a discrepancy between the real and
the perfectly just world. However, by failing to account for the mechanisms by
which our non-ideal world arose, the responsibilities of various
decision-makers, and the impacts of proposed policies, naive applications of
ideal thinking can lead to misguided interventions. In this paper, we
demonstrate a connection between the fair machine learning literature and the
ideal approach in political philosophy, and argue that the increasingly
apparent shortcomings of proposed fair machine learning algorithms reflect
broader troubles faced by the ideal approach. We conclude with a critical
discussion of the harms of misguided solutions, a reinterpretation of
impossibility results, and directions for future research. | [
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] |
This paper discusses the problem of causal query in observational data with
hidden variables, with the aim of seeking the change of an outcome when
"manipulating" a variable while given a set of plausible confounding variables
which affect the manipulated variable and the outcome. Such an "experiment on
data" to estimate the causal effect of the manipulated variable is useful for
validating an experiment design using historical data or for exploring
confounders when studying a new relationship. However, existing data-driven
methods for causal effect estimation face some major challenges, including poor
scalability with high dimensional data, low estimation accuracy due to
heuristics used by the global causal structure learning algorithms, and the
assumption of causal sufficiency when hidden variables are inevitable in data.
In this paper, we develop a theorem for using local search to find a superset
of the adjustment (or confounding) variables for causal effect estimation from
observational data under a realistic pretreatment assumption. The theorem
ensures that the unbiased estimate of causal effect is included in the set of
causal effects estimated by the superset of adjustment variables. Based on the
developed theorem, we propose a data-driven algorithm for causal query.
Experiments show that the proposed algorithm is faster and produces better
causal effect estimation than an existing data-driven causal effect estimation
method with hidden variables. The causal effects estimated by the proposed
algorithm are as accurate as those by the state-of-the-art methods using domain
knowledge. | [
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] |
In the last decade, two paradigm shifts have reshaped the software industry -
the move from boxed products to services and the widespread adoption of cloud
computing. This has had a huge impact on the software development life cycle
and the DevOps processes. Particularly, incident management has become critical
for developing and operating large-scale services. Incidents are created to
ensure timely communication of service issues and, also, their resolution.
Prior work on incident management has been heavily focused on the challenges
with incident triaging and de-duplication. In this work, we address the
fundamental problem of structured knowledge extraction from service incidents.
We have built SoftNER, a framework for unsupervised knowledge extraction from
service incidents. We frame the knowledge extraction problem as a Named-entity
Recognition task for extracting factual information. SoftNER leverages
structural patterns like key,value pairs and tables for bootstrapping the
training data. Further, we build a novel multi-task learning based BiLSTM-CRF
model which leverages not just the semantic context but also the data-types for
named-entity extraction. We have deployed SoftNER at Microsoft, a major cloud
service provider and have evaluated it on more than 2 months of cloud
incidents. We show that the unsupervised machine learning based approach has a
high precision of 0.96. Our multi-task learning based deep learning model also
outperforms the state of the art NER models. Lastly, using the knowledge
extracted by SoftNER we are able to build significantly more accurate models
for important downstream tasks like incident triaging. | [
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General-purpose trajectory planning algorithms for automated driving utilize
complex reward functions to perform a combined optimization of strategic,
behavioral, and kinematic features. The specification and tuning of a single
reward function is a tedious task and does not generalize over a large set of
traffic situations. Deep learning approaches based on path integral inverse
reinforcement learning have been successfully applied to predict local
situation-dependent reward functions using features of a set of sampled driving
policies. Sample-based trajectory planning algorithms are able to approximate a
spatio-temporal subspace of feasible driving policies that can be used to
encode the context of a situation. However, the interaction with dynamic
objects requires an extended planning horizon, which depends on sequential
context modeling. In this work, we are concerned with the sequential reward
prediction over an extended time horizon. We present a neural network
architecture that uses a policy attention mechanism to generate a
low-dimensional context vector by concentrating on trajectories with a
human-like driving style. Apart from this, we propose a temporal attention
mechanism to identify context switches and allow for stable adaptation of
rewards. We evaluate our results on complex simulated driving situations,
including other moving vehicles. Our evaluation shows that our policy attention
mechanism learns to focus on collision-free policies in the configuration
space. Furthermore, the temporal attention mechanism learns persistent
interaction with other vehicles over an extended planning horizon. | [
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] |
A primary indicator of success in the fight against COVID-19 is avoiding
stress on critical care infrastructure and services (CCIS). However, CCIS will
likely remain stressed until sustained herd immunity is built. There are also
secondary considerations for success: mitigating economic damage; curbing the
spread of misinformation, improving morale, and preserving a sense of control;
building global trust for diplomacy, trade and travel; and restoring
reliability and normalcy to day-to-day life, among others. We envision
technology plays a pivotal role. Here, we focus on the effective use of readily
available technology to improve the primary and secondary success criteria for
the fight against SARS-CoV-2. In a multifaceted technology approach, we start
with effective technology use for remote patient monitoring (RPM) of COVID-19
with the following objectives:
1. Deploy readily available technology for continuous real-time remote
monitoring of patient vitals with the help of biosensors on a large scale.
2. Effective and safe remote large-scale communitywide care of low-severity
cases as a buffer against surges in COVID-19 hospitalizations to reduce strain
on critical care services and emergency hospitals.
3. Improve the patient, their family, and their community's sense of control
and morale.
4. Propose a clear technology and medical definition of remote patient
monitoring for COVID-19 to address an urgent technology need; address
obfuscated, narrow, and erroneous information and provide examples; and urge
publishers to be clear and complete in their disclosures.
5. Leverage the cloud-based distributed cognitive RPM platform for community
leaders and decision makers to enable planning and resource management,
pandemic research, damage prevention and containment, and receiving feedback on
strategies and executions. | [
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] |
Decision making in crucial applications such as lending, hiring, and college
admissions has witnessed increasing use of algorithmic models and techniques as
a result of a confluence of factors such as ubiquitous connectivity, ability to
collect, aggregate, and process large amounts of fine-grained data using cloud
computing, and ease of access to applying sophisticated machine learning
models. Quite often, such applications are powered by search and recommendation
systems, which in turn make use of personalized ranking algorithms. At the same
time, there is increasing awareness about the ethical and legal challenges
posed by the use of such data-driven systems. Researchers and practitioners
from different disciplines have recently highlighted the potential for such
systems to discriminate against certain population groups, due to biases in the
datasets utilized for learning their underlying recommendation models. We
present a study of fairness in online personalization settings involving the
ranking of individuals. Starting from a fair warm-start machine-learned model,
we first demonstrate that online personalization can cause the model to learn
to act in an unfair manner if the user is biased in his/her responses. For this
purpose, we construct a stylized model for generating training data with
potentially biased features as well as potentially biased labels and quantify
the extent of bias that is learned by the model when the user responds in a
biased manner as in many real-world scenarios. We then formulate the problem of
learning personalized models under fairness constraints and present a
regularization based approach for mitigating biases in machine learning. We
demonstrate the efficacy of our approach through extensive simulations with
different parameter settings. Code:
https://github.com/groshanlal/Fairness-Aware-Online-Personalization | [
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Multi-stage cascade architecture exists widely in many industrial systems
such as recommender systems and online advertising, which often consists of
sequential modules including matching, pre-ranking, ranking, etc. For a long
time, it is believed pre-ranking is just a simplified version of the ranking
module, considering the larger size of the candidate set to be ranked. Thus,
efforts are made mostly on simplifying ranking model to handle the explosion of
computing power for online inference. In this paper, we rethink the challenge
of the pre-ranking system from an algorithm-system co-design view. Instead of
saving computing power with restriction of model architecture which causes loss
of model performance, here we design a new pre-ranking system by joint
optimization of both the pre-ranking model and the computing power it costs. We
name it COLD (Computing power cost-aware Online and Lightweight Deep
pre-ranking system). COLD beats SOTA in three folds: (i) an arbitrary deep
model with cross features can be applied in COLD under a constraint of
controllable computing power cost. (ii) computing power cost is explicitly
reduced by applying optimization tricks for inference acceleration. This
further brings space for COLD to apply more complex deep models to reach better
performance. (iii) COLD model works in an online learning and severing manner,
bringing it excellent ability to handle the challenge of the data distribution
shift. Meanwhile, the fully online pre-ranking system of COLD provides us with
a flexible infrastructure that supports efficient new model developing and
online A/B testing.Since 2019, COLD has been deployed in almost all products
involving the pre-ranking module in the display advertising system in Alibaba,
bringing significant improvements. | [
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] |
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of
the pandemic. However, vaccine is also expected to be a limited resource. An
optimal allocation strategy, especially in countries with access inequities and
temporal separation of hot-spots, might be an effective way of halting the
disease spread. We approach this problem by proposing a novel pipeline VacSIM
that dovetails Deep Reinforcement Learning models into a Contextual Bandits
approach for optimizing the distribution of COVID-19 vaccine. Whereas the
Reinforcement Learning models suggest better actions and rewards, Contextual
Bandits allow online modifications that may need to be implemented on a
day-to-day basis in the real world scenario. We evaluate this framework against
a naive allocation approach of distributing vaccine proportional to the
incidence of COVID-19 cases in five different States across India (Assam,
Delhi, Jharkhand, Maharashtra and Nagaland) and demonstrate up to 9039
potential infections prevented and a significant increase in the efficacy of
limiting the spread over a period of 45 days through the VacSIM approach. Our
models and the platform are extensible to all states of India and potentially
across the globe. We also propose novel evaluation strategies including
standard compartmental model-based projections and a causality-preserving
evaluation of our model. Since all models carry assumptions that may need to be
tested in various contexts, we open source our model VacSIM and contribute a
new reinforcement learning environment compatible with OpenAI gym to make it
extensible for real-world applications across the globe.
(http://vacsim.tavlab.iiitd.edu.in:8000/). | [
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] |
Contact-tracing is an essential tool in order to mitigate the impact of
pandemic such as the COVID-19. In order to achieve efficient and scalable
contact-tracing in real time, digital devices can play an important role. While
a lot of attention has been paid to analyzing the privacy and ethical risks of
the associated mobile applications, so far much less research has been devoted
to optimizing their performance and assessing their impact on the mitigation of
the epidemic. We develop Bayesian inference methods to estimate the risk that
an individual is infected. This inference is based on the list of his recent
contacts and their own risk levels, as well as personal information such as
results of tests or presence of syndromes. We propose to use probabilistic risk
estimation in order to optimize testing and quarantining strategies for the
control of an epidemic. Our results show that in some range of epidemic
spreading (typically when the manual tracing of all contacts of infected people
becomes practically impossible, but before the fraction of infected people
reaches the scale where a lock-down becomes unavoidable), this inference of
individuals at risk could be an efficient way to mitigate the epidemic. Our
approaches translate into fully distributed algorithms that only require
communication between individuals who have recently been in contact. Such
communication may be encrypted and anonymized and thus compatible with privacy
preserving standards. We conclude that probabilistic risk estimation is capable
to enhance performance of digital contact tracing and should be considered in
the currently developed mobile applications. | [
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The connectivity aspect of connected autonomous vehicles (CAV) is beneficial
because it facilitates dissemination of traffic-related information to vehicles
through Vehicle-to-External (V2X) communication. Onboard sensing equipment
including LiDAR and camera can reasonably characterize the traffic environment
in the immediate locality of the CAV. However, their performance is limited by
their sensor range (SR). On the other hand, longer-range information is helpful
for characterizing imminent conditions downstream. By contemporaneously
coalescing the short- and long-range information, the CAV can construct
comprehensively its surrounding environment and thereby facilitate informed,
safe, and effective movement planning in the short-term (local decisions
including lane change) and long-term (route choice). In this paper, we describe
a Deep Reinforcement Learning based approach that integrates the data collected
through sensing and connectivity capabilities from other vehicles located in
the proximity of the CAV and from those located further downstream, and we use
the fused data to guide lane changing, a specific context of CAV operations. In
addition, recognizing the importance of the connectivity range (CR) to the
performance of not only the algorithm but also of the vehicle in the actual
driving environment, the paper carried out a case study. The case study
demonstrates the application of the proposed algorithm and duly identifies the
appropriate CR for each level of prevailing traffic density. It is expected
that implementation of the algorithm in CAVs can enhance the safety and
mobility associated with CAV driving operations. From a general perspective,
its implementation can provide guidance to connectivity equipment manufacturers
and CAV operators, regarding the default CR settings for CAVs or the
recommended CR setting in a given traffic environment. | [
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] |
Machine Learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science
and engineering domains, like geoscience, climate science, and health science.
In these domains, users need to perform comprehensive data analyses combining
scientific data and ML models to provide for critical requirements, such as
reproducibility, model explainability, and experiment data understanding.
However, scientific ML is multidisciplinary, heterogeneous, and affected by the
physical constraints of the domain, making such analyses even more challenging.
In this work, we leverage workflow provenance techniques to build a holistic
view to support the lifecycle of scientific ML. We contribute with (i)
characterization of the lifecycle and taxonomy for data analyses; (ii) design
principles to build this view, with a W3C PROV compliant data representation
and a reference system architecture; and (iii) lessons learned after an
evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946
GPUs. The experiments show that the principles enable queries that integrate
domain semantics with ML models while keeping low overhead (<1%), high
scalability, and an order of magnitude of query acceleration under certain
workloads against without our representation. | [
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] |
Artificial Intelligence is providing astonishing results, with medicine being
one of its favourite playgrounds. In a few decades, computers may be capable of
formulating diagnoses and choosing the correct treatment, while robots may
perform surgical operations, and conversational agents could interact with
patients as virtual coaches. Machine Learning and, in particular, Deep Neural
Networks are behind this revolution. In this scenario, important decisions will
be controlled by standalone machines that have learned predictive models from
provided data. Among the most challenging targets of interest in medicine are
cancer diagnosis and therapies but, to start this revolution, software tools
need to be adapted to cover the new requirements. In this sense, learning tools
are becoming a commodity in Python and Matlab libraries, just to name two, but
to exploit all their possibilities, it is essential to fully understand how
models are interpreted and which models are more interpretable than others. In
this survey, we analyse current machine learning models, frameworks, databases
and other related tools as applied to medicine - specifically, to cancer
research - and we discuss their interpretability, performance and the necessary
input data. From the evidence available, ANN, LR and SVM have been observed to
be the preferred models. Besides, CNNs, supported by the rapid development of
GPUs and tensor-oriented programming libraries, are gaining in importance.
However, the interpretability of results by doctors is rarely considered which
is a factor that needs to be improved. We therefore consider this study to be a
timely contribution to the issue. | [
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Optimism about the Internet's potential to bring the world together has been
tempered by concerns about its role in inflaming the 'culture wars'. Via mass
selection into like-minded groups, online society may be becoming more
fragmented and polarized, particularly with respect to partisan differences.
However, our ability to measure the social makeup of online communities, and in
turn understand the social organization of online platforms, is limited by the
pseudonymous, unstructured, and large-scale nature of digital discussion. We
develop a neural embedding methodology to quantify the positioning of online
communities along social dimensions by leveraging large-scale patterns of
aggregate behaviour. Applying our methodology to 5.1B Reddit comments made in
10K communities over 14 years, we measure how the macroscale community
structure is organized with respect to age, gender, and U.S. political
partisanship. Examining political content, we find Reddit underwent a
significant polarization event around the 2016 U.S. presidential election, and
remained highly polarized for years afterward. Contrary to conventional wisdom,
however, individual-level polarization is rare; the system-level shift in 2016
was disproportionately driven by the arrival of new and newly political users.
Political polarization on Reddit is unrelated to previous activity on the
platform, and is instead temporally aligned with external events. We also
observe a stark ideological asymmetry, with the sharp increase in 2016 being
entirely attributable to changes in right-wing activity. Our methodology is
broadly applicable to the study of online interaction, and our findings have
implications for the design of online platforms, understanding the social
contexts of online behaviour, and quantifying the dynamics and mechanisms of
online polarization. | [
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] |
Objective: We used deep convolutional neural networks (DCNNs) to classify
electroencephalography (EEG) signals in a steady-state visually evoked
potentials (SSVEP) based single-channel brain-computer interface (BCI), which
does not require calibration on the user.
Methods: EEG signals were converted to spectrograms and served as input to
train DCNNs using the transfer learning technique. We also modified and applied
a data augmentation method, SpecAugment, generally employed for speech
recognition. Furthermore, for comparison purposes, we classified the SSVEP
dataset using Support-vector machines (SVMs) and Filter Bank canonical
correlation analysis (FBCCA).
Results: Excluding the evaluated user's data from the fine-tuning process, we
reached 82.2% mean test accuracy and 0.825 mean F1-Score on 35 subjects from an
open dataset, using a small data length (0.5 s), only one electrode (Oz) and
the DCNN with transfer learning, window slicing (WS) and SpecAugment's time
masks.
Conclusion: The DCNN results surpassed SVM and FBCCA performances, using a
single electrode and a small data length. Transfer learning provided minimal
accuracy change, but made training faster. SpecAugment created a small
performance improvement and was successfully combined with WS, yielding higher
accuracies.
Significance: We present a new methodology to solve the problem of SSVEP
classification using DCNNs. We also modified a speech recognition data
augmentation technique and applied it to the context of BCIs. The presented
methodology surpassed performances obtained with FBCCA and SVMs (more
traditional SSVEP classification methods) in BCIs with small data lengths and
one electrode. This type of BCI can be used to develop small and fast systems. | [
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] |
The thesis explores the role machine learning methods play in creating
intuitive computational models of neural processing. Combined with
interpretability techniques, machine learning could replace human modeler and
shift the focus of human effort to extracting the knowledge from the ready-made
models and articulating that knowledge into intuitive descroptions of reality.
This perspective makes the case in favor of the larger role that exploratory
and data-driven approach to computational neuroscience could play while
coexisting alongside the traditional hypothesis-driven approach.
We exemplify the proposed approach in the context of the knowledge
representation taxonomy with three research projects that employ
interpretability techniques on top of machine learning methods at three
different levels of neural organization. The first study (Chapter 3) explores
feature importance analysis of a random forest decoder trained on intracerebral
recordings from 100 human subjects to identify spectrotemporal signatures that
characterize local neural activity during the task of visual categorization.
The second study (Chapter 4) employs representation similarity analysis to
compare the neural responses of the areas along the ventral stream with the
activations of the layers of a deep convolutional neural network. The third
study (Chapter 5) proposes a method that allows test subjects to visually
explore the state representation of their neural signal in real time. This is
achieved by using a topology-preserving dimensionality reduction technique that
allows to transform the neural data from the multidimensional representation
used by the computer into a two-dimensional representation a human can grasp.
The approach, the taxonomy, and the examples, present a strong case for the
applicability of machine learning methods to automatic knowledge discovery in
neuroscience. | [
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A robust body of evidence demonstrates the adverse effects of implicit bias
in various contexts--from hiring to health care. The Rooney Rule is an
intervention developed to counter implicit bias and has been implemented in the
private and public sectors. The Rooney Rule requires that a selection panel
include at least one candidate from an underrepresented group in their
shortlist of candidates. Recently, Kleinberg and Raghavan proposed a model of
implicit bias and studied the effectiveness of the Rooney Rule when applied to
a single selection decision. However, selection decisions often occur
repeatedly over time. Further, it has been observed that, given consistent
counterstereotypical feedback, implicit biases against underrepresented
candidates can change.
We consider a model of how a selection panel's implicit bias changes over
time given their hiring decisions either with or without the Rooney Rule in
place. Our main result is that, when the panel is constrained by the Rooney
Rule, their implicit bias roughly reduces at a rate that is the inverse of the
size of the shortlist--independent of the number of candidates, whereas without
the Rooney Rule, the rate is inversely proportional to the number of
candidates. Thus, when the number of candidates is much larger than the size of
the shortlist, the Rooney Rule enables a faster reduction in implicit bias,
providing an additional reason in favor of using it as a strategy to mitigate
implicit bias. Towards empirically evaluating the long-term effect of the
Rooney Rule in repeated selection decisions, we conduct an iterative candidate
selection experiment on Amazon MTurk. We observe that, indeed, decision-makers
subject to the Rooney Rule select more minority candidates in addition to those
required by the rule itself than they would if no rule is in effect, and do so
without considerably decreasing the utility of candidates selected. | [
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] |
Learning from diverse offline datasets is a promising path towards learning
general purpose robotic agents. However, a core challenge in this paradigm lies
in collecting large amounts of meaningful data, while not depending on a human
in the loop for data collection. One way to address this challenge is through
task-agnostic exploration, where an agent attempts to explore without a
task-specific reward function, and collect data that can be useful for any
downstream task. While these approaches have shown some promise in simple
domains, they often struggle to explore the relevant regions of the state space
in more challenging settings, such as vision based robotic manipulation. This
challenge stems from an objective that encourages exploring everything in a
potentially vast state space. To mitigate this challenge, we propose to focus
exploration on the important parts of the state space using weak human
supervision. Concretely, we propose an exploration technique, Batch Exploration
with Examples (BEE), that explores relevant regions of the state-space, guided
by a modest number of human provided images of important states. These human
provided images only need to be collected once at the beginning of data
collection and can be collected in a matter of minutes, allowing us to scalably
collect diverse datasets, which can then be combined with any batch RL
algorithm. We find that BEE is able to tackle challenging vision-based
manipulation tasks both in simulation and on a real Franka robot, and observe
that compared to task-agnostic and weakly-supervised exploration techniques, it
(1) interacts more than twice as often with relevant objects, and (2) improves
downstream task performance when used in conjunction with offline RL. | [
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] |
In spite of several claims stating that some models are more interpretable
than others -- e.g., "linear models are more interpretable than deep neural
networks" -- we still lack a principled notion of interpretability to formally
compare among different classes of models. We make a step towards such a notion
by studying whether folklore interpretability claims have a correlate in terms
of computational complexity theory. We focus on local post-hoc explainability
queries that, intuitively, attempt to answer why individual inputs are
classified in a certain way by a given model. In a nutshell, we say that a
class $\mathcal{C}_1$ of models is more interpretable than another class
$\mathcal{C}_2$, if the computational complexity of answering post-hoc queries
for models in $\mathcal{C}_2$ is higher than for those in $\mathcal{C}_1$. We
prove that this notion provides a good theoretical counterpart to current
beliefs on the interpretability of models; in particular, we show that under
our definition and assuming standard complexity-theoretical assumptions (such
as P$\neq$NP), both linear and tree-based models are strictly more
interpretable than neural networks. Our complexity analysis, however, does not
provide a clear-cut difference between linear and tree-based models, as we
obtain different results depending on the particular post-hoc explanations
considered. Finally, by applying a finer complexity analysis based on
parameterized complexity, we are able to prove a theoretical result suggesting
that shallow neural networks are more interpretable than deeper ones. | [
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In a Big Data setting computing the dominant SVD factors is restrictive due
to the main memory requirements. Recently introduced streaming Randomized SVD
schemes work under the restrictive assumption that the singular value spectrum
of the data has exponential decay. This is seldom true for any practical data.
Although these methods are claimed to be applicable to scientific computations
due to associated tail-energy error bounds, the approximation errors in the
singular vectors and values are high when the aforementioned assumption does
not hold. Furthermore from a practical perspective, oversampling can still be
memory intensive or worse can exceed the feature dimension of the data. To
address these issues, we present Range-Net as an alternative to randomized SVD
that satisfies the tail-energy lower bound given by Eckart-Young-Mirsky (EYM)
theorem. Range-Net is a deterministic two-stage neural optimization approach
with random initialization, where the main memory requirement depends
explicitly on the feature dimension and desired rank, independent of the sample
dimension. The data samples are read in a streaming setting with the network
minimization problem converging to the desired rank-r approximation. Range-Net
is fully interpretable where all the network outputs and weights have a
specific meaning. We provide theoretical guarantees that Range-Net extracted
SVD factors satisfy EYM tail-energy lower bound at machine precision. Our
numerical experiments on real data at various scales confirms this bound. A
comparison against the state of the art streaming Randomized SVD shows that
Range-Net accuracy is better by six orders of magnitude while being memory
efficient. | [
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] |
Along with the blooming of AI and Machine Learning-based applications and
services, data privacy and security have become a critical challenge.
Conventionally, data is collected and aggregated in a data centre on which
machine learning models are trained. This centralised approach has induced
severe privacy risks to personal data leakage, misuse, and abuse. Furthermore,
in the era of the Internet of Things and big data in which data is essentially
distributed, transferring a vast amount of data to a data centre for processing
seems to be a cumbersome solution. This is not only because of the difficulties
in transferring and sharing data across data sources but also the challenges on
complying with rigorous data protection regulations and complicated
administrative procedures such as the EU General Data Protection Regulation
(GDPR). In this respect, Federated learning (FL) emerges as a prospective
solution that facilitates distributed collaborative learning without disclosing
original training data whilst naturally complying with the GDPR. Recent
research has demonstrated that retaining data and computation on-device in FL
is not sufficient enough for privacy-guarantee. This is because ML model
parameters exchanged between parties in an FL system still conceal sensitive
information, which can be exploited in some privacy attacks. Therefore, FL
systems shall be empowered by efficient privacy-preserving techniques to comply
with the GDPR. This article is dedicated to surveying on the state-of-the-art
privacy-preserving techniques which can be employed in FL in a systematic
fashion, as well as how these techniques mitigate data security and privacy
risks. Furthermore, we provide insights into the challenges along with
prospective approaches following the GDPR regulatory guidelines that an FL
system shall implement to comply with the GDPR. | [
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] |
The focus of this paper is on stochastic variational inequalities (VI) under
Markovian noise. A prominent application of our algorithmic developments is the
stochastic policy evaluation problem in reinforcement learning. Prior
investigations in the literature focused on temporal difference (TD) learning
by employing nonsmooth finite time analysis motivated by stochastic subgradient
descent leading to certain limitations. These encompass the requirement of
analyzing a modified TD algorithm that involves projection to an a-priori
defined Euclidean ball, achieving a non-optimal convergence rate and no clear
way of deriving the beneficial effects of parallel implementation. Our approach
remedies these shortcomings in the broader context of stochastic VIs and in
particular when it comes to stochastic policy evaluation. We developed a
variety of simple TD learning type algorithms motivated by its original version
that maintain its simplicity, while offering distinct advantages from a
non-asymptotic analysis point of view. We first provide an improved analysis of
the standard TD algorithm that can benefit from parallel implementation. Then
we present versions of a conditional TD algorithm (CTD), that involves periodic
updates of the stochastic iterates, which reduce the bias and therefore exhibit
improved iteration complexity. This brings us to the fast TD (FTD) algorithm
which combines elements of CTD and the stochastic operator extrapolation method
of the companion paper. For a novel index resetting policy FTD exhibits the
best known convergence rate. We also devised a robust version of the algorithm
that is particularly suitable for discounting factors close to 1. | [
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Recently, convex nested stochastic composite optimization (NSCO) has received
considerable attention for its applications in reinforcement learning and
risk-averse optimization. The current NSCO algorithms have worse stochastic
oracle complexities, by orders of magnitude, than those for simpler stochastic
composite optimization problems (e.g., sum of smooth and nonsmooth functions)
without the nested structure. Moreover, they require all outer-layer functions
to be smooth, which is not satisfied by some important applications. These
discrepancies prompt us to ask: ``does the nested composition make stochastic
optimization more difficult in terms of the order of oracle complexity?" In
this paper, we answer the question by developing order-optimal algorithms for
the convex NSCO problem constructed from an arbitrary composition of smooth,
structured non-smooth and general non-smooth layer functions. When all
outer-layer functions are smooth, we propose a stochastic sequential dual (SSD)
method to achieve an oracle complexity of $\mathcal{O}(1/\epsilon^2)$
($\mathcal{O}(1/\epsilon)$) when the problem is non-strongly (strongly) convex.
When there exists some structured non-smooth or general non-smooth outer-layer
function, we propose a nonsmooth stochastic sequential dual (nSSD) method to
achieve an oracle complexity of $\mathcal{O}(1/\epsilon^2)$. We provide a lower
complexity bound to show the latter $\mathcal{O}(1/\epsilon^2)$ complexity to
be unimprovable even under a strongly convex setting. All these complexity
results seem to be new in the literature and they indicate that the convex NSCO
problem has the same order of oracle complexity as those without the nested
composition in all but the strongly convex and outer-non-smooth problem. | [
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] |
Click through rate(CTR) prediction is a core task in advertising systems. The
booming e-commerce business in our company, results in a growing number of
scenes. Most of them are so-called long-tail scenes, which means that the
traffic of a single scene is limited, but the overall traffic is considerable.
Typical studies mainly focus on serving a single scene with a well designed
model. However, this method brings excessive resource consumption both on
offline training and online serving. Besides, simply training a single model
with data from multiple scenes ignores the characteristics of their own. To
address these challenges, we propose a novel but practical model named
Domain-Aware Deep Neural Network(DADNN) by serving multiple scenes with only
one model. Specifically, shared bottom block among all scenes is applied to
learn a common representation, while domain-specific heads maintain the
characteristics of every scene. Besides, knowledge transfer is introduced to
enhance the opportunity of knowledge sharing among different scenes. In this
paper, we study two instances of DADNN where its shared bottom block is
multilayer perceptron(MLP) and Multi-gate Mixture-of-Experts(MMoE)
respectively, for which we denote as DADNN-MLP and DADNN-MMoE.Comprehensive
offline experiments on a real production dataset from our company show that
DADNN outperforms several state-of-the-art methods for multi-scene CTR
prediction. Extensive online A/B tests reveal that DADNN-MLP contributes up to
6.7% CTR and 3.0% CPM(Cost Per Mille) promotion compared with a well-engineered
DCN model. Furthermore, DADNN-MMoE outperforms DADNN-MLP with a relative
improvement of 2.2% and 2.7% on CTR and CPM respectively. More importantly,
DADNN utilizes a single model for multiple scenes which saves a lot of offline
training and online serving resources. | [
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] |
Pull requests are a key part of the collaborative software development and
code review process today. However, pull requests can also slow down the
software development process when the reviewer(s) or the author do not actively
engage with the pull request. In this work, we design an end-to-end service,
Nudge, for accelerating overdue pull requests towards completion by reminding
the author or the reviewer(s) to engage with their overdue pull requests.
First, we use models based on effort estimation and machine learning to predict
the completion time for a given pull request. Second, we use activity detection
to filter out pull requests that may be overdue, but for which sufficient
action is taking place nonetheless. Lastly, we use actor identification to
understand who the blocker of the pull request is and nudge the appropriate
actor (author or reviewer(s)). The key novelty of Nudge is that it succeeds in
reducing pull request resolution time, while ensuring that developers perceive
the notifications sent as useful, at the scale of thousands of repositories. In
a randomized trial on 147 repositories in use at Microsoft, Nudge was able to
reduce pull request resolution time by 60% for 8,500 pull requests, when
compared to overdue pull requests for which Nudge did not send a notification.
Furthermore, developers receiving Nudge notifications resolved 73% of these
notifications as positive. We observed similar results when scaling up the
deployment of Nudge to 8,000 repositories at Microsoft, for which Nudge sent
210,000 notifications during a full year. This demonstrates Nudge's ability to
scale to thousands of repositories. Lastly, our qualitative analysis of a
selection of Nudge notifications indicates areas for future research, such as
taking dependencies among pull requests and developer availability into
account. | [
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] |
Pain in sickle cell disease (SCD) is often associated with increased
morbidity, mortality, and high healthcare costs. The standard method for
predicting the absence, presence, and intensity of pain has long been
self-report. However, medical providers struggle to manage patients based on
subjective pain reports correctly and pain medications often lead to further
difficulties in patient communication as they may cause sedation and
sleepiness. Recent studies have shown that objective physiological measures can
predict subjective self-reported pain scores for inpatient visits using machine
learning (ML) techniques. In this study, we evaluate the generalizability of ML
techniques to data collected from 50 patients over an extended period across
three types of hospital visits (i.e., inpatient, outpatient and outpatient
evaluation). We compare five classification algorithms for various pain
intensity levels at both intra-individual (within each patient) and
inter-individual (between patients) level. While all the tested classifiers
perform much better than chance, a Decision Tree (DT) model performs best at
predicting pain on an 11-point severity scale (from 0-10) with an accuracy of
0.728 at an inter-individual level and 0.653 at an intra-individual level. The
accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e.,
no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our
experimental results demonstrate that ML techniques can provide an objective
and quantitative evaluation of pain intensity levels for all three types of
hospital visits. | [
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Building deep learning models on source code has found many successful
software engineering applications, such as code search, code comment
generation, bug detection, code migration, and so on. Current learning
techniques, however, have a major drawback that these models are mostly trained
on datasets labeled for particular downstream tasks, and code representations
may not be suitable for other tasks. While some techniques produce
representations from unlabeled code, they are far from satisfactory when
applied to downstream tasks. Although certain techniques generate
representations from unlabeled code when applied to downstream tasks they are
far from satisfactory. This paper proposes InferCode to overcome the limitation
by adapting the self-supervised learning mechanism to build source code model.
The key novelty lies in training code representations by predicting
automatically identified subtrees from the context of the ASTs. Subtrees in
ASTs are treated with InferCode as the labels for training code representations
without any human labeling effort or the overhead of expensive graph
construction, and the trained representations are no longer tied to any
specific downstream tasks or code units. We trained an InferCode model instance
using the Tree-based CNN as the encoder of a large set of Java code and applied
it to downstream unsupervised tasks such as code clustering, code clone
detection, cross-language code search or reused under a transfer learning
scheme to continue training the model weights for supervised tasks such as code
classification and method name prediction. Compared to previous code learning
techniques applied to the same downstream tasks, such as Code2Vec, Code2Seq,
ASTNN, higher performance results are achieved using our pre-trained InferCode
model with a significant margin for most tasks including those involving
different programming languages. | [
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] |
We propose a learning-based navigation system for reaching visually indicated
goals and demonstrate this system on a real mobile robot platform. Learning
provides an appealing alternative to conventional methods for robotic
navigation: instead of reasoning about environments in terms of geometry and
maps, learning can enable a robot to learn about navigational affordances,
understand what types of obstacles are traversable (e.g., tall grass) or not
(e.g., walls), and generalize over patterns in the environment. However, unlike
conventional planning algorithms, it is harder to change the goal for a learned
policy during deployment. We propose a method for learning to navigate towards
a goal image of the desired destination. By combining a learned policy with a
topological graph constructed out of previously observed data, our system can
determine how to reach this visually indicated goal even in the presence of
variable appearance and lighting. Three key insights, waypoint proposal, graph
pruning and negative mining, enable our method to learn to navigate in
real-world environments using only offline data, a setting where prior methods
struggle. We instantiate our method on a real outdoor ground robot and show
that our system, which we call ViNG, outperforms previously-proposed methods
for goal-conditioned reinforcement learning, including other methods that
incorporate reinforcement learning and search. We also study how \sysName
generalizes to unseen environments and evaluate its ability to adapt to such an
environment with growing experience. Finally, we demonstrate ViNG on a number
of real-world applications, such as last-mile delivery and warehouse
inspection. We encourage the reader to visit the project website for videos of
our experiments and demonstrations sites.google.com/view/ving-robot. | [
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The ever-growing popularity and rapid improving of artificial intelligence
(AI) have raised rethinking on the evolution of wireless networks. Mobile edge
computing (MEC) provides a natural platform for AI applications since it is
with rich computation resources to train machine learning (ML) models, as well
as low-latency access to the data generated by mobile and internet of things
(IoT) devices. In this paper, we present an infrastructure to perform ML tasks
at an MEC server with the assistance of a reconfigurable intelligent surface
(RIS). In contrast to conventional communication systems where the principal
criterions are to maximize the throughput, we aim at maximizing the learning
performance. Specifically, we minimize the maximum learning error of all
participating users by jointly optimizing transmit power of mobile users,
beamforming vectors of the base station (BS), and the phase-shift matrix of the
RIS. An alternating optimization (AO)-based framework is proposed to optimize
the three terms iteratively, where a successive convex approximation
(SCA)-based algorithm is developed to solve the power allocation problem,
closed-form expressions of the beamforming vectors are derived, and an
alternating direction method of multipliers (ADMM)-based algorithm is designed
together with an error level searching (ELS) framework to effectively solve the
challenging nonconvex optimization problem of the phase-shift matrix.
Simulation results demonstrate significant gains of deploying an RIS and
validate the advantages of our proposed algorithms over various benchmarks.
Lastly, a unified communication-training-inference platform is developed based
on the CARLA platform and the SECOND network, and a use case (3D object
detection in autonomous driving) for the proposed scheme is demonstrated on the
developed platform. | [
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] |
In this work, we develop practical user scheduling algorithms for downlink
bursty traffic with emphasis on user fairness. In contrast to the conventional
scheduling algorithms that either equally divides the transmission time slots
among users or maximizing some ratios without physcial meanings, we propose to
use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness.
Since it is difficult to directly optimize 5TUDR, we first cast the problem
into the stochastic game framework and subsequently propose a Multi-Agent
Reinforcement Learning (MARL)-based algorithm to perform distributed
optimization on the resource block group (RBG) allocation. Furthermore, each
MARL agent is designed to take information measured by network counters from
multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the
input states while the RBG allocation as action with a proposed reward function
designed to maximize 5TUDR. Extensive simulation is performed to show that the
proposed MARL-based scheduler can achieve fair scheduling while maintaining
good average network throughput as compared to conventional schedulers. | [
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] |
Community detection, a fundamental task for network analysis, aims to
partition a network into multiple sub-structures to help reveal their latent
functions. Community detection has been extensively studied in and broadly
applied to many real-world network problems. Classical approaches to community
detection typically utilize probabilistic graphical models and adopt a variety
of prior knowledge to infer community structures. As the problems that network
methods try to solve and the network data to be analyzed become increasingly
more sophisticated, new approaches have also been proposed and developed,
particularly those that utilize deep learning and convert networked data into
low dimensional representation. Despite all the recent advancement, there is
still a lack of insightful understanding of the theoretical and methodological
underpinning of community detection, which will be critically important for
future development of the area of network analysis. In this paper, we develop
and present a unified architecture of network community-finding methods to
characterize the state-of-the-art of the field of community detection.
Specifically, we provide a comprehensive review of the existing community
detection methods and introduce a new taxonomy that divides the existing
methods into two categories, namely probabilistic graphical model and deep
learning. We then discuss in detail the main idea behind each method in the two
categories. Furthermore, to promote future development of community detection,
we release several benchmark datasets from several problem domains and
highlight their applications to various network analysis tasks. We conclude
with discussions of the challenges of the field and suggestions of possible
directions for future research. | [
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Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are
increasingly being deployed across multiple functionalities, ranging from
healthcare devices and wearables to critical infrastructures, e.g., nuclear
power plants, autonomous vehicles, smart cities, and smart homes. These devices
are inherently not secure across their comprehensive software, hardware, and
network stacks, thus presenting a large attack surface that can be exploited by
hackers. In this article, we present an innovative technique for detecting
unknown system vulnerabilities, managing these vulnerabilities, and improving
incident response when such vulnerabilities are exploited. The novelty of this
approach lies in extracting intelligence from known real-world CPS/IoT attacks,
representing them in the form of regular expressions, and employing machine
learning (ML) techniques on this ensemble of regular expressions to generate
new attack vectors and security vulnerabilities. Our results show that 10 new
attack vectors and 122 new vulnerability exploits can be successfully generated
that have the potential to exploit a CPS or an IoT ecosystem. The ML
methodology achieves an accuracy of 97.4% and enables us to predict these
attacks efficiently with an 87.2% reduction in the search space. We demonstrate
the application of our method to the hacking of the in-vehicle network of a
connected car. To defend against the known attacks and possible novel exploits,
we discuss a defense-in-depth mechanism for various classes of attacks and the
classification of data targeted by such attacks. This defense mechanism
optimizes the cost of security measures based on the sensitivity of the
protected resource, thus incentivizing its adoption in real-world CPS/IoT by
cybersecurity practitioners. | [
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] |
In spite that Federated Learning (FL) is well known for its privacy
protection when training machine learning models among distributed clients
collaboratively, recent studies have pointed out that the naive FL is
susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy
(DP) emerges as a promising countermeasure to defend against gradient leakage
attacks. However, the adoption of DP by clients in FL may significantly
jeopardize the model accuracy. It is still an open problem to understand the
practicality of DP from a theoretic perspective. In this paper, we make the
first attempt to understand the practicality of DP in FL through tuning the
number of conducted iterations. Based on the FedAvg algorithm, we formally
derive the convergence rate with DP noises in FL. Then, we theoretically
derive: 1) the conditions for the DP based FedAvg to converge as the number of
global iterations (GI) approaches infinity; 2) the method to set the number of
local iterations (LI) to minimize the negative influence of DP noises. By
further substituting the Laplace and Gaussian mechanisms into the derived
convergence rate respectively, we show that: 3) The DP based FedAvg with the
Laplace mechanism cannot converge, but the divergence rate can be effectively
prohibited by setting the number of LIs with our method; 4) The learning error
of the DP based FedAvg with the Gaussian mechanism can converge to a constant
number finally if we use a fixed number of LIs per GI. To verify our
theoretical findings, we conduct extensive experiments using two real-world
datasets. The results not only validate our analysis results, but also provide
useful guidelines on how to optimize model accuracy when incorporating DP into
FL | [
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] |
This work presents a non-intrusive surrogate modeling scheme based on machine
learning technology for predictive modeling of complex systems, described by
parametrized time-dependent PDEs. For these problems, typical finite element
approaches involve the spatiotemporal discretization of the PDE and the
solution of the corresponding linear system of equations at each time step.
Instead, the proposed method utilizes a convolutional autoencoder in
conjunction with a feed forward neural network to establish a low-cost and
accurate mapping from the problem's parametric space to its solution space. For
this purpose, time history response data are collected by solving the
high-fidelity model via FEM for a reduced set of parameter values. Then, by
applying the convolutional autoencoder to this data set, a low-dimensional
representation of the high-dimensional solution matrices is provided by the
encoder, while the reconstruction map is obtained by the decoder. Using the
latent representation given by the encoder, a feed-forward neural network is
efficiently trained to map points from the problem's parametric space to the
compressed version of the respective solution matrices. This way, the encoded
response of the system at new parameter values is given by the neural network,
while the entire response is delivered by the decoder. This approach
effectively bypasses the need to serially formulate and solve the system's
governing equations at each time increment, thus resulting in a significant
cost reduction and rendering the method ideal for problems requiring repeated
model evaluations or 'real-time' computations. The elaborated methodology is
demonstrated on the stochastic analysis of time-dependent PDEs solved with the
Monte Carlo method, however, it can be straightforwardly applied to other
similar-type problems, such as sensitivity analysis, design optimization, etc. | [
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] |
Causal inference for observational longitudinal studies often requires the
accurate estimation of treatment effects on time-to-event outcomes in the
presence of time-dependent patient history and time-dependent covariates. To
tackle this longitudinal treatment effect estimation problem, we have developed
a time-variant causal survival (TCS) model that uses the potential outcomes
framework with an ensemble of recurrent subnetworks to estimate the difference
in survival probabilities and its confidence interval over time as a function
of time-dependent covariates and treatments. Using simulated survival datasets,
the TCS model showed good causal effect estimation performance across scenarios
of varying sample dimensions, event rates, confounding and overlapping.
However, increasing the sample size was not effective in alleviating the
adverse impact of a high level of confounding. In a large clinical cohort
study, TCS identified the expected conditional average treatment effect and
detected individual treatment effect heterogeneity over time. TCS provides an
efficient way to estimate and update individualized treatment effects over
time, in order to improve clinical decisions. The use of a propensity score
layer and potential outcome subnetworks helps correcting for selection bias.
However, the proposed model is limited in its ability to correct the bias from
unmeasured confounding, and more extensive testing of TCS under extreme
scenarios such as low overlapping and the presence of unmeasured confounders is
desired and left for future work. | [
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] |
Building domain-specific accelerators for autonomous unmanned aerial vehicles
(UAVs) is challenging due to a lack of systematic methodology for designing
onboard compute. Balancing a computing system for a UAV requires considering
both the cyber (e.g., sensor rate, compute performance) and physical (e.g.,
payload weight) characteristics that affect overall performance. Iterating over
the many component choices results in a combinatorial explosion of the number
of possible combinations: from 10s of thousands to billions, depending on
implementation details. Manually selecting combinations of these components is
tedious and expensive. To navigate the {cyber-physical design space}
efficiently, we introduce \emph{AutoPilot}, a framework that automates
full-system UAV co-design. AutoPilot uses Bayesian optimization to navigate a
large design space and automatically select a combination of autonomy algorithm
and hardware accelerator while considering the cross-product effect of other
cyber and physical UAV components. We show that the AutoPilot methodology
consistently outperforms general-purpose hardware selections like Xavier NX and
Jetson TX2, as well as dedicated hardware accelerators built for autonomous
UAVs, across a range of representative scenarios (three different UAV types and
three deployment environments). Designs generated by AutoPilot increase the
number of missions on average by up to 2.25x, 1.62x, and 1.43x for nano, micro,
and mini-UAVs respectively over baselines. Our work demonstrates the need for
holistic full-UAV co-design to achieve maximum overall UAV performance and the
need for automated flows to simplify the design process for autonomous
cyber-physical systems. | [
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] |
A reliable, remote, and continuous real-time respiratory sound monitor with
automated respiratory sound analysis ability is urgently required in many
clinical scenarios-such as in monitoring disease progression of coronavirus
disease 2019-to replace conventional auscultation with a handheld stethoscope.
However, a robust computerized respiratory sound analysis algorithm has not yet
been validated in practical applications. In this study, we developed a lung
sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds
(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,
13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze
labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous
adventitious sound labels (all crackles). We conducted benchmark tests for long
short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM
(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,
CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and
adventitious sound detection. We also conducted a performance comparison
between the LSTM-based and GRU-based models, between unidirectional and
bidirectional models, and between models with and without a CNN. The results
revealed that these models exhibited adequate performance in lung sound
analysis. The GRU-based models outperformed, in terms of F1 scores and areas
under the receiver operating characteristic curves, the LSTM-based models in
most of the defined tasks. Furthermore, all bidirectional models outperformed
their unidirectional counterparts. Finally, the addition of a CNN improved the
accuracy of lung sound analysis, especially in the CAS detection tasks. | [
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] |
Financial portfolio management (PM) is one of the most applicable problems in
reinforcement learning (RL) owing to its sequential decision-making nature.
However, existing RL-based approaches rarely focus on scalability or
reusability to adapt to the ever-changing markets. These approaches are rigid
and unscalable to accommodate the varying number of assets of portfolios and
increasing need for heterogeneous data. Also, RL agents in the existing systems
are ad-hoc trained and hardly reusable for different portfolios. To confront
the above problems, a modular design is desired for the systems to be
compatible with reusable asset-dedicated agents. In this paper, we propose a
multi-agent RL-based system for PM (MSPM). MSPM involves two types of
asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent
Module (SAM). An EAM is an information-generating module with a DQN agent, and
it receives heterogeneous data and generates signal-comprised information for a
particular asset. An SAM is a decision-making module with a PPO agent for
portfolio optimization, and it connects to EAMs to reallocate the assets in a
portfolio. Trained EAMs can be connected to any SAM at will. With its
modularized architecture, the multi-step condensation of volatile market
information, and the reusable design of EAM, MSPM simultaneously addresses the
two challenges in RL-based PM: scalability and reusability. Experiments on
8-year U.S. stock market data prove the effectiveness of MSPM in profit
accumulation by its outperformance over five baselines in terms of accumulated
rate of return (ARR), daily rate of return, and Sortino ratio. MSPM improves
ARR by at least 186.5% compared to CRP, a widely-used PM strategy. To validate
the indispensability of EAM, we back-test and compare MSPMs on four portfolios.
EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled
MSPMs. | [
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] |
Purpose: We previously established an open-access lung sound database,
HF_Lung_V1, and developed deep learning models for inhalation, exhalation,
continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS)
detection. The amount of data used for training contributes to model accuracy.
Herein, we collected larger quantities of data to further improve model
performance. Moreover, the issues of noisy labels and sound overlapping were
explored. Methods: HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.45x increase
in the number of audio files. Convolutional neural network-bidirectional gated
recurrent unit network models were trained separately using the HF_Lung_V1
(V1_Train) and HF_Lung_V2 (V2_Train) training sets and then tested using the
HF_Lung_V1 (V1_Test) and HF_Lung_V2 (V2_Test) test sets, respectively. Segment
and event detection performance was evaluated using the F1 scores. Label
quality was assessed. Moreover, the overlap ratios between inhalation,
exhalation, CAS, and DAS labels were computed. Results: The model trained using
V2_Train exhibited improved F1 scores in inhalation, exhalation, and CAS
detection on both V1_Test and V2_Test but not in DAS detection. Poor CAS
detection was attributed to the quality of CAS labels. DAS detection was
strongly influenced by the overlapping of DAS labels with inhalation and
exhalation labels. Conclusion: Collecting greater quantities of lung sound data
is vital for developing more accurate lung sound analysis models. To build real
ground-truth labels, the labels must be reworked; this process is ongoing.
Furthermore, a method for addressing the sound overlapping problem in DAS
detection must be formulated. | [
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] |
Background. Forecasting the time of forthcoming pandemic reduces the impact
of diseases by taking precautionary steps such as public health messaging and
raising the consciousness of doctors. With the continuous and rapid increase in
the cumulative incidence of COVID-19, statistical and outbreak prediction
models including various machine learning (ML) models are being used by the
research community to track and predict the trend of the epidemic, and also in
developing appropriate strategies to combat and manage its spread. Methods. In
this paper, we present a comparative analysis of various ML approaches
including Support Vector Machine, Random Forest, K-Nearest Neighbor and
Artificial Neural Network in predicting the COVID-19 outbreak in the
epidemiological domain. We first apply the autoregressive distributed lag
(ARDL) method to identify and model the short and long-run relationships of the
time-series COVID-19 datasets. That is, we determine the lags between a
response variable and its respective explanatory time series variables as
independent variables. Then, the resulting significant variables concerning
their lags are used in the regression model selected by the ARDL for predicting
and forecasting the trend of the epidemic. Results. Statistical measures i.e.,
Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute
Percentage Error (MAPE) are used for model accuracy. The values of MAPE for the
best selected models for confirmed, recovered and deaths cases are 0.407, 0.094
and 0.124 respectively, which falls under the category of highly accurate
forecasts. In addition, we computed fifteen days ahead forecast for the daily
deaths, recover, and confirm patients and the cases fluctuated across time in
all aspects. Besides, the results reveal the advantages of ML algorithms for
supporting decision making of evolving short term policies. | [
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Cyber Physical Systems (CPS) are characterized by their ability to integrate
the physical and information or cyber worlds. Their deployment in critical
infrastructure have demonstrated a potential to transform the world. However,
harnessing this potential is limited by their critical nature and the far
reaching effects of cyber attacks on human, infrastructure and the environment.
An attraction for cyber concerns in CPS rises from the process of sending
information from sensors to actuators over the wireless communication medium,
thereby widening the attack surface. Traditionally, CPS security has been
investigated from the perspective of preventing intruders from gaining access
to the system using cryptography and other access control techniques. Most
research work have therefore focused on the detection of attacks in CPS.
However, in a world of increasing adversaries, it is becoming more difficult to
totally prevent CPS from adversarial attacks, hence the need to focus on making
CPS resilient. Resilient CPS are designed to withstand disruptions and remain
functional despite the operation of adversaries. One of the dominant
methodologies explored for building resilient CPS is dependent on machine
learning (ML) algorithms. However, rising from recent research in adversarial
ML, we posit that ML algorithms for securing CPS must themselves be resilient.
This paper is therefore aimed at comprehensively surveying the interactions
between resilient CPS using ML and resilient ML when applied in CPS. The paper
concludes with a number of research trends and promising future research
directions. Furthermore, with this paper, readers can have a thorough
understanding of recent advances on ML-based security and securing ML for CPS
and countermeasures, as well as research trends in this active research area. | [
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] |
Research has shown that trust is an essential aspect of human-computer
interaction directly determining the degree to which the person is willing to
use a system. An automatic prediction of the level of trust that a user has on
a certain system could be used to attempt to correct potential distrust by
having the system take relevant actions like, for example, apologizing or
explaining its decisions. In this work, we explore the feasibility of
automatically detecting the level of trust that a user has on a virtual
assistant (VA) based on their speech. We developed a novel protocol for
collecting speech data from subjects induced to have different degrees of trust
in the skills of a VA. The protocol consists of an interactive session where
the subject is asked to respond to a series of factual questions with the help
of a virtual assistant. In order to induce subjects to either trust or distrust
the VA's skills, they are first informed that the VA was previously rated by
other users as being either good or bad; subsequently, the VA answers the
subjects' questions consistently to its alleged abilities. All interactions are
speech-based, with subjects and VAs communicating verbally, which allows the
recording of speech produced under different trust conditions. Using this
protocol, we collected a speech corpus in Argentine Spanish. We show clear
evidence that the protocol effectively succeeded in influencing subjects into
the desired mental state of either trusting or distrusting the agent's skills,
and present results of a perceptual study of the degree of trust performed by
expert listeners. Finally, we found that the subject's speech can be used to
detect which type of VA they were using, which could be considered a proxy for
the user's trust toward the VA's abilities, with an accuracy up to 76%,
compared to a random baseline of 50%. | [
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] |
Machine learning (ML) is an important part of modern data science
applications. Data scientists today have to manage the end-to-end ML life cycle
that includes both model training and model serving, the latter of which is
essential, as it makes their works available to end-users. Systems of model
serving require high performance, low cost, and ease of management. Cloud
providers are already offering model serving choices, including managed
services and self-rented servers. Recently, serverless computing, whose
advantages include high elasticity and a fine-grained cost model, brings
another option for model serving. Our goal in this paper is to examine the
viability of serverless as a mainstream model serving platform. To this end, we
first conduct a comprehensive evaluation of the performance and cost of
serverless against other model serving systems on Amazon Web Service and Google
Cloud Platform. We find that serverless outperforms many cloud-based
alternatives. Further, there are settings under which it even achieves better
performance than GPU-based systems. Next, we present the design space of
serverless model serving, which comprises multiple dimensions, including cloud
platforms, serving runtimes, and other function-specific parameters. For each
dimension, we analyze the impact of different choices and provide suggestions
for data scientists to better utilize serverless model serving. Finally, we
discuss challenges and opportunities in building a more practical serverless
model serving system. | [
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] |
Identifying the research topics that best describe the scope of a scientific
publication is a crucial task for editors, in particular because the quality of
these annotations determine how effectively users are able to discover the
right content in online libraries. For this reason, Springer Nature, the
world's largest academic book publisher, has traditionally entrusted this task
to their most expert editors. These editors manually analyse all new books,
possibly including hundreds of chapters, and produce a list of the most
relevant topics. Hence, this process has traditionally been very expensive,
time-consuming, and confined to a few senior editors. For these reasons, back
in 2016 we developed Smart Topic Miner (STM), an ontology-driven application
that assists the Springer Nature editorial team in annotating the volumes of
all books covering conference proceedings in Computer Science. Since then STM
has been regularly used by editors in Germany, China, Brazil, India, and Japan,
for a total of about 800 volumes per year. Over the past three years the
initial prototype has iteratively evolved in response to feedback from the
users and evolving requirements. In this paper we present the most recent
version of the tool and describe the evolution of the system over the years,
the key lessons learnt, and the impact on the Springer Nature workflow. In
particular, our solution has drastically reduced the time needed to annotate
proceedings and significantly improved their discoverability, resulting in 9.3
million additional downloads. We also present a user study involving 9 editors,
which yielded excellent results in term of usability, and report an evaluation
of the new topic classifier used by STM, which outperforms previous versions in
recall and F-measure. | [
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] |
We identify label errors in the test sets of 10 of the most commonly-used
computer vision, natural language, and audio datasets, and subsequently study
the potential for these label errors to affect benchmark results. Errors in
test sets are numerous and widespread: we estimate an average of at least 3.3%
errors across the 10 datasets, where for example label errors comprise at least
6% of the ImageNet validation set. Putative label errors are identified using
confident learning algorithms and then human-validated via crowdsourcing (51%
of the algorithmically-flagged candidates are indeed erroneously labeled, on
average across the datasets). Traditionally, machine learning practitioners
choose which model to deploy based on test accuracy - our findings advise
caution here, proposing that judging models over correctly labeled test sets
may be more useful, especially for noisy real-world datasets. Surprisingly, we
find that lower capacity models may be practically more useful than higher
capacity models in real-world datasets with high proportions of erroneously
labeled data. For example, on ImageNet with corrected labels: ResNet-18
outperforms ResNet-50 if the prevalence of originally mislabeled test examples
increases by just 6%. On CIFAR-10 with corrected labels: VGG-11 outperforms
VGG-19 if the prevalence of originally mislabeled test examples increases by
just 5%. Test set errors across the 10 datasets can be viewed at
https://labelerrors.com and all label errors can be reproduced by
https://github.com/cleanlab/label-errors. | [
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] |
Building operations represent a significant percentage of the total primary
energy consumed in most countries due to the proliferation of Heating,
Ventilation and Air-Conditioning (HVAC) installations in response to the
growing demand for improved thermal comfort. Reducing the associated energy
consumption while maintaining comfortable conditions in buildings are
conflicting objectives and represent a typical optimization problem that
requires intelligent system design. Over the last decade, different
methodologies based on the Artificial Intelligence (AI) techniques have been
deployed to find the sweet spot between energy use in HVAC systems and suitable
indoor comfort levels to the occupants. This paper performs a comprehensive and
an in-depth systematic review of AI-based techniques used for building control
systems by assessing the outputs of these techniques, and their implementations
in the reviewed works, as well as investigating their abilities to improve the
energy-efficiency, while maintaining thermal comfort conditions. This enables a
holistic view of (1) the complexities of delivering thermal comfort to users
inside buildings in an energy-efficient way, and (2) the associated
bibliographic material to assist researchers and experts in the field in
tackling such a challenge. Among the 20 AI tools developed for both energy
consumption and comfort control, functions such as identification and
recognition patterns, optimization, predictive control. Based on the findings
of this work, the application of AI technology in building control is a
promising area of research and still an ongoing, i.e., the performance of
AI-based control is not yet completely satisfactory. This is mainly due in part
to the fact that these algorithms usually need a large amount of high-quality
real-world data, which is lacking in the building or, more precisely, the
energy sector. | [
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] |
It is not hard to see that the need for clean water is growing by considering
the decrease of the water sources day by day in the world. Potable fresh water
is also used for irrigation, so it should be planned to decrease freshwater
wastage. With the development of technology and the availability of cheaper and
more effective solutions, the efficiency of irrigation increased and the water
loss can be reduced. In particular, Internet of things (IoT) devices has begun
to be used in all areas. We can easily and precisely collect temperature,
humidity and mineral values from the irrigation field with the IoT devices and
sensors. Most of the operations and decisions about irrigation are carried out
by people. For people, it is hard to have all the real-time data such as
temperature, moisture and mineral levels in the decision-making process and
make decisions by considering them. People usually make decisions with their
experience. In this study, a wide range of information from the irrigation
field was obtained by using IoT devices and sensors. Data collected from IoT
devices and sensors sent via communication channels and stored on MongoDB. With
the help of Weka software, the data was normalized and the normalized data was
used as a learning set. As a result of the examinations, a decision tree (J48)
algorithm with the highest accuracy was chosen and an artificial intelligence
model was created. Decisions are used to manage operations such as starting,
maintaining and stopping the irrigation. The accuracy of the decisions was
evaluated and the irrigation system was tested with the results. There are
options to manage, view the system remotely and manually and also see the
system s decisions with the created mobile application. | [
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] |
Rapid development of advanced modelling techniques gives an opportunity to
develop tools that are more and more accurate. However as usually, everything
comes with a price and in this case, the price to pay is to loose
interpretability of a model while gaining on its accuracy and precision. For
managers to control and effectively manage credit risk and for regulators to be
convinced with model quality the price to pay is too high. In this paper, we
show how to take credit scoring analytics in to the next level, namely we
present comparison of various predictive models (logistic regression, logistic
regression with weight of evidence transformations and modern artificial
intelligence algorithms) and show that advanced tree based models give best
results in prediction of client default. What is even more important and
valuable we also show how to boost advanced models using techniques which allow
to interpret them and made them more accessible for credit risk practitioners,
resolving the crucial obstacle in widespread deployment of more complex, 'black
box' models like random forests, gradient boosted or extreme gradient boosted
trees. All this will be shown on the large dataset obtained from the Polish
Credit Bureau to which all the banks and most of the lending companies in the
country do report the credit files. In this paper the data from lending
companies were used. The paper then compares state of the art best practices in
credit risk modelling with new advanced modern statistical tools boosted by the
latest developments in the field of interpretability and explainability of
artificial intelligence algorithms. We believe that this is a valuable
contribution when it comes to presentation of different modelling tools but
what is even more important it is showing which methods might be used to get
insight and understanding of AI methods in credit risk context. | [
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] |
Automated seizure detection and classification from electroencephalography
(EEG) can greatly improve seizure diagnosis and treatment. However, several
modeling challenges remain unaddressed in prior automated seizure detection and
classification studies: (1) representing non-Euclidean data structure in EEGs,
(2) accurately classifying rare seizure types, and (3) lacking a quantitative
interpretability approach to measure model ability to localize seizures. In
this study, we address these challenges by (1) representing the spatiotemporal
dependencies in EEGs using a graph neural network (GNN) and proposing two EEG
graph structures that capture the electrode geometry or dynamic brain
connectivity, (2) proposing a self-supervised pre-training method that predicts
preprocessed signals for the next time period to further improve model
performance, particularly on rare seizure types, and (3) proposing a
quantitative model interpretability approach to assess a model's ability to
localize seizures within EEGs. When evaluating our approach on seizure
detection and classification on a large public dataset, we find that our GNN
with self-supervised pre-training achieves 0.875 Area Under the Receiver
Operating Characteristic Curve on seizure detection and 0.749 weighted F1-score
on seizure classification, outperforming previous methods for both seizure
detection and classification. Moreover, our self-supervised pre-training
strategy significantly improves classification of rare seizure types.
Furthermore, quantitative interpretability analysis shows that our GNN with
self-supervised pre-training precisely localizes 25.4% focal seizures, a 21.9
point improvement over existing CNNs. Finally, by superimposing the identified
seizure locations on both raw EEG signals and EEG graphs, our approach could
provide clinicians with an intuitive visualization of localized seizure
regions. | [
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] |
Mental fatigue increases the risk of operator error in language comprehension
tasks. In order to prevent operator performance degradation, we used EEG
signals to assess the mental fatigue of operators in human-computer systems.
This study presents an experimental design for fatigue detection in language
comprehension tasks. We obtained EEG signals from a 14-channel wireless EEG
detector in 15 healthy participants. Each participant was given a cognitive
test of a language comprehension task, in the form of multiple choice
questions, in which pronoun references were selected between nominal and
surrogate sentences. In this paper, the 2400 EEG fragments collected are
divided into three data sets according to different utilization rates, namely
1200s data set with 50% utilization rate, 1500s data set with 62.5% utilization
rate, and 1800s data set with 75% utilization rate. In the aspect of feature
extraction, different EEG features were extracted, including time domain
features, frequency domain features and entropy features, and the effects of
different features and feature combinations on classification accuracy were
explored. In terms of classification, we introduced the Convolutional Neural
Network (CNN) method as the preferred method, It was compared with Least
Squares Support Vector Machines(LSSVM),Support Vector Machines(SVM),Logistic
Regression (LR), Random Forest(RF), Naive Bayes (NB), K-Nearest Neighbor (KNN)
and Decision Tree(DT).According to the results, the classification accuracy of
convolutional neural network (CNN) is higher than that of other classification
methods. The classification results show that the classification accuracy of
1200S dataset is higher than the other two datasets. The combination of
Frequency and entropy feature and CNN has the highest classification accuracy,
which is 85.34%. | [
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] |
Because of the superior feature representation ability of deep learning,
various deep Click-Through Rate (CTR) models are deployed in the commercial
systems by industrial companies. To achieve better performance, it is necessary
to train the deep CTR models on huge volume of training data efficiently, which
makes speeding up the training process an essential problem. Different from the
models with dense training data, the training data for CTR models is usually
high-dimensional and sparse. To transform the high-dimensional sparse input
into low-dimensional dense real-value vectors, almost all deep CTR models adopt
the embedding layer, which easily reaches hundreds of GB or even TB. Since a
single GPU cannot afford to accommodate all the embedding parameters, when
performing distributed training, it is not reasonable to conduct the
data-parallelism only. Therefore, existing distributed training platforms for
recommendation adopt model-parallelism. Specifically, they use CPU (Host)
memory of servers to maintain and update the embedding parameters and utilize
GPU worker to conduct forward and backward computations. Unfortunately, these
platforms suffer from two bottlenecks: (1) the latency of pull \& push
operations between Host and GPU; (2) parameters update and synchronization in
the CPU servers. To address such bottlenecks, in this paper, we propose the
ScaleFreeCTR: a MixCache-based distributed training system for CTR models.
Specifically, in SFCTR, we also store huge embedding table in CPU but utilize
GPU instead of CPU to conduct embedding synchronization efficiently. To reduce
the latency of data transfer between both GPU-Host and GPU-GPU, the MixCache
mechanism and Virtual Sparse Id operation are proposed. Comprehensive
experiments and ablation studies are conducted to demonstrate the effectiveness
and efficiency of SFCTR. | [
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] |
Industrial Information Technology (IT) infrastructures are often vulnerable
to cyberattacks. To ensure security to the computer systems in an industrial
environment, it is required to build effective intrusion detection systems to
monitor the cyber-physical systems (e.g., computer networks) in the industry
for malicious activities. This paper aims to build such intrusion detection
systems to protect the computer networks from cyberattacks. More specifically,
we propose a novel unsupervised machine learning approach that combines the
K-Means algorithm with the Isolation Forest for anomaly detection in industrial
big data scenarios. Since our objective is to build the intrusion detection
system for the big data scenario in the industrial domain, we utilize the
Apache Spark framework to implement our proposed model which was trained in
large network traffic data (about 123 million instances of network traffic)
stored in Elasticsearch. Moreover, we evaluate our proposed model on the live
streaming data and find that our proposed system can be used for real-time
anomaly detection in the industrial setup. In addition, we address different
challenges that we face while training our model on large datasets and
explicitly describe how these issues were resolved. Based on our empirical
evaluation in different use-cases for anomaly detection in real-world network
traffic data, we observe that our proposed system is effective to detect
anomalies in big data scenarios. Finally, we evaluate our proposed model on
several academic datasets to compare with other models and find that it
provides comparable performance with other state-of-the-art approaches. | [
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Modelling the dynamics of urban venues is a challenging task as it is
multifaceted in nature. Demand is a function of many complex and nonlinear
features such as neighborhood composition, real-time events, and seasonality.
Recent advances in Graph Convolutional Networks (GCNs) have had promising
results as they build a graphical representation of a system and harness the
potential of deep learning architectures. However, there has been limited work
using GCNs in a temporal setting to model dynamic dependencies of the network.
Further, within the context of urban environments, there has been no prior work
using dynamic GCNs to support venue demand analysis and prediction. In this
paper, we propose a novel deep learning framework which aims to better model
the popularity and growth of urban venues. Using a longitudinal dataset from
location technology platform Foursquare, we model individual venues and venue
types across London and Paris. First, representing cities as connected networks
of venues, we quantify their structure and note a strong community structure in
these retail networks, an observation that highlights the interplay of
cooperative and competitive forces that emerge in local ecosystems of retail
businesses. Next, we present our deep learning architecture which integrates
both spatial and topological features into a temporal model which predicts the
demand of a venue at the subsequent time-step. Our experiments demonstrate that
our model can learn spatio-temporal trends of venue demand and consistently
outperform baseline models. Relative to state-of-the-art deep learning models,
our model reduces the RSME by ~ 28% in London and ~ 13% in Paris. Our approach
highlights the power of complex network measures and GCNs in building
prediction models for urban environments. The model could have numerous
applications within the retail sector to better model venue demand and growth. | [
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] |
Left ventricular assist devices (LVADs) are mechanical pumps, which can be
used to support heart failure (HF) patients as bridge to transplant and
destination therapy. To automatically adjust the LVAD speed, a physiological
control system needs to be designed to respond to variations of patient
hemodynamics across a variety of clinical scenarios. These control systems
require pressure feedback signals from the cardiovascular system. However,
there are no suitable long-term implantable sensors available. In this study, a
novel real-time deep convolutional neural network (CNN) for estimation of
preload based on the LVAD flow was proposed. A new sensorless adaptive
physiological control system for an LVAD pump was developed using the full
dynamic form of model free adaptive control (FFDL-MFAC) and the proposed
preload estimator to maintain the patient conditions in safe physiological
ranges. The CNN model for preload estimation was trained and evaluated through
10-fold cross validation on 100 different patient conditions and the proposed
sensorless control system was assessed on a new testing set of 30 different
patient conditions across six different patient scenarios. The proposed preload
estimator was extremely accurate with a correlation coefficient of 0.97, root
mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg,
coefficient of variation of 14.44 %, and bias of 0.29 mmHg for the testing
dataset. The results also indicate that the proposed sensorless physiological
controller works similarly to the preload-based physiological control system
for LVAD using measured preload to prevent ventricular suction and pulmonary
congestion. This study shows that the LVADs can respond appropriately to
changing patient states and physiological demands without the need for
additional pressure or flow measurements. | [
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] |
Recommendation systems have been extensively studied by many literature in
the past and are ubiquitous in online advertisement, shopping
industry/e-commerce, query suggestions in search engines, and friend
recommendation in social networks. Moreover,
restaurant/music/product/movie/news/app recommendations are only a few of the
applications of a recommender system. A small percent improvement on the CTR
prediction accuracy has been mentioned to add millions of dollars of revenue to
the advertisement industry. Click-Through-Rate (CTR) prediction is a special
version of recommender system in which the goal is predicting whether or not a
user is going to click on a recommended item. A content-based recommendation
approach takes into account the past history of the user's behavior, i.e. the
recommended products and the users reaction to them. So, a personalized model
that recommends the right item to the right user at the right time is the key
to building such a model. On the other hand, the so-called collaborative
filtering approach incorporates the click history of the users who are very
similar to a particular user, thereby helping the recommender to come up with a
more confident prediction for that particular user by leveraging the wider
knowledge of users who share their taste in a connected network of users. In
this project, we are interested in building a CTR predictor using Graph Neural
Networks complemented by an online learning algorithm that models such dynamic
interactions. By framing the problem as a binary classification task, we have
evaluated this system both on the offline models (GNN, Deep Factorization
Machines) with test-AUC of 0.7417 and on the online learning model with
test-AUC of 0.7585 using a sub-sampled version of Criteo public dataset
consisting of 10,000 data points. | [
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We extended dynamic time warping (DTW) into interval-based dynamic time
warping (iDTW), including (A) interval-based representation (iRep): [1]
abstracting raw, time-stamped data into interval-based abstractions, [2]
comparison-period scoping, [3] partitioning abstract intervals into a given
temporal granularity; (B) interval-based matching (iMatch): matching
partitioned, abstract-concepts records, using a modified DTW. Using domain
knowledge, we abstracted the raw data of medical records, for up to three
concepts out of four or five relevant concepts, into two interval types: State
abstractions (e.g. LOW, HIGH) and Gradient abstractions (e.g. INCREASING,
DECREASING). We created all uni-dimensional (State or Gradient) or
multi-dimensional (State and Gradient) abstraction combinations. Tasks:
Classifying 161 oncology patients records as autologous or allogenic
bone-marrow transplantation; classifying 125 hepatitis patients records as B or
C hepatitis; predicting micro- or macro-albuminuria in the next year for 151
Type 2 diabetes patients. We used a k-Nearest-Neighbors majority, k = an odd
number from 1 to SQRT(N), N = set size. 75,936 10-fold cross-validation
experiments were performed: 33,600 (Oncology), 28,800 (Hepatitis), 13,536
(Diabetes). Measures: Area Under the Curve (AUC), optimal Youden's Index.
Paired t-tests compared result vectors for equivalent configurations other than
a tested variable, to determine a significant mean accuracy difference
(P<0.05). Mean classification and prediction using abstractions was
significantly better than using only raw time-stamped data. In each domain, at
least one abstraction combination led to a significantly better mean
performance than raw data. Increasing feature number and using
Multi-dimensional abstractions enhanced performance. Unlike when using raw
data, optimal mean performance was often reached with k=5, using abstractions. | [
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] |
Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models
have recently begun to be explored in the context of drug discovery and have
the potential to assist in key challenges such as target identification. In the
drug discovery domain, KGs can be employed as part of a process which can
result in lab-based experiments being performed, or impact on other decisions,
incurring significant time and financial costs and most importantly, ultimately
influencing patient healthcare. For KGE models to have impact in this domain, a
better understanding of not only of performance, but also the various factors
which determine it, is required. In this study we investigate, over the course
of many thousands of experiments, the predictive performance of five KGE models
on two public drug discovery-oriented KGs. Our goal is not to focus on the best
overall model or configuration, instead we take a deeper look at how
performance can be affected by changes in the training setup, choice of
hyperparameters, model parameter initialisation seed and different splits of
the datasets. Our results highlight that these factors have significant impact
on performance and can even affect the ranking of models. Indeed these factors
should be reported along with model architectures to ensure complete
reproducibility and fair comparisons of future work, and we argue this is
critical for the acceptance of use, and impact of KGEs in a biomedical setting. | [
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] |
Modern vehicles, including connected vehicles and autonomous vehicles,
nowadays involve many electronic control units connected through intra-vehicle
networks to implement various functionalities and perform actions. Modern
vehicles are also connected to external networks through vehicle-to-everything
technologies, enabling their communications with other vehicles,
infrastructures, and smart devices. However, the improving functionality and
connectivity of modern vehicles also increase their vulnerabilities to
cyber-attacks targeting both intra-vehicle and external networks due to the
large attack surfaces. To secure vehicular networks, many researchers have
focused on developing intrusion detection systems (IDSs) that capitalize on
machine learning methods to detect malicious cyber-attacks. In this paper, the
vulnerabilities of intra-vehicle and external networks are discussed, and a
multi-tiered hybrid IDS that incorporates a signature-based IDS and an
anomaly-based IDS is proposed to detect both known and unknown attacks on
vehicular networks. Experimental results illustrate that the proposed system
can detect various types of known attacks with 99.99% accuracy on the
CAN-intrusion-dataset representing the intra-vehicle network data and 99.88%
accuracy on the CICIDS2017 dataset illustrating the external vehicular network
data. For the zero-day attack detection, the proposed system achieves high
F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The
average processing time of each data packet on a vehicle-level machine is less
than 0.6 ms, which shows the feasibility of implementing the proposed system in
real-time vehicle systems. This emphasizes the effectiveness and efficiency of
the proposed IDS. | [
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EEG is the gold standard for seizure detection in the newborn infant, but EEG
interpretation in the preterm group is particularly challenging; trained
experts are scarce and the task of interpreting EEG in real-time is arduous.
Preterm infants are reported to have a higher incidence of seizures compared to
term infants. Preterm EEG morphology differs from that of term infants, which
implies that seizure detection algorithms trained on term EEG may not be
appropriate. The task of developing preterm specific algorithms becomes
extra-challenging given the limited amount of annotated preterm EEG data
available. This paper explores novel deep learning (DL) architectures for the
task of neonatal seizure detection in preterm infants. The study tests and
compares several approaches to address the problem: training on data from
full-term infants; training on data from preterm infants; training on
age-specific preterm data and transfer learning. The system performance is
assessed on a large database of continuous EEG recordings of 575h in duration.
It is shown that the accuracy of a validated term-trained EEG seizure detection
algorithm, based on a support vector machine classifier, when tested on preterm
infants falls well short of the performance achieved for full-term infants. An
AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6%
obtained when tested on term EEG. When re-trained on preterm EEG, the
performance marginally increases to 89.7%. An alternative DL approach shows a
more stable trend when tested on the preterm cohort, starting with an AUC of
93.3% for the term-trained algorithm and reaching 95.0% by transfer learning
from the term model using available preterm data. | [
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] |
A key challenge in mining social media data streams is to identify events
which are actively discussed by a group of people in a specific local or global
area. Such events are useful for early warning for accident, protest, election
or breaking news. However, neither the list of events nor the resolution of
both event time and space is fixed or known beforehand. In this work, we
propose an online spatio-temporal event detection system using social media
that is able to detect events at different time and space resolutions. First,
to address the challenge related to the unknown spatial resolution of events, a
quad-tree method is exploited in order to split the geographical space into
multiscale regions based on the density of social media data. Then, a
statistical unsupervised approach is performed that involves Poisson
distribution and a smoothing method for highlighting regions with unexpected
density of social posts. Further, event duration is precisely estimated by
merging events happening in the same region at consecutive time intervals. A
post processing stage is introduced to filter out events that are spam, fake or
wrong. Finally, we incorporate simple semantics by using social media entities
to assess the integrity, and accuracy of detected events. The proposed method
is evaluated using different social media datasets: Twitter and Flickr for
different cities: Melbourne, London, Paris and New York. To verify the
effectiveness of the proposed method, we compare our results with two baseline
algorithms based on fixed split of geographical space and clustering method.
For performance evaluation, we manually compute recall and precision. We also
propose a new quality measure named strength index, which automatically
measures how accurate the reported event is. | [
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] |
With the widespread use of Deep Neural Networks (DNNs), machine learning
algorithms have evolved in two diverse directions -- one with ever-increasing
connection density for better accuracy and the other with more compact sizing
for energy efficiency. The increase in connection density increases on-chip
data movement, which makes efficient on-chip communication a critical function
of the DNN accelerator. The contribution of this work is threefold. First, we
illustrate that the point-to-point (P2P)-based interconnect is incapable of
handling a high volume of on-chip data movement for DNNs. Second, we evaluate
P2P and network-on-chip (NoC) interconnect (with a regular topology such as a
mesh) for SRAM- and ReRAM-based in-memory computing (IMC) architectures for a
range of DNNs. This analysis shows the necessity for the optimal interconnect
choice for an IMC DNN accelerator. Finally, we perform an experimental
evaluation for different DNNs to empirically obtain the performance of the IMC
architecture with both NoC-tree and NoC-mesh. We conclude that, at the tile
level, NoC-tree is appropriate for compact DNNs employed at the edge, and
NoC-mesh is necessary to accelerate DNNs with high connection density.
Furthermore, we propose a technique to determine the optimal choice of
interconnect for any given DNN. In this technique, we use analytical models of
NoC to evaluate end-to-end communication latency of any given DNN. We
demonstrate that the interconnect optimization in the IMC architecture results
in up to 6$\times$ improvement in energy-delay-area product for VGG-19
inference compared to the state-of-the-art ReRAM-based IMC architectures. | [
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Models are used in both Software Engineering (SE) and Artificial Intelligence
(AI). SE models may specify the architecture at different levels of abstraction
and for addressing different concerns at various stages of the software
development life-cycle, from early conceptualization and design, to
verification, implementation, testing and evolution. However, AI models may
provide smart capabilities, such as prediction and decision-making support. For
instance, in Machine Learning (ML), which is currently the most popular
sub-discipline of AI, mathematical models may learn useful patterns in the
observed data and can become capable of making predictions. The goal of this
work is to create synergy by bringing models in the said communities together
and proposing a holistic approach to model-driven software development for
intelligent systems that require ML. We illustrate how software models can
become capable of creating and dealing with ML models in a seamless manner. The
main focus is on the domain of the Internet of Things (IoT), where both ML and
model-driven SE play a key role. In the context of the need to take a
Cyber-Physical System-of-Systems perspective of the targeted architecture, an
integrated design environment for both SE and ML sub-systems would best support
the optimization and overall efficiency of the implementation of the resulting
system. In particular, we implement the proposed approach, called ML-Quadrat,
based on ThingML, and validate it using a case study from the IoT domain, as
well as through an empirical user evaluation. It transpires that the proposed
approach is not only feasible, but may also contribute to the performance leap
of software development for smart Cyber-Physical Systems (CPS) which are
connected to the IoT, as well as an enhanced user experience of the
practitioners who use the proposed modeling solution. | [
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] |
Collaborative filtering (CF) is widely used to learn informative latent
representations of users and items from observed interactions. Existing
CF-based methods commonly adopt negative sampling to discriminate different
items. Training with negative sampling on large datasets is computationally
expensive. Further, negative items should be carefully sampled under the
defined distribution, in order to avoid selecting an observed positive item in
the training dataset. Unavoidably, some negative items sampled from the
training dataset could be positive in the test set. In this paper, we propose a
self-supervised collaborative filtering framework (SelfCF), that is specially
designed for recommender scenario with implicit feedback. The proposed SelfCF
framework simplifies the Siamese networks and can be easily applied to existing
deep-learning based CF models, which we refer to as backbone networks. The main
idea of SelfCF is to augment the output embeddings generated by backbone
networks, because it is infeasible to augment raw input of user/item ids. We
propose and study three output perturbation techniques that can be applied to
different types of backbone networks including both traditional CF models and
graph-based models. The framework enables learning informative representations
of users and items without negative samples, and is agnostic to the
encapsulated backbones. We conduct comprehensive experiments on four datasets
to show that our framework may achieve even better recommendation accuracy than
the encapsulated supervised counterpart with a 2$\times$--4$\times$ faster
training speed. We also show that SelfCF can boost up the accuracy by up to
17.79% on average, compared with a self-supervised framework BUIR. | [
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] |
This paper is an initial endeavor to bridge the gap between powerful Deep
Reinforcement Learning methodologies and the problem of exploration/coverage of
unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible
environment tailored to exploration/coverage of unknown areas, is presented.
MarsExplorer translates the original robotics problem into a Reinforcement
Learning setup that various off-the-shelf algorithms can tackle. Any learned
policy can be straightforwardly applied to a robotic platform without an
elaborate simulation model of the robot's dynamics to apply a different
learning/adaptation phase. One of its core features is the controllable
multi-dimensional procedural generation of terrains, which is the key for
producing policies with strong generalization capabilities. Four different
state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the
MarsExplorer environment, and a proper evaluation of their results compared to
the average human-level performance is reported. In the follow-up experimental
analysis, the effect of the multi-dimensional difficulty setting on the
learning capabilities of the best-performing algorithm (PPO) is analyzed. A
milestone result is the generation of an exploration policy that follows the
Hilbert curve without providing this information to the environment or
rewarding directly or indirectly Hilbert-curve-like trajectories. The
experimental analysis is concluded by evaluating PPO learned policy algorithm
side-by-side with frontier-based exploration strategies. A study on the
performance curves revealed that PPO-based policy was capable of performing
adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit
areas uncovered, underlying the capability of RL-based methodologies to tackle
exploration tasks efficiently. The source code can be found at:
https://github.com/dimikout3/MarsExplorer. | [
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] |
The early detection of cancer is a challenging problem in medicine. The blood
sera of cancer patients are enriched with heterogeneous secretory lipid bound
extracellular vesicles (EVs), which present a complex repertoire of information
and biomarkers, representing their cell of origin, that are being currently
studied in the field of liquid biopsy and cancer screening. Vibrational
spectroscopies provide non-invasive approaches for the assessment of structural
and biophysical properties in complex biological samples. In this pilot study,
multiple Raman spectroscopy measurements were performed on the EVs extracted
from the blood sera of 9 patients consisting of four different cancer subtypes
(colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic
cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared)
spectroscopy measurements were performed as a complementary approach to Raman
analysis, on two of the four cancer subtypes. The AdaBoost Random Forest
Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the
baseline corrected Raman spectra of cancer EVs from those of healthy controls
(18 spectra) with a classification accuracy of above 90 percent when reduced to
a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50
training: testing split. FTIR classification accuracy on 14 spectra showed an
80 percent classification accuracy. Our findings demonstrate that basic machine
learning algorithms are powerful applied intelligence tools to distinguish the
complex vibrational spectra of cancer patient EVs from those of healthy
patients. These experimental methods hold promise as valid and efficient liquid
biopsy for artificial intelligence-assisted early cancer screening. | [
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