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Due to the data shortage problem, which is one of the major problems in the
field of machine learning, the accuracy level of many applications remains well
below the expected. It prevents researchers from producing new artificial
intelligence-based systems with the available data. This problem can be solved
by generating new synthetic data with augmentation methods. In this study, new
ECG signals are produced using MIT-BIH Arrhythmia Database by using Generative
Adversarial Neural Networks (GAN), which is a modern data augmentation method.
These generated data are used for training a machine learning system and real
ECG data for testing it. The obtained results show that this way the
performance of the machine learning system is increased. | [
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] |
We formalize the problem of contextual optimization through the lens of
Bayesian experimental design and propose CO-BED -- a general, model-agnostic
framework for designing contextual experiments using information-theoretic
principles. After formulating a suitable information-based objective, we employ
black-box variational methods to simultaneously estimate it and optimize the
designs in a single stochastic gradient scheme. In addition, to accommodate
discrete actions within our framework, we propose leveraging continuous
relaxation schemes, which can naturally be integrated into our variational
objective. As a result, CO-BED provides a general and automated solution to a
wide range of contextual optimization problems. We illustrate its effectiveness
in a number of experiments, where CO-BED demonstrates competitive performance
even when compared to bespoke, model-specific alternatives. | [
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] |
In observational studies, the true causal model is typically unknown and
needs to be estimated from available observational and limited experimental
data. In such cases, the learned causal model is commonly represented as a
partially directed acyclic graph (PDAG), which contains both directed and
undirected edges indicating uncertainty of causal relations between random
variables. The main focus of this paper is on the maximal orientation task,
which, for a given PDAG, aims to orient the undirected edges maximally such
that the resulting graph represents the same Markov equivalent DAGs as the
input PDAG. This task is a subroutine used frequently in causal discovery, e.
g., as the final step of the celebrated PC algorithm. Utilizing connections to
the problem of finding a consistent DAG extension of a PDAG, we derive faster
algorithms for computing the maximal orientation by proposing two novel
approaches for extending PDAGs, both constructed with an emphasis on simplicity
and practical effectiveness. | [
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] |
The gap between the randomly initialized item ID embedding and the
well-trained warm item ID embedding makes the cold items hard to suit the
recommendation system, which is trained on the data of historical warm items.
To alleviate the performance decline of new items recommendation, the
distribution of the new item ID embedding should be close to that of the
historical warm items. To achieve this goal, we propose an Adversarial
Variational Auto-encoder Warm-up model (AVAEW) to generate warm-up item ID
embedding for cold items. Specifically, we develop a conditional variational
auto-encoder model to leverage the side information of items for generating the
warm-up item ID embedding. Particularly, we introduce an adversarial module to
enforce the alignment between warm-up item ID embedding distribution and
historical item ID embedding distribution. We demonstrate the effectiveness and
compatibility of the proposed method by extensive offline experiments on public
datasets and online A/B tests on a real-world large-scale news recommendation
platform. | [
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] |
The unit selection problem aims to identify objects, called units, that are
most likely to exhibit a desired mode of behavior when subjected to stimuli
(e.g., customers who are about to churn but would change their mind if
encouraged). Unit selection with counterfactual objective functions was
introduced relatively recently with existing work focusing on bounding a
specific class of objective functions, called the benefit functions, based on
observational and interventional data -- assuming a fully specified model is
not available to evaluate these functions. We complement this line of work by
proposing the first exact algorithm for finding optimal units given a broad
class of causal objective functions and a fully specified structural causal
model (SCM). We show that unit selection under this class of objective
functions is $\text{NP}^\text{PP}$-complete but is $\text{NP}$-complete when
unit variables correspond to all exogenous variables in the SCM. We also
provide treewidth-based complexity bounds on our proposed algorithm while
relating it to a well-known algorithm for Maximum a Posteriori (MAP) inference. | [
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] |
Automatic underwater vehicle hull Design optimization is a complex
engineering process for generating a UUV hull with optimized properties on a
given requirement. First, it involves the integration of involved
computationally complex engineering simulation tools. Second, it needs
integration of a sample efficient optimization framework with the integrated
toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool
openFoam for automatic design evaluation. For optimization, we chose Bayesian
optimization (BO), which is a well-known technique developed for optimizing
time-consuming expensive engineering simulations and has proven to be very
sample efficient in a variety of problems, including hyper-parameter tuning and
experimental design. During the optimization process, we can handle infeasible
design as constraints integrated into the optimization process. By integrating
domain-specific toolchain with AI-based optimization, we executed the automatic
design optimization of underwater vehicle hull design. For empirical
evaluation, we took two different use cases of real-world underwater vehicle
design to validate the execution of our tool. | [
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] |
In this paper, we present AR3n (pronounced as Aaron), an assist-as-needed
(AAN) controller that utilizes reinforcement learning to supply adaptive
assistance during a robot assisted handwriting rehabilitation task. Unlike
previous AAN controllers, our method does not rely on patient specific
controller parameters or physical models. We propose the use of a virtual
patient model to generalize AR3n across multiple subjects. The system modulates
robotic assistance in realtime based on a subject's tracking error, while
minimizing the amount of robotic assistance. The controller is experimentally
validated through a set of simulations and human subject experiments. Finally,
a comparative study with a traditional rule-based controller is conducted to
analyze differences in assistance mechanisms of the two controllers. | [
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] |
Recognizing already explored places (a.k.a. place recognition) is a
fundamental task in Simultaneous Localization and Mapping (SLAM) to enable
robot relocalization and loop closure detection. In topological SLAM the
recognition takes place by comparing a signature (or feature vector) associated
to the current node with the signatures of the nodes in the known map. However,
as the number of nodes increases, matching the current node signature against
all the existing ones becomes inefficient and thwarts real-time navigation. In
this paper we propose a novel approach to pre-select a subset of map nodes for
place recognition. The map nodes are clustered during exploration and each
cluster is associated with a region. The region labels become the prediction
targets of a deep neural network and, during navigation, only the nodes
associated with the regions predicted with high probability are considered for
matching. While the proposed technique can be integrated in different SLAM
approaches, in this work we describe an effective integration with RTAB-Map (a
popular framework for real-time topological SLAM) which allowed us to design
and run several experiments to demonstrate its effectiveness. All the code and
material from the experiments will be available online at
https://github.com/MI-BioLab/region-learner. | [
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Recent research has suggested different metrics to measure the inconsistency
of recommendation performance, including the accuracy difference between user
groups, miscalibration, and popularity lift. However, a study that relates
miscalibration and popularity lift to recommendation accuracy across different
user groups is still missing. Additionally, it is unclear if particular genres
contribute to the emergence of inconsistency in recommendation performance
across user groups. In this paper, we present an analysis of these three
aspects of five well-known recommendation algorithms for user groups that
differ in their preference for popular content. Additionally, we study how
different genres affect the inconsistency of recommendation performance, and
how this is aligned with the popularity of the genres. Using data from LastFm,
MovieLens, and MyAnimeList, we present two key findings. First, we find that
users with little interest in popular content receive the worst recommendation
accuracy, and that this is aligned with miscalibration and popularity lift.
Second, our experiments show that particular genres contribute to a different
extent to the inconsistency of recommendation performance, especially in terms
of miscalibration in the case of the MyAnimeList dataset. | [
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] |
We present a prototype hybrid prediction market and demonstrate the avenue it
represents for meaningful human-AI collaboration. We build on prior work
proposing artificial prediction markets as a novel machine-learning algorithm.
In an artificial prediction market, trained AI agents buy and sell outcomes of
future events. Classification decisions can be framed as outcomes of future
events, and accordingly, the price of an asset corresponding to a given
classification outcome can be taken as a proxy for the confidence of the system
in that decision. By embedding human participants in these markets alongside
bot traders, we can bring together insights from both. In this paper, we detail
pilot studies with prototype hybrid markets for the prediction of replication
study outcomes. We highlight challenges and opportunities, share insights from
semi-structured interviews with hybrid market participants, and outline a
vision for ongoing and future work. | [
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] |
Explainable artificial intelligence techniques are developed at breakneck
speed, but suitable evaluation approaches lag behind. With explainers becoming
increasingly complex and a lack of consensus on how to assess their utility, it
is challenging to judge the benefit and effectiveness of different
explanations. To address this gap, we take a step back from sophisticated
predictive algorithms and instead look into explainability of simple
decision-making models. In this setting, we aim to assess how people perceive
comprehensibility of their different representations such as mathematical
formulation, graphical representation and textual summarisation (of varying
complexity and scope). This allows us to capture how diverse stakeholders --
engineers, researchers, consumers, regulators and the like -- judge
intelligibility of fundamental concepts that more elaborate artificial
intelligence explanations are built from. This position paper charts our
approach to establishing appropriate evaluation methodology as well as a
conceptual and practical framework to facilitate setting up and executing
relevant user studies. | [
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] |
Cardinality Estimation over Knowledge Graphs (KG) is crucial for query
optimization, yet remains a challenging task due to the semi-structured nature
and complex correlations of typical Knowledge Graphs. In this work, we propose
GNCE, a novel approach that leverages knowledge graph embeddings and Graph
Neural Networks (GNN) to accurately predict the cardinality of conjunctive
queries. GNCE first creates semantically meaningful embeddings for all entities
in the KG, which are then integrated into the given query, which is processed
by a GNN to estimate the cardinality of the query. We evaluate GNCE on several
KGs in terms of q-Error and demonstrate that it outperforms state-of-the-art
approaches based on sampling, summaries, and (machine) learning in terms of
estimation accuracy while also having lower execution time and less parameters.
Additionally, we show that GNCE can inductively generalise to unseen entities,
making it suitable for use in dynamic query processing scenarios. Our proposed
approach has the potential to significantly improve query optimization and
related applications that rely on accurate cardinality estimates of conjunctive
queries. | [
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] |
Privacy enhancing technologies (PETs) have been proposed as a way to protect
the privacy of data while still allowing for data analysis. In this work, we
focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for
arbitrary computations to be performed on encrypted data. FHE has received lots
of attention in the past few years and has reached realistic execution times
and correctness.
More precisely, we explain in this paper how we apply FHE to tree-based
models and get state-of-the-art solutions over encrypted tabular data. We show
that our method is applicable to a wide range of tree-based models, including
decision trees, random forests, and gradient boosted trees, and has been
implemented within the Concrete-ML library, which is open-source at
https://github.com/zama-ai/concrete-ml. With a selected set of use-cases, we
demonstrate that our FHE version is very close to the unprotected version in
terms of accuracy. | [
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] |
The recent efforts in automation of machine learning or data science has
achieved success in various tasks such as hyper-parameter optimization or model
selection. However, key areas such as utilizing domain knowledge and data
semantics are areas where we have seen little automation. Data Scientists have
long leveraged common sense reasoning and domain knowledge to understand and
enrich data for building predictive models. In this paper we discuss important
shortcomings of current data science and machine learning solutions. We then
envision how leveraging "semantic" understanding and reasoning on data in
combination with novel tools for data science automation can help with
consistent and explainable data augmentation and transformation. Additionally,
we discuss how semantics can assist data scientists in a new manner by helping
with challenges related to trust, bias, and explainability in machine learning.
Semantic annotation can also help better explore and organize large data
sources. | [
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In the experts problem, on each of $T$ days, an agent needs to follow the
advice of one of $n$ ``experts''. After each day, the loss associated with each
expert's advice is revealed. A fundamental result in learning theory says that
the agent can achieve vanishing regret, i.e. their cumulative loss is within
$o(T)$ of the cumulative loss of the best-in-hindsight expert.
Can the agent perform well without sufficient space to remember all the
experts? We extend a nascent line of research on this question in two
directions:
$\bullet$ We give a new algorithm against the oblivious adversary, improving
over the memory-regret tradeoff obtained by [PZ23], and nearly matching the
lower bound of [SWXZ22].
$\bullet$ We also consider an adaptive adversary who can observe past experts
chosen by the agent. In this setting we give both a new algorithm and a novel
lower bound, proving that roughly $\sqrt{n}$ memory is both necessary and
sufficient for obtaining $o(T)$ regret. | [
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] |
Energy market rules should incentivize market participants to behave in a
market and grid conform way. However, they can also provide incentives for
undesired and unexpected strategies if the market design is flawed. Multi-agent
Reinforcement learning (MARL) is a promising new approach to predicting the
expected profit-maximizing behavior of energy market participants in
simulation. However, reinforcement learning requires many interactions with the
system to converge, and the power system environment often consists of
extensive computations, e.g., optimal power flow (OPF) calculation for market
clearing. To tackle this complexity, we provide a model of the energy market to
a basic MARL algorithm in the form of a learned OPF approximation and explicit
market rules. The learned OPF surrogate model makes an explicit solving of the
OPF completely unnecessary. Our experiments demonstrate that the model
additionally reduces training time by about one order of magnitude but at the
cost of a slightly worse performance. Potential applications of our method are
market design, more realistic modeling of market participants, and analysis of
manipulative behavior. | [
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] |
Approaches to fair and ethical AI have recently fell under the scrutiny of
the emerging, chiefly qualitative, field of critical data studies, placing
emphasis on the lack of sensitivity to context and complex social phenomena of
such interventions. We employ some of these lessons to introduce a tripartite
decision-making toolkit, informed by dilemmas encountered in the pursuit of
responsible AI/ML. These are: (a) the opportunity dilemma between the
availability of data shaping problem statements vs problem statements shaping
data; (b) the trade-off between scalability and contextualizability (too much
data versus too specific data); and (c) the epistemic positioning between the
pragmatic technical objectivism and the reflexive relativism in acknowledging
the social. This paper advocates for a situated reasoning and creative
engagement with the dilemmas surrounding responsible algorithmic/data-driven
systems, and going beyond the formulaic bias elimination and ethics
operationalization narratives found in the fair-AI literature. | [
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] |
Artificial Intelligence and digital health have the potential to transform
global health. However, having access to representative data to test and
validate algorithms in realistic production environments is essential. We
introduce HealthSyn, an open-source synthetic data generator of user behavior
for testing reinforcement learning algorithms in the context of mobile health
interventions. The generator utilizes Markov processes to generate diverse user
actions, with individual user behavioral patterns that can change in reaction
to personalized interventions (i.e., reminders, recommendations, and
incentives). These actions are translated into actual logs using an ML-purposed
data schema specific to the mobile health application functionality included
with HealthKit, and open-source SDK. The logs can be fed to pipelines to obtain
user metrics. The generated data, which is based on real-world behaviors and
simulation techniques, can be used to develop, test, and evaluate, both ML
algorithms in research and end-to-end operational RL-based intervention
delivery frameworks. | [
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] |
Recent work have demonstrated that robustness (to "corruption") can be at
odds with generalization. Adversarial training, for instance, aims to reduce
the problematic susceptibility of modern neural networks to small data
perturbations. Surprisingly, overfitting is a major concern in adversarial
training despite being mostly absent in standard training. We provide here
theoretical evidence for this peculiar "robust overfitting" phenomenon.
Subsequently, we advance a novel distributionally robust loss function bridging
robustness and generalization. We demonstrate both theoretically as well as
empirically the loss to enjoy a certified level of robustness against two
common types of corruption--data evasion and poisoning attacks--while ensuring
guaranteed generalization. We show through careful numerical experiments that
our resulting holistic robust (HR) training procedure yields SOTA performance.
Finally, we indicate that HR training can be interpreted as a direct extension
of adversarial training and comes with a negligible additional computational
burden. A ready-to-use python library implementing our algorithm is available
at https://github.com/RyanLucas3/HR_Neural_Networks. | [
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] |
Machine learning practitioners often end up tunneling on low-level technical
details like model architectures and performance metrics. Could early model
development instead focus on high-level questions of which factors a model
ought to pay attention to? Inspired by the practice of sketching in design,
which distills ideas to their minimal representation, we introduce model
sketching: a technical framework for iteratively and rapidly authoring
functional approximations of a machine learning model's decision-making logic.
Model sketching refocuses practitioner attention on composing high-level,
human-understandable concepts that the model is expected to reason over (e.g.,
profanity, racism, or sarcasm in a content moderation task) using zero-shot
concept instantiation. In an evaluation with 17 ML practitioners, model
sketching reframed thinking from implementation to higher-level exploration,
prompted iteration on a broader range of model designs, and helped identify
gaps in the problem formulation$\unicode{x2014}$all in a fraction of the time
ordinarily required to build a model. | [
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] |
Disadvantaged communities (DAC), as defined by the Justice40 initiative of
the Department of Energy (DOE), USA, identifies census tracts across the USA to
determine where benefits of climate and energy investments are or are not
currently accruing. The DAC status not only helps in determining the
eligibility for future Justice40-related investments but is also critical for
exploring ways to achieve equitable distribution of resources. However,
designing inclusive and equitable strategies not just requires a good
understanding of current demographics, but also a deeper analysis of the
transformations that happened in those demographics over the years. In this
paper, machine learning (ML) models are trained on publicly available census
data from recent years to classify the DAC status at the census tracts level
and then the trained model is used to classify DAC status for historical years.
A detailed analysis of the feature and model selection along with the evolution
of disadvantaged communities between 2013 and 2018 is presented in this study. | [
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] |
We introduce a Python open-source library for $\mathcal{X}$-armed bandit and
online blackbox optimization named PyXAB. PyXAB contains the implementations
for more than 10 $\mathcal{X}$-armed bandit algorithms, such as HOO, StoSOO,
HCT, and the most recent works GPO and VHCT. PyXAB also provides the most
commonly-used synthetic objectives to evaluate the performance of different
algorithms and the various choices of the hierarchical partitions on the
parameter space. The online documentation for PyXAB includes clear instructions
for installation, straight-forward examples, detailed feature descriptions, and
a complete reference of the API. PyXAB is released under the MIT license in
order to encourage both academic and industrial usage. The library can be
directly installed from PyPI with its source code available at
https://github.com/WilliamLwj/PyXAB | [
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] |
Tasks where the set of possible actions depend discontinuously on the state
pose a significant challenge for current reinforcement learning algorithms. For
example, a locked door must be first unlocked, and then the handle turned
before the door can be opened. The sequential nature of these tasks makes
obtaining final rewards difficult, and transferring information between task
variants using continuous learned values such as weights rather than discrete
symbols can be inefficient. Our key insight is that agents that act and think
symbolically are often more effective in dealing with these tasks. We propose a
memory-based learning approach that leverages the symbolic nature of
constraints and temporal ordering of actions in these tasks to quickly acquire
and transfer high-level information. We evaluate the performance of
memory-based learning on both real and simulated tasks with approximately
discontinuous constraints between states and actions, and show our method
learns to solve these tasks an order of magnitude faster than both model-based
and model-free deep reinforcement learning methods. | [
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] |
A rigorous formalization of desired system requirements is indispensable when
performing any verification task. This often limits the application of
verification techniques, as writing formal specifications is an error-prone and
time-consuming manual task. To facilitate this, we present nl2spec, a framework
for applying Large Language Models (LLMs) to derive formal specifications (in
temporal logics) from unstructured natural language. In particular, we
introduce a new methodology to detect and resolve the inherent ambiguity of
system requirements in natural language: we utilize LLMs to map subformulas of
the formalization back to the corresponding natural language fragments of the
input. Users iteratively add, delete, and edit these sub-translations to amend
erroneous formalizations, which is easier than manually redrafting the entire
formalization. The framework is agnostic to specific application domains and
can be extended to similar specification languages and new neural models. We
perform a user study to obtain a challenging dataset, which we use to run
experiments on the quality of translations. We provide an open-source
implementation, including a web-based frontend. | [
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] |
Reinforcement learning requires interaction with an environment, which is
expensive for robots. This constraint necessitates approaches that work with
limited environmental interaction by maximizing the reuse of previous
experiences. We propose an approach that maximizes experience reuse while
learning to solve a given task by generating and simultaneously learning useful
auxiliary tasks. To generate these tasks, we construct an abstract temporal
logic representation of the given task and leverage large language models to
generate context-aware object embeddings that facilitate object replacements.
Counterfactual reasoning and off-policy methods allow us to simultaneously
learn these auxiliary tasks while solving the given target task. We combine
these insights into a novel framework for multitask reinforcement learning and
experimentally show that our generated auxiliary tasks share similar underlying
exploration requirements as the given task, thereby maximizing the utility of
directed exploration. Our approach allows agents to automatically learn
additional useful policies without extra environment interaction. | [
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] |
We present a framework for learning useful subgoals that support efficient
long-term planning to achieve novel goals. At the core of our framework is a
collection of rational subgoals (RSGs), which are essentially binary
classifiers over the environmental states. RSGs can be learned from
weakly-annotated data, in the form of unsegmented demonstration trajectories,
paired with abstract task descriptions, which are composed of terms initially
unknown to the agent (e.g., collect-wood then craft-boat then go-across-river).
Our framework also discovers dependencies between RSGs, e.g., the task
collect-wood is a helpful subgoal for the task craft-boat. Given a goal
description, the learned subgoals and the derived dependencies facilitate
off-the-shelf planning algorithms, such as A* and RRT, by setting helpful
subgoals as waypoints to the planner, which significantly improves
performance-time efficiency. | [
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] |
This paper studies a model learning and online planning approach towards
building flexible and general robots. Specifically, we investigate how to
exploit the locality and sparsity structures in the underlying environmental
transition model to improve model generalization, data-efficiency, and
runtime-efficiency. We present a new domain definition language, named
PDSketch. It allows users to flexibly define high-level structures in the
transition models, such as object and feature dependencies, in a way similar to
how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden
dimensions of a convolutional neural network. The details of the transition
model will be filled in by trainable neural networks. Based on the defined
structures and learned parameters, PDSketch automatically generates
domain-independent planning heuristics without additional training. The derived
heuristics accelerate the performance-time planning for novel goals. | [
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] |
Choice problems refer to selecting the best choices from several items, and
learning users' preferences in choice problems is of great significance in
understanding the decision making mechanisms and providing personalized
services. Existing works typically assume that people evaluate items
independently. In practice, however, users' preferences depend on the market in
which items are placed, which is known as context effects; and the order of
users' preferences for two items may even be reversed, which is referred to
preference reversals. In this work, we identify three factors contributing to
context effects: users' adaptive weights, the inter-item comparison, and
display positions. We propose a context-dependent preference model named Pacos
as a unified framework for addressing three factors simultaneously, and
consider two design methods including an additive method with high
interpretability and an ANN-based method with high accuracy. We study the
conditions for preference reversals to occur and provide an theoretical proof
of the effectiveness of Pacos in addressing preference reversals. Experimental
results show that the proposed method has better performance than prior works
in predicting users' choices, and has great interpretability to help understand
the cause of preference reversals. | [
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] |
Displaying confidence scores in human-AI interaction has been shown to help
build trust between humans and AI systems. However, most existing research uses
only the confidence score as a form of communication. As confidence scores are
just another model output, users may want to understand why the algorithm is
confident to determine whether to accept the confidence score. In this paper,
we show that counterfactual explanations of confidence scores help study
participants to better understand and better trust a machine learning model's
prediction. We present two methods for understanding model confidence using
counterfactual explanation: (1) based on counterfactual examples; and (2) based
on visualisation of the counterfactual space. Both increase understanding and
trust for study participants over a baseline of no explanation, but qualitative
results show that they are used quite differently, leading to recommendations
of when to use each one and directions of designing better explanations. | [
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] |
Developing automated and smart software vulnerability detection models has
been receiving great attention from both research and development communities.
One of the biggest challenges in this area is the lack of code samples for all
different programming languages. In this study, we address this issue by
proposing a transfer learning technique to leverage available datasets and
generate a model to detect common vulnerabilities in different programming
languages. We use C source code samples to train a Convolutional Neural Network
(CNN) model, then, we use Java source code samples to adopt and evaluate the
learned model. We use code samples from two benchmark datasets: NIST Software
Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show
that proposed model detects vulnerabilities in both C and Java codes with
average recall of 72\%. Additionally, we employ explainable AI to investigate
how much each feature contributes to the knowledge transfer mechanisms between
C and Java in the proposed model. | [
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] |
Sequential recommender systems aim to predict users' next interested item
given their historical interactions. However, a long-standing issue is how to
distinguish between users' long/short-term interests, which may be
heterogeneous and contribute differently to the next recommendation. Existing
approaches usually set pre-defined short-term interest length by exhaustive
search or empirical experience, which is either highly inefficient or yields
subpar results. The recent advanced transformer-based models can achieve
state-of-the-art performances despite the aforementioned issue, but they have a
quadratic computational complexity to the length of the input sequence. To this
end, this paper proposes a novel sequential recommender system, AutoMLP, aiming
for better modeling users' long/short-term interests from their historical
interactions. In addition, we design an automated and adaptive search algorithm
for preferable short-term interest length via end-to-end optimization. Through
extensive experiments, we show that AutoMLP has competitive performance against
state-of-the-art methods, while maintaining linear computational complexity. | [
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] |
Brain network analysis is essential for diagnosing and intervention for
Alzheimer's disease (AD). However, previous research relied primarily on
specific time-consuming and subjective toolkits. Only few tools can obtain the
structural brain networks from brain diffusion tensor images (DTI). In this
paper, we propose a diffusion based end-to-end brain network generative model
Brain Diffuser that directly shapes the structural brain networks from DTI.
Compared to existing toolkits, Brain Diffuser exploits more structural
connectivity features and disease-related information by analyzing disparities
in structural brain networks across subjects. For the case of Alzheimer's
disease, the proposed model performs better than the results from existing
toolkits on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. | [
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] |
The use of Shap scores has become widespread in Explainable AI. However,
their computation is in general intractable, in particular when done with a
black-box classifier, such as neural network. Recent research has unveiled
classes of open-box Boolean Circuit classifiers for which Shap can be computed
efficiently. We show how to transform binary neural networks into those
circuits for efficient Shap computation.We use logic-based knowledge
compilation techniques. The performance gain is huge, as we show in the light
of our experiments. | [
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] |
As a present to Mizar on its 50th anniversary, we develop an AI/TP system
that automatically proves about 60\% of the Mizar theorems in the hammer
setting. We also automatically prove 75\% of the Mizar theorems when the
automated provers are helped by using only the premises used in the
human-written Mizar proofs. We describe the methods and large-scale experiments
leading to these results. This includes in particular the E and Vampire
provers, their ENIGMA and Deepire learning modifications, a number of
learning-based premise selection methods, and the incremental loop that
interleaves growing a corpus of millions of ATP proofs with training
increasingly strong AI/TP systems on them. We also present a selection of Mizar
problems that were proved automatically. | [
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In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly
autonomously in solving a task, but can request help from an external expert
when needed. However, knowing when to request such assistance is critical: too
few requests can lead to the robot making mistakes, but too many requests can
overload the expert. In this paper, we present a Reinforcement Learning based
approach to this problem, where a semi-autonomous agent asks for external
assistance when it has low confidence in the eventual success of the task. The
confidence level is computed by estimating the variance of the return from the
current state. We show that this estimate can be iteratively improved during
training using a Bellman-like recursion. On discrete navigation problems with
both fully- and partially-observable state information, we show that our method
makes effective use of a limited budget of expert calls at run-time, despite
having no access to the expert at training time. | [
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] |
Artwork recommendation is challenging because it requires understanding how
users interact with highly subjective content, the complexity of the concepts
embedded within the artwork, and the emotional and cognitive reflections they
may trigger in users. In this paper, we focus on efficiently capturing the
elements (i.e., latent semantic relationships) of visual art for personalized
recommendation. We propose and study recommender systems based on textual and
visual feature learning techniques, as well as their combinations. We then
perform a small-scale and a large-scale user-centric evaluation of the quality
of the recommendations. Our results indicate that textual features compare
favourably with visual ones, whereas a fusion of both captures the most
suitable hidden semantic relationships for artwork recommendation. Ultimately,
this paper contributes to our understanding of how to deliver content that
suitably matches the user's interests and how they are perceived. | [
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] |
Researchers apply machine-learning techniques for code smell detection to
counter the subjectivity of many code smells. Such approaches need a large,
manually annotated dataset for training and benchmarking. Existing literature
offers a few datasets; however, they are small in size and, more importantly,
do not focus on the subjective code snippets. In this paper, we present DACOS,
a manually annotated dataset containing 10,267 annotations for 5,192 code
snippets. The dataset targets three kinds of code smells at different
granularity: multifaceted abstraction, complex method, and long parameter list.
The dataset is created in two phases. The first phase helps us identify the
code snippets that are potentially subjective by determining the thresholds of
metrics used to detect a smell. The second phase collects annotations for
potentially subjective snippets. We also offer an extended dataset DACOSX that
includes definitely benign and definitely smelly snippets by using the
thresholds identified in the first phase. We have developed TagMan, a web
application to help annotators view and mark the snippets one-by-one and record
the provided annotations. We make the datasets and the web application
accessible publicly. This dataset will help researchers working on smell
detection techniques to build relevant and context-aware machine-learning
models. | [
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] |
A rich representation is key to general robotic manipulation, but existing
approaches to representation learning require large amounts of multimodal
demonstrations. In this work we propose PLEX, a transformer-based architecture
that learns from a small amount of task-agnostic visuomotor trajectories and a
much larger amount of task-conditioned object manipulation videos -- a type of
data available in quantity. PLEX uses visuomotor trajectories to induce a
latent feature space and to learn task-agnostic manipulation routines, while
diverse video-only demonstrations teach PLEX how to plan in the induced latent
feature space for a wide variety of tasks. Experiments showcase PLEX's
generalization on Meta-World and SOTA performance in challenging Robosuite
environments. In particular, using relative positional encoding in PLEX's
transformers greatly helps in low-data regimes of learning from human-collected
demonstrations. The paper's accompanying code and data are available at
https://microsoft.github.io/PLEX. | [
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] |
Variable-rate mechanism has improved the flexibility and efficiency of
learning-based image compression that trains multiple models for different
rate-distortion tradeoffs. One of the most common approaches for variable-rate
is to channel-wisely or spatial-uniformly scale the internal features. However,
the diversity of spatial importance is instructive for bit allocation of image
compression. In this paper, we introduce a Spatial Importance Guided
Variable-rate Image Compression (SigVIC), in which a spatial gating unit (SGU)
is designed for adaptively learning a spatial importance mask. Then, a spatial
scaling network (SSN) takes the spatial importance mask to guide the feature
scaling and bit allocation for variable-rate. Moreover, to improve the quality
of decoded image, Top-K shallow features are selected to refine the decoded
features through a shallow feature fusion module (SFFM). Experiments show that
our method outperforms other learning-based methods (whether variable-rate or
not) and traditional codecs, with storage saving and high flexibility. | [
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] |
Detecting hidden geometrical structures from surface measurements under
electromagnetic, acoustic, or mechanical loading is the goal of noninvasive
imaging techniques in medical and industrial applications. Solving the inverse
problem can be challenging due to the unknown topology and geometry, the
sparsity of the data, and the complexity of the physical laws. Physics-informed
neural networks (PINNs) have shown promise as a simple-yet-powerful tool for
problem inversion, but they have yet to be applied to general problems with a
priori unknown topology. Here, we introduce a topology optimization framework
based on PINNs that solves geometry detection problems without prior knowledge
of the number or types of shapes. We allow for arbitrary solution topology by
representing the geometry using a material density field that approaches binary
values thanks to a novel eikonal regularization. We validate our framework by
detecting the number, locations, and shapes of hidden voids and inclusions in
linear and nonlinear elastic bodies using measurements of outer surface
displacement from a single mechanical loading experiment. Our methodology opens
a pathway for PINNs to solve various engineering problems targeting geometry
optimization. | [
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] |
Component-based development is one of the core principles behind modern
software engineering practices. Understanding of causal relationships between
components of a software system can yield significant benefits to developers.
Yet modern software design approaches make it difficult to track and discover
such relationships at system scale, which leads to growing intellectual debt.
In this paper we consider an alternative approach to software design,
flow-based programming (FBP), and draw the attention of the community to the
connection between dataflow graphs produced by FBP and structural causal
models. With expository examples we show how this connection can be leveraged
to improve day-to-day tasks in software projects, including fault localisation,
business analysis and experimentation. | [
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] |
Implicit gender bias in software development is a well-documented issue, such
as the association of technical roles with men. To address this bias, it is
important to understand it in more detail. This study uses data mining
techniques to investigate the extent to which 56 tasks related to software
development, such as assigning GitHub issues and testing, are affected by
implicit gender bias embedded in large language models. We systematically
translated each task from English into a genderless language and back, and
investigated the pronouns associated with each task. Based on translating each
task 100 times in different permutations, we identify a significant disparity
in the gendered pronoun associations with different tasks. Specifically,
requirements elicitation was associated with the pronoun "he" in only 6% of
cases, while testing was associated with "he" in 100% of cases. Additionally,
tasks related to helping others had a 91% association with "he" while the same
association for tasks related to asking coworkers was only 52%. These findings
reveal a clear pattern of gender bias related to software development tasks and
have important implications for addressing this issue both in the training of
large language models and in broader society. | [
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] |
Online mental health communities (OMHCs) are an effective and accessible
channel to give and receive social support for individuals with mental and
emotional issues. However, a key challenge on these platforms is finding
suitable partners to interact with given that mechanisms to match users are
currently underdeveloped. In this paper, we collaborate with one of the world's
largest OMHC to develop an agent-based simulation framework and explore the
trade-offs in different matching algorithms. The simulation framework allows us
to compare current mechanisms and new algorithmic matching policies on the
platform, and observe their differing effects on a variety of outcome metrics.
Our findings include that usage of the deferred-acceptance algorithm can
significantly better the experiences of support-seekers in one-on-one chats
while maintaining low waiting time. We note key design considerations that
agent-based modeling reveals in the OMHC context, including the potential
benefits of algorithmic matching on marginalized communities. | [
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] |
Using deep learning methods to classify EEG signals can accurately identify
people's emotions. However, existing studies have rarely considered the
application of the information in another domain's representations to feature
selection in the time-frequency domain. We propose a classification network of
EEG signals based on the cross-domain feature fusion method, which makes the
network more focused on the features most related to brain activities and
thinking changes by using the multi-domain attention mechanism. In addition, we
propose a two-step fusion method and apply these methods to the EEG emotion
recognition network. Experimental results show that our proposed network, which
combines multiple representations in the time-frequency domain and spatial
domain, outperforms previous methods on public datasets and achieves
state-of-the-art at present. | [
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] |
Deductive domains are typical of many cognitive skills in that no single
problem-solving strategy is always optimal for solving all problems. It was
shown that students who know how and when to use each strategy (StrTime)
outperformed those who know neither and stick to the default strategy
(Default). In this work, students were trained on a logic tutor that supports a
default forward-chaining and a backward-chaining (BC) strategy, then a
probability tutor that only supports BC. We investigated three types of
interventions on teaching the Default students how and when to use which
strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime
students received no interventions. Overall, our results show that Nudge
outperformed their Default peers and caught up with StrTime on both tutors. | [
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Metacognitive skills have been commonly associated with preparation for
future learning in deductive domains. Many researchers have regarded strategy-
and time-awareness as two metacognitive skills that address how and when to use
a problem-solving strategy, respectively. It was shown that students who are
both strategy-and time-aware (StrTime) outperformed their nonStrTime peers
across deductive domains. In this work, students were trained on a logic tutor
that supports a default forward-chaining (FC) and a backward-chaining (BC)
strategy. We investigated the impact of mixing BC with FC on teaching strategy-
and time-awareness for nonStrTime students. During the logic instruction, the
experimental students (Exp) were provided with two BC worked examples and some
problems in BC to practice how and when to use BC. Meanwhile, their control
(Ctrl) and StrTime peers received no such intervention. Six weeks later, all
students went through a probability tutor that only supports BC to evaluate
whether the acquired metacognitive skills are transferred from logic. Our
results show that on both tutors, Exp outperformed Ctrl and caught up with
StrTime. | [
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] |
Intelligent intersection managers can improve safety by detecting dangerous
drivers or failure modes in autonomous vehicles, warning oncoming vehicles as
they approach an intersection. In this work, we present FailureNet, a recurrent
neural network trained end-to-end on trajectories of both nominal and reckless
drivers in a scaled miniature city. FailureNet observes the poses of vehicles
as they approach an intersection and detects whether a failure is present in
the autonomy stack, warning cross-traffic of potentially dangerous drivers.
FailureNet can accurately identify control failures, upstream perception
errors, and speeding drivers, distinguishing them from nominal driving. The
network is trained and deployed with autonomous vehicles in the MiniCity.
Compared to speed or frequency-based predictors, FailureNet's recurrent neural
network structure provides improved predictive power, yielding upwards of 84%
accuracy when deployed on hardware. | [
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] |
Numerous parties are calling for the democratisation of AI, but the phrase is
used to refer to a variety of goals, the pursuit of which sometimes conflict.
This paper identifies four kinds of AI democratisation that are commonly
discussed: (1) the democratisation of AI use, (2) the democratisation of AI
development, (3) the democratisation of AI profits, and (4) the democratisation
of AI governance. Numerous goals and methods of achieving each form of
democratisation are discussed. The main takeaway from this paper is that AI
democratisation is a multifarious and sometimes conflicting concept that should
not be conflated with improving AI accessibility. If we want to move beyond
ambiguous commitments to democratising AI, to productive discussions of
concrete policies and trade-offs, then we need to recognise the principal role
of the democratisation of AI governance in navigating tradeoffs and risks
across decisions around use, development, and profits. | [
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] |
In this paper, we introduce a new approach, called Posthoc Interpretation via
Quantization (PIQ), for interpreting decisions made by trained classifiers. Our
method utilizes vector quantization to transform the representations of a
classifier into a discrete, class-specific latent space. The class-specific
codebooks act as a bottleneck that forces the interpreter to focus on the parts
of the input data deemed relevant by the classifier for making a prediction.
Our model formulation also enables learning concepts by incorporating the
supervision of pretrained annotation models such as state-of-the-art image
segmentation models. We evaluated our method through quantitative and
qualitative studies involving black-and-white images, color images, and audio.
As a result of these studies we found that PIQ generates interpretations that
are more easily understood by participants to our user studies when compared to
several other interpretation methods in the literature. | [
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] |
Deep Learning (DL) developers come from different backgrounds, e.g.,
medicine, genomics, finance, and computer science. To create a DL model, they
must learn and use high-level programming languages (e.g., Python), thus
needing to handle related setups and solve programming errors. This paper
presents DeepBlocks, a visual programming tool that allows DL developers to
design, train, and evaluate models without relying on specific programming
languages. DeepBlocks works by building on the typical model structure: a
sequence of learnable functions whose arrangement defines the specific
characteristics of the model. We derived DeepBlocks' design goals from a
5-participants formative interview, and we validated the first implementation
of the tool through a typical use case. Results are promising and show that
developers could visually design complex DL architectures. | [
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] |
The neural network has become an integral part of modern software systems.
However, they still suffer from various problems, in particular, vulnerability
to adversarial attacks. In this work, we present a novel program reasoning
framework for neural-network verification, which we refer to as symbolic
reasoning. The key components of our framework are the use of the symbolic
domain and the quadratic relation. The symbolic domain has very flexible
semantics, and the quadratic relation is quite expressive. They allow us to
encode many verification problems for neural networks as quadratic programs.
Our scheme then relaxes the quadratic programs to semidefinite programs, which
can be efficiently solved. This framework allows us to verify various
neural-network properties under different scenarios, especially those that
appear challenging for non-symbolic domains. Moreover, it introduces new
representations and perspectives for the verification tasks. We believe that
our framework can bring new theoretical insights and practical tools to
verification problems for neural networks. | [
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Algorithms are used to aid human decision makers by making predictions and
recommending decisions. Currently, these algorithms are trained to optimize
prediction accuracy. What if they were optimized to control final decisions? In
this paper, we study a decision-aid algorithm that learns about the human
decision maker and provides ''personalized recommendations'' to influence final
decisions. We first consider fixed human decision functions which map
observable features and the algorithm's recommendations to final decisions. We
characterize the conditions under which perfect control over final decisions is
attainable. Under fairly general assumptions, the parameters of the human
decision function can be identified from past interactions between the
algorithm and the human decision maker, even when the algorithm was constrained
to make truthful recommendations. We then consider a decision maker who is
aware of the algorithm's manipulation and responds strategically. By posing the
setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we
show that all equilibria are partition equilibria where only coarse information
is shared: the algorithm recommends an interval containing the ideal decision.
We discuss the potential applications of such algorithms and their social
implications. | [
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] |
Planning agents are ill-equipped to act in novel situations in which their
domain model no longer accurately represents the world. We introduce an
approach for such agents operating in open worlds that detects the presence of
novelties and effectively adapts their domain models and consequent action
selection. It uses observations of action execution and measures their
divergence from what is expected, according to the environment model, to infer
existence of a novelty. Then, it revises the model through a heuristics-guided
search over model changes. We report empirical evaluations on the CartPole
problem, a standard Reinforcement Learning (RL) benchmark. The results show
that our approach can deal with a class of novelties very quickly and in an
interpretable fashion. | [
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] |
Structure perception is a fundamental aspect of music cognition in humans.
Historically, the hierarchical organization of music into structures served as
a narrative device for conveying meaning, creating expectancy, and evoking
emotions in the listener. Thereby, musical structures play an essential role in
music composition, as they shape the musical discourse through which the
composer organises his ideas. In this paper, we present a novel music
segmentation method, pitchclass2vec, based on symbolic chord annotations, which
are embedded into continuous vector representations using both natural language
processing techniques and custom-made encodings. Our algorithm is based on
long-short term memory (LSTM) neural network and outperforms the
state-of-the-art techniques based on symbolic chord annotations in the field. | [
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Graph data is omnipresent and has a wide variety of applications, such as in
natural science, social networks, or the semantic web. However, while being
rich in information, graphs are often noisy and incomplete. As a result, graph
completion tasks, such as node classification or link prediction, have gained
attention. On one hand, neural methods, such as graph neural networks, have
proven to be robust tools for learning rich representations of noisy graphs. On
the other hand, symbolic methods enable exact reasoning on graphs.We propose
Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework
for graph completion that combines both paradigms as it allows for the
integration of prior knowledge into a graph neural network model.Essentially,
KeGNN consists of a graph neural network as a base upon which knowledge
enhancement layers are stacked with the goal of refining predictions with
respect to prior knowledge.We instantiate KeGNN in conjunction with two
state-of-the-art graph neural networks, Graph Convolutional Networks and Graph
Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node
classification. | [
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] |
Neural text-to-speech (TTS) models can synthesize natural human speech when
trained on large amounts of transcribed speech. However, collecting such
large-scale transcribed data is expensive. This paper proposes an unsupervised
pre-training method for a sequence-to-sequence TTS model by leveraging large
untranscribed speech data. With our pre-training, we can remarkably reduce the
amount of paired transcribed data required to train the model for the target
downstream TTS task. The main idea is to pre-train the model to reconstruct
de-warped mel-spectrograms from warped ones, which may allow the model to learn
proper temporal assignment relation between input and output sequences. In
addition, we propose a data augmentation method that further improves the data
efficiency in fine-tuning. We empirically demonstrate the effectiveness of our
proposed method in low-resource language scenarios, achieving outstanding
performance compared to competing methods. The code and audio samples are
available at: https://github.com/cnaigithub/SpeechDewarping | [
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Optimization problems involving sequential decisions in a stochastic
environment were studied in Stochastic Programming (SP), Stochastic Optimal
Control (SOC) and Markov Decision Processes (MDP). In this paper we mainly
concentrate on SP and SOC modelling approaches. In these frameworks there are
natural situations when the considered problems are convex. Classical approach
to sequential optimization is based on dynamic programming. It has the problem
of the so-called ``Curse of Dimensionality", in that its computational
complexity increases exponentially with increase of dimension of state
variables. Recent progress in solving convex multistage stochastic problems is
based on cutting planes approximations of the cost-to-go (value) functions of
dynamic programming equations. Cutting planes type algorithms in dynamical
settings is one of the main topics of this paper. We also discuss Stochastic
Approximation type methods applied to multistage stochastic optimization
problems. From the computational complexity point of view, these two types of
methods seem to be complimentary to each other. Cutting plane type methods can
handle multistage problems with a large number of stages, but a relatively
smaller number of state (decision) variables. On the other hand, stochastic
approximation type methods can only deal with a small number of stages, but a
large number of decision variables. | [
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] |
We present a multimodal deep learning (MDL) framework for predicting physical
properties of a 10-dimensional acrylic polymer composite material by merging
physical attributes and chemical data. Our MDL model comprises four modules,
including three generative deep learning models for material structure
characterization and a fourth model for property prediction. Our approach
handles an 18-dimensional complexity, with 10 compositional inputs and 8
property outputs, successfully predicting 913,680 property data points across
114,210 composition conditions. This level of complexity is unprecedented in
computational materials science, particularly for materials with undefined
structures. We propose a framework to analyze the high-dimensional information
space for inverse material design, demonstrating flexibility and adaptability
to various materials and scales, provided sufficient data is available. This
study advances future research on different materials and the development of
more sophisticated models, drawing us closer to the ultimate goal of predicting
all properties of all materials. | [
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] |
There are plenty of graph neural network (GNN) accelerators being proposed.
However, they highly rely on users' hardware expertise and are usually
optimized for one specific GNN model, making them challenging for practical
use. Therefore, in this work, we propose GNNBuilder, the first automated,
generic, end-to-end GNN accelerator generation framework. It features four
advantages: (1) GNNBuilder can automatically generate GNN accelerators for a
wide range of GNN models arbitrarily defined by users; (2) GNNBuilder takes
standard PyTorch programming interface, introducing zero overhead for algorithm
developers; (3) GNNBuilder supports end-to-end code generation, simulation,
accelerator optimization, and hardware deployment, realizing a push-button
fashion for GNN accelerator design; (4) GNNBuilder is equipped with accurate
performance models of its generated accelerator, enabling fast and flexible
design space exploration (DSE). In the experiments, first, we show that our
accelerator performance model has errors within $36\%$ for latency prediction
and $18\%$ for BRAM count prediction. Second, we show that our generated
accelerators can outperform CPU by $6.33\times$ and GPU by $6.87\times$. This
framework is open-source, and the code is available at
https://github.com/sharc-lab/gnn-builder. | [
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With the continuous growth in communication network complexity and traffic
volume, communication load balancing solutions are receiving increasing
attention. Specifically, reinforcement learning (RL)-based methods have shown
impressive performance compared with traditional rule-based methods. However,
standard RL methods generally require an enormous amount of data to train, and
generalize poorly to scenarios that are not encountered during training. We
propose a policy reuse framework in which a policy selector chooses the most
suitable pre-trained RL policy to execute based on the current traffic
condition. Our method hinges on a policy bank composed of policies trained on a
diverse set of traffic scenarios. When deploying to an unknown traffic
scenario, we select a policy from the policy bank based on the similarity
between the previous-day traffic of the current scenario and the traffic
observed during training. Experiments demonstrate that this framework can
outperform classical and adaptive rule-based methods by a large margin. | [
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] |
With the phenomenal success of diffusion models and ChatGPT, deep generation
models (DGMs) have been experiencing explosive growth from 2022. Not limited to
content generation, DGMs are also widely adopted in Internet of Things,
Metaverse, and digital twin, due to their outstanding ability to represent
complex patterns and generate plausible samples. In this article, we explore
the applications of DGMs in a crucial task, i.e., improving the efficiency of
wireless network management. Specifically, we firstly overview the generative
AI, as well as three representative DGMs. Then, a DGM-empowered framework for
wireless network management is proposed, in which we elaborate the issues of
the conventional network management approaches, why DGMs can address them
efficiently, and the step-by-step workflow for applying DGMs in managing
wireless networks. Moreover, we conduct a case study on network economics,
using the state-of-the-art DGM model, i.e., diffusion model, to generate
effective contracts for incentivizing the mobile AI-Generated Content (AIGC)
services. Last but not least, we discuss important open directions for the
further research. | [
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] |
The science of social bots seeks knowledge and solutions to one of the most
debated forms of online misinformation. Yet, social bots research is plagued by
widespread biases, hyped results, and misconceptions that set the stage for
ambiguities, unrealistic expectations, and seemingly irreconcilable findings.
Overcoming such issues is instrumental towards ensuring reliable solutions and
reaffirming the validity of the scientific method. In this contribution we
revise some recent results in social bots research, highlighting and correcting
factual errors as well as methodological and conceptual issues. More
importantly, we demystify common misconceptions, addressing fundamental points
on how social bots research is discussed. Our analysis surfaces the need to
discuss misinformation research in a rigorous, unbiased, and responsible way.
This article bolsters such effort by identifying and refuting common fallacious
arguments used by both proponents and opponents of social bots research as well
as providing indications on the correct methodologies and sound directions for
future research in the field. | [
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] |
As an emerging concept cognitive learning model, partial order formal
structure analysis (POFSA) has been widely used in the field of knowledge
processing. In this paper, we propose the method named three-way causal
attribute partial order structure (3WCAPOS) to evolve the POFSA from set
coverage to causal coverage in order to increase the interpretability and
classification performance of the model. First, the concept of causal factor
(CF) is proposed to evaluate the causal correlation between attributes and
decision attributes in the formal decision context. Then, combining CF with
attribute partial order structure, the concept of causal attribute partial
order structure is defined and makes set coverage evolve into causal coverage.
Finally, combined with the idea of three-way decision, 3WCAPOS is formed, which
makes the purity of nodes in the structure clearer and the changes between
levels more obviously. In addition, the experiments are carried out from the
classification ability and the interpretability of the structure through the
six datasets. Through these experiments, it is concluded the accuracy of
3WCAPOS is improved by 1% - 9% compared with classification and regression
tree, and more interpretable and the processing of knowledge is more reasonable
compared with attribute partial order structure. | [
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] |
Intersectionality is a critical framework that, through inquiry and praxis,
allows us to examine how social inequalities persist through domains of
structure and discipline. Given AI fairness' raison d'etre of "fairness", we
argue that adopting intersectionality as an analytical framework is pivotal to
effectively operationalizing fairness. Through a critical review of how
intersectionality is discussed in 30 papers from the AI fairness literature, we
deductively and inductively: 1) map how intersectionality tenets operate within
the AI fairness paradigm and 2) uncover gaps between the conceptualization and
operationalization of intersectionality. We find that researchers
overwhelmingly reduce intersectionality to optimizing for fairness metrics over
demographic subgroups. They also fail to discuss their social context and when
mentioning power, they mostly situate it only within the AI pipeline. We: 3)
outline and assess the implications of these gaps for critical inquiry and
praxis, and 4) provide actionable recommendations for AI fairness researchers
to engage with intersectionality in their work by grounding it in AI
epistemology. | [
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] |
The debate about the concept of the so called right to explanation in AI is
the subject of a wealth of literature. It has focused, in the legal
scholarship, on art. 22 GDPR and, in the technical scholarship, on techniques
that help explain the output of a certain model (XAI). The purpose of this work
is to investigate if the new provisions introduced by the proposal for a
Regulation laying down harmonised rules on artificial intelligence (AI Act), in
combination with Convention 108 plus and GDPR, are enough to indicate the
existence of a right to technical explainability in the EU legal framework and,
if not, whether the EU should include it in its current legislation. This is a
preliminary work submitted to the online event organised by the Information
Society Law Center and it will be later developed into a full paper. | [
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] |
Metadata quality is crucial for digital objects to be discovered through
digital library interfaces. However, due to various reasons, the metadata of
digital objects often exhibits incomplete, inconsistent, and incorrect values.
We investigate methods to automatically detect, correct, and canonicalize
scholarly metadata, using seven key fields of electronic theses and
dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that
utilizes state-of-the-art artificial intelligence methods to improve the
quality of these fields. To evaluate MetaEnhance, we compiled a metadata
quality evaluation benchmark containing 500 ETDs, by combining subsets sampled
using multiple criteria. We tested MetaEnhance on this benchmark and found that
the proposed methods achieved nearly perfect F1-scores in detecting errors and
F1-scores in correcting errors ranging from 0.85 to 1.00 for five of seven
fields. | [
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] |
In smart electrical grids, fault detection tasks may have a high impact on
society due to their economic and critical implications. In the recent years,
numerous smart grid applications, such as defect detection and load
forecasting, have embraced data-driven methodologies. The purpose of this study
is to investigate the challenges associated with the security of machine
learning (ML) applications in the smart grid scenario. Indeed, the robustness
and security of these data-driven algorithms have not been extensively studied
in relation to all power grid applications. We demonstrate first that the deep
neural network method used in the smart grid is susceptible to adversarial
perturbation. Then, we highlight how studies on fault localization and type
classification illustrate the weaknesses of present ML algorithms in smart
grids to various adversarial attacks | [
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] |
We introduce a machine-learning-based tool for the Lean proof assistant that
suggests relevant premises for theorems being proved by a user. The design
principles for the tool are (1) tight integration with the proof assistant, (2)
ease of use and installation, (3) a lightweight and fast approach. For this
purpose, we designed a custom version of the random forest model, trained in an
online fashion. It is implemented directly in Lean, which was possible thanks
to the rich and efficient metaprogramming features of Lean 4. The random forest
is trained on data extracted from mathlib -- Lean's mathematics library. We
experiment with various options for producing training features and labels. The
advice from a trained model is accessible to the user via the suggest_premises
tactic which can be called in an editor while constructing a proof
interactively. | [
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] |
DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged
robotic system that can dribble a soccer ball under the same real-world
conditions as humans (i.e., in-the-wild). We adopt the paradigm of training
policies in simulation using reinforcement learning and transferring them into
the real world. We overcome critical challenges of accounting for variable ball
motion dynamics on different terrains and perceiving the ball using
body-mounted cameras under the constraints of onboard computing. Our results
provide evidence that current quadruped platforms are well-suited for studying
dynamic whole-body control problems involving simultaneous locomotion and
manipulation directly from sensory observations. | [
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] |
The Frank-Wolfe algorithm is a popular method in structurally constrained
machine learning applications, due to its fast per-iteration complexity.
However, one major limitation of the method is a slow rate of convergence that
is difficult to accelerate due to erratic, zig-zagging step directions, even
asymptotically close to the solution. We view this as an artifact of
discretization; that is to say, the Frank-Wolfe \emph{flow}, which is its
trajectory at asymptotically small step sizes, does not zig-zag, and reducing
discretization error will go hand-in-hand in producing a more stabilized
method, with better convergence properties. We propose two improvements: a
multistep Frank-Wolfe method that directly applies optimized higher-order
discretization schemes; and an LMO-averaging scheme with reduced discretization
error, and whose local convergence rate over general convex sets accelerates
from a rate of $O(1/k)$ to up to $O(1/k^{3/2})$. | [
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] |
Bandit learning has been an increasingly popular design choice for
recommender system. Despite the strong interest in bandit learning from the
community, there remains multiple bottlenecks that prevent many bandit learning
approaches from productionalization. One major bottleneck is how to test the
effectiveness of bandit algorithm with fairness and without data leakage.
Different from supervised learning algorithms, bandit learning algorithms
emphasize greatly on the data collection process through their explorative
nature. Such explorative behavior may induce unfair evaluation in a classic A/B
test setting. In this work, we apply upper confidence bound (UCB) to our large
scale short video recommender system and present a test framework for the
production bandit learning life-cycle with a new set of metrics. Extensive
experiment results show that our experiment design is able to fairly evaluate
the performance of bandit learning in the recommender system. | [
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APT detection is difficult to detect due to the long-term latency, covert and
slow multistage attack patterns of Advanced Persistent Threat (APT). To tackle
these issues, we propose TBDetector, a transformer-based advanced persistent
threat detection method for APT attack detection. Considering that provenance
graphs provide rich historical information and have the powerful attacks
historic correlation ability to identify anomalous activities, TBDetector
employs provenance analysis for APT detection, which summarizes long-running
system execution with space efficiency and utilizes transformer with
self-attention based encoder-decoder to extract long-term contextual features
of system states to detect slow-acting attacks. Furthermore, we further
introduce anomaly scores to investigate the anomaly of different system states,
where each state is calculated with an anomaly score corresponding to its
similarity score and isolation score. To evaluate the effectiveness of the
proposed method, we have conducted experiments on five public datasets, i.e.,
streamspot, cadets, shellshock, clearscope, and wget_baseline. Experimental
results and comparisons with state-of-the-art methods have exhibited better
performance of our proposed method. | [
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] |
We focus on the task of language-conditioned object placement, in which a
robot should generate placements that satisfy all the spatial relational
constraints in language instructions. Previous works based on rule-based
language parsing or scene-centric visual representation have restrictions on
the form of instructions and reference objects or require large amounts of
training data. We propose an object-centric framework that leverages foundation
models to ground the reference objects and spatial relations for placement,
which is more sample efficient and generalizable. Experiments indicate that our
model can achieve a 97.75% success rate of placement with only ~0.26M trainable
parameters. Besides, our method generalizes better to both unseen objects and
instructions. Moreover, with only 25% training data, we still outperform the
top competing approach. | [
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] |
Synthetic data are becoming a critical tool for building artificially
intelligent systems. Simulators provide a way of generating data systematically
and at scale. These data can then be used either exclusively, or in conjunction
with real data, for training and testing systems. Synthetic data are
particularly attractive in cases where the availability of ``real'' training
examples might be a bottleneck. While the volume of data in healthcare is
growing exponentially, creating datasets for novel tasks and/or that reflect a
diverse set of conditions and causal relationships is not trivial. Furthermore,
these data are highly sensitive and often patient specific. Recent research has
begun to illustrate the potential for synthetic data in many areas of medicine,
but no systematic review of the literature exists. In this paper, we present
the cases for physical and statistical simulations for creating data and the
proposed applications in healthcare and medicine. We discuss that while
synthetics can promote privacy, equity, safety and continual and causal
learning, they also run the risk of introducing flaws, blind spots and
propagating or exaggerating biases. | [
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] |
Given a formal context, an ordinal factor is a subset of its incidence
relation that forms a chain in the concept lattice, i.e., a part of the dataset
that corresponds to a linear order. To visualize the data in a formal context,
Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the
biplot to be useful, it is important that these factors comprise as much data
points as possible, i.e., that they cover a large part of the incidence
relation. In this work, we investigate such ordinal two-factorizations. First,
we investigate for formal contexts that omit ordinal two-factorizations the
disjointness of the two factors. Then, we show that deciding on the existence
of two-factorizations of a given size is an NP-complete problem which makes
computing maximal factorizations computationally expensive. Finally, we provide
the algorithm Ord2Factor that allows us to compute large ordinal
two-factorizations. | [
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] |
The popularity of encryption mechanisms poses a great challenge to malicious
traffic detection. The reason is traditional detection techniques cannot work
without the decryption of encrypted traffic. Currently, research on encrypted
malicious traffic detection without decryption has focused on feature
extraction and the choice of machine learning or deep learning algorithms. In
this paper, we first provide an in-depth analysis of traffic features and
compare different state-of-the-art traffic feature creation approaches, while
proposing a novel concept for encrypted traffic feature which is specifically
designed for encrypted malicious traffic analysis. In addition, we propose a
framework for encrypted malicious traffic detection. The framework is a
two-layer detection framework which consists of both deep learning and
traditional machine learning algorithms. Through comparative experiments, it
outperforms classical deep learning and traditional machine learning
algorithms, such as ResNet and Random Forest. Moreover, to provide sufficient
training data for the deep learning model, we also curate a dataset composed
entirely of public datasets. The composed dataset is more comprehensive than
using any public dataset alone. Lastly, we discuss the future directions of
this research. | [
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Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often
solve large real-world problems within minutes. This success can partially be
attributed to heuristics. Since their behavior is highly instance-dependent,
relying on hard-coded rules derived from empirical testing on a large
heterogeneous corpora of benchmark instances might lead to sub-optimal
performance. In this work, we propose an online learning approach that adapts
the application of heuristics towards the single instance at hand. We replace
the commonly used static heuristic handling with an adaptive framework
exploiting past observations about the heuristic's behavior to make future
decisions. In particular, we model the problem of controlling Large
Neighborhood Search and Diving - two broad and complex classes of heuristics -
as a multi-armed bandit problem. Going beyond existing work in the literature,
we control two different classes of heuristics simultaneously by a single
learning agent. We verify our approach numerically and show consistent node
reductions over the MIPLIB 2017 Benchmark set. For harder instances that take
at least 1000 seconds to solve, we observe a speedup of 4%. | [
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] |
In this work we theoretically show that conservative objective models (COMs)
for offline model-based optimisation (MBO) are a special kind of contrastive
divergence-based energy model, one where the energy function represents both
the unconditional probability of the input and the conditional probability of
the reward variable. While the initial formulation only samples modes from its
learned distribution, we propose a simple fix that replaces its gradient ascent
sampler with a Langevin MCMC sampler. This gives rise to a special
probabilistic model where the probability of sampling an input is proportional
to its predicted reward. Lastly, we show that better samples can be obtained if
the model is decoupled so that the unconditional and conditional probabilities
are modelled separately. | [
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] |
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics. | [
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] |
Submodular function optimization has numerous applications in machine
learning and data analysis, including data summarization which aims to identify
a concise and diverse set of data points from a large dataset. It is important
to implement fairness-aware algorithms when dealing with data items that may
contain sensitive attributes like race or gender, to prevent biases that could
lead to unequal representation of different groups. With this in mind, we
investigate the problem of maximizing a monotone submodular function while
meeting group fairness constraints. Unlike previous studies in this area, we
allow for randomized solutions, with the objective being to calculate a
distribution over feasible sets such that the expected number of items selected
from each group is subject to constraints in the form of upper and lower
thresholds, ensuring that the representation of each group remains balanced in
the long term. Here a set is considered feasible if its size does not exceed a
constant value of $b$. Our research includes the development of a series of
approximation algorithms for this problem. | [
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] |
We present Scallop, a language which combines the benefits of deep learning
and logical reasoning. Scallop enables users to write a wide range of
neurosymbolic applications and train them in a data- and compute-efficient
manner. It achieves these goals through three key features: 1) a flexible
symbolic representation that is based on the relational data model; 2) a
declarative logic programming language that is based on Datalog and supports
recursion, aggregation, and negation; and 3) a framework for automatic and
efficient differentiable reasoning that is based on the theory of provenance
semirings. We evaluate Scallop on a suite of eight neurosymbolic applications
from the literature. Our evaluation demonstrates that Scallop is capable of
expressing algorithmic reasoning in diverse and challenging AI tasks, provides
a succinct interface for machine learning programmers to integrate logical
domain knowledge, and yields solutions that are comparable or superior to
state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions
outperform these models in aspects such as runtime and data efficiency,
interpretability, and generalizability. | [
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] |
Given their flexibility and encouraging performance, deep-learning models are
becoming standard for motion prediction in autonomous driving. However, with
great flexibility comes a lack of interpretability and possible violations of
physical constraints. Accompanying these data-driven methods with
differentially-constrained motion models to provide physically feasible
trajectories is a promising future direction. The foundation for this work is a
previously introduced graph-neural-network-based model, MTP-GO. The neural
network learns to compute the inputs to an underlying motion model to provide
physically feasible trajectories. This research investigates the performance of
various motion models in combination with numerical solvers for the prediction
task. The study shows that simpler models, such as low-order integrator models,
are preferred over more complex, e.g., kinematic models, to achieve accurate
predictions. Further, the numerical solver can have a substantial impact on
performance, advising against commonly used first-order methods like Euler
forward. Instead, a second-order method like Heun's can greatly improve
predictions. | [
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] |
Architected materials possessing physico-chemical properties adaptable to
disparate environmental conditions embody a disruptive new domain of materials
science. Fueled by advances in digital design and fabrication, materials shaped
into lattice topologies enable a degree of property customization not afforded
to bulk materials. A promising venue for inspiration toward their design is in
the irregular micro-architectures of nature. However, the immense design
variability unlocked by such irregularity is challenging to probe analytically.
Here, we propose a new computational approach using graph-based representation
for regular and irregular lattice materials. Our method uses differentiable
message passing algorithms to calculate mechanical properties, therefore
allowing automatic differentiation with surrogate derivatives to adjust both
geometric structure and local attributes of individual lattice elements to
achieve inversely designed materials with desired properties. We further
introduce a graph neural network surrogate model for structural analysis at
scale. The methodology is generalizable to any system representable as
heterogeneous graphs. | [
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] |
Reinforcement learning (RL) is an area of significant research interest, and
safe RL in particular is attracting attention due to its ability to handle
safety-driven constraints that are crucial for real-world applications of RL
algorithms. This work proposes a novel approach to RL training, called control
invariant set (CIS) enhanced RL, which leverages the benefits of CIS to improve
stability guarantees and sampling efficiency. The approach consists of two
learning stages: offline and online. In the offline stage, CIS is incorporated
into the reward design, initial state sampling, and state reset procedures. In
the online stage, RL is retrained whenever the state is outside of CIS, which
serves as a stability criterion. A backup table that utilizes the explicit form
of CIS is obtained to ensure the online stability. To evaluate the proposed
approach, we apply it to a simulated chemical reactor. The results show a
significant improvement in sampling efficiency during offline training and
closed-loop stability in the online implementation. | [
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] |
In our study, we apply different statistical models to nowcast quarterly GDP
growth for the US economy. Using the monthly FRED-MD database, we compare the
nowcasting performance of the dynamic factor model (DFM) and four artificial
neural networks (ANNs): the multilayer perceptron (MLP), the one-dimensional
convolutional neural network (1D CNN), the long short-term memory network
(LSTM), and the gated recurrent unit (GRU). The empirical analysis presents the
results from two distinctively different evaluation periods. The first (2010:Q1
-- 2019:Q4) is characterized by balanced economic growth, while the second
(2010:Q1 -- 2022:Q3) also includes periods of the COVID-19 recession. According
to our results, longer input sequences result in more accurate nowcasts in
periods of balanced economic growth. However, this effect ceases above a
relatively low threshold value of around six quarters (eighteen months). During
periods of economic turbulence (e.g., during the COVID-19 recession), longer
training sequences do not help the models' predictive performance; instead,
they seem to weaken their generalization capability. Our results show that 1D
CNN, with the same parameters, generates accurate nowcasts in both of our
evaluation periods. Consequently, first in the literature, we propose the use
of this specific neural network architecture for economic nowcasting. | [
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Next generation cars embed intelligent assessment of car driving safety
through innovative solutions often based on usage of artificial intelligence.
The safety driving monitoring can be carried out using several methodologies
widely treated in scientific literature. In this context, the author proposes
an innovative approach that uses ad-hoc bio-sensing system suitable to
reconstruct the physio-based attentional status of the car driver. To
reconstruct the car driver physiological status, the author proposed the use of
a bio-sensing probe consisting of a coupled LEDs at Near infrared (NiR)
spectrum with a photodetector. This probe placed over the monitored subject
allows to detect a physiological signal called PhotoPlethysmoGraphy (PPG). The
PPG signal formation is regulated by the change in oxygenated and
non-oxygenated hemoglobin concentration in the monitored subject bloodstream
which will be directly connected to cardiac activity in turn regulated by the
Autonomic Nervous System (ANS) that characterizes the subject's attention
level. This so designed car driver drowsiness monitoring will be combined with
further driving safety assessment based on correlated intelligent driving
scenario understanding. | [
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] |
Machine learning-based wearable human activity recognition (WHAR) models
enable the development of various smart and connected community applications
such as sleep pattern monitoring, medication reminders, cognitive health
assessment, sports analytics, etc. However, the widespread adoption of these
WHAR models is impeded by their degraded performance in the presence of data
distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and
environmental diversities. Various traditional machine learning algorithms and
transfer learning techniques have been proposed in the literature to address
the underpinning challenges of handling such data heterogeneities. Domain
adaptation is one such transfer learning techniques that has gained significant
popularity in recent literature. In this paper, we survey the recent progress
of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based
human activity recognition area, discuss potential future directions. | [
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] |
This chapter presents some of the fundamental assumptions and principles that
could form the philosophical foundation of GeoAI and spatial data science.
Instead of reviewing the well-established characteristics of spatial data
(analysis), including interaction, neighborhoods, and autocorrelation, the
chapter highlights themes such as sustainability, bias in training data,
diversity in schema knowledge, and the (potential lack of) neutrality of GeoAI
systems from a unifying ethical perspective. Reflecting on our profession's
ethical implications will assist us in conducting potentially disruptive
research more responsibly, identifying pitfalls in designing, training, and
deploying GeoAI-based systems, and developing a shared understanding of the
benefits but also potential dangers of artificial intelligence and machine
learning research across academic fields, all while sharing our unique
(geo)spatial perspective with others. | [
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] |
We propose a novel, physically-constrained and differentiable approach for
the generation of D-dimensional qudit states via spontaneous parametric
down-conversion (SPDC) in quantum optics. We circumvent any limitations imposed
by the inherently stochastic nature of the physical process and incorporate a
set of stochastic dynamical equations governing its evolution under the SPDC
Hamiltonian. We demonstrate the effectiveness of our model through the design
of structured nonlinear photonic crystals (NLPCs) and shaped pump beams; and
show, theoretically and experimentally, how to generate maximally entangled
states in the spatial degree of freedom. The learning of NLPC structures offers
a promising new avenue for shaping and controlling arbitrary quantum states and
enables all-optical coherent control of the generated states. We believe that
this approach can readily be extended from bulky crystals to thin Metasurfaces
and potentially applied to other quantum systems sharing a similar Hamiltonian
structures, such as superfluids and superconductors. | [
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] |
With increased reliance on Internet based technologies, cyberattacks
compromising users' sensitive data are becoming more prevalent. The scale and
frequency of these attacks are escalating rapidly, affecting systems and
devices connected to the Internet. The traditional defense mechanisms may not
be sufficiently equipped to handle the complex and ever-changing new threats.
The significant breakthroughs in the machine learning methods including deep
learning, had attracted interests from the cybersecurity research community for
further enhancements in the existing anomaly detection methods. Unfortunately,
collecting labelled anomaly data for all new evolving and sophisticated attacks
is not practical. Training and tuning the machine learning model for anomaly
detection using only a handful of labelled data samples is a pragmatic
approach. Therefore, few-shot weakly supervised anomaly detection is an
encouraging research direction. In this paper, we propose an enhancement to an
existing few-shot weakly-supervised deep learning anomaly detection framework.
This framework incorporates data augmentation, representation learning and
ordinal regression. We then evaluated and showed the performance of our
implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and
TON_IoT. | [
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] |
Online Social Networks have revolutionized how we consume and share
information, but they have also led to a proliferation of content not always
reliable and accurate. One particular type of social accounts is known to
promote unreputable content, hyperpartisan, and propagandistic information.
They are automated accounts, commonly called bots. Focusing on Twitter
accounts, we propose a novel approach to bot detection: we first propose a new
algorithm that transforms the sequence of actions that an account performs into
an image; then, we leverage the strength of Convolutional Neural Networks to
proceed with image classification. We compare our performances with
state-of-the-art results for bot detection on genuine accounts / bot accounts
datasets well known in the literature. The results confirm the effectiveness of
the proposal, because the detection capability is on par with the state of the
art, if not better in some cases. | [
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] |
In this paper, we present microring resonator (MRR) based polymorphic E-O
circuits and architectures that can be employed for high-speed and
energy-efficient non-binary reconfigurable computing. Our polymorphic E-O
circuits can be dynamically programmed to implement different logic and
arithmetic functions at different times. They can provide compactness and
polymorphism to consequently improve operand handling, reduce idle time, and
increase amortization of area and static power overheads. When combined with
flexible photodetectors with the innate ability to accumulate a high number of
optical pulses in situ, our circuits can support energy-efficient processing of
data in non-binary formats such as stochastic/unary and high-dimensional
reservoir formats. Furthermore, our polymorphic E-O circuits enable
configurable E-O computing accelerator architectures for processing binarized
and integer quantized convolutional neural networks (CNNs). We compare our
designed polymorphic E-O circuits and architectures to several circuits and
architectures from prior works in terms of area, latency, and energy
consumption. | [
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] |
Recent breakthroughs in generative artificial intelligence have triggered a
surge in demand for machine learning training, which poses significant cost
burdens and environmental challenges due to its substantial energy consumption.
Scheduling training jobs among geographically distributed cloud data centers
unveils the opportunity to optimize the usage of computing capacity powered by
inexpensive and low-carbon energy and address the issue of workload imbalance.
To tackle the challenge of multi-objective scheduling, i.e., maximizing GPU
utilization while reducing operational costs, we propose an algorithm based on
multi-agent reinforcement learning and actor-critic methods to learn the
optimal collaborative scheduling strategy through interacting with a cloud
system built with real-life workload patterns, energy prices, and carbon
intensities. Compared with other algorithms, our proposed method improves the
system utility by up to 28.6% attributable to higher GPU utilization, lower
energy cost, and less carbon emission. | [
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] |
Jumping is essential for legged robots to traverse through difficult
terrains. In this work, we propose a hierarchical framework that combines
optimal control and reinforcement learning to learn continuous jumping motions
for quadrupedal robots. The core of our framework is a stance controller, which
combines a manually designed acceleration controller with a learned residual
policy. As the acceleration controller warm starts policy for efficient
training, the trained policy overcomes the limitation of the acceleration
controller and improves the jumping stability. In addition, a low-level
whole-body controller converts the body pose command from the stance controller
to motor commands. After training in simulation, our framework can be deployed
directly to the real robot, and perform versatile, continuous jumping motions,
including omni-directional jumps at up to 50cm high, 60cm forward, and
jump-turning at up to 90 degrees. Please visit our website for more results:
https://sites.google.com/view/learning-to-jump. | [
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] |
In this paper, we study multiple problems from sponsored product optimization
in ad system, including position-based de-biasing, click-conversion multi-task
learning, and calibration on predicted click-through-rate (pCTR). We propose a
practical machine learning framework that provides the solutions to such
problems without structural change to existing machine learning models, thus
can be combined with most machine learning models including shallow models
(e.g. gradient boosting decision trees, support vector machines). In this
paper, we first propose data and feature engineering techniques to handle the
aforementioned problems in ad system; after that, we evaluate the benefit of
our practical framework on real-world data sets from our traffic logs from
online shopping site. We show that our proposed practical framework with data
and feature engineering can also handle the perennial problems in ad systems
and bring increments to multiple evaluation metrics. | [
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] |
Quantum computing holds great potential for advancing the limitations of
machine learning algorithms to handle higher data dimensions and reduce overall
training parameters in deep neural network (DNN) models. This study uses a
parameterized quantum circuit (PQC) on a gate-based quantum computer to
investigate the potential for quantum advantage in a model-free reinforcement
learning problem. Through a comprehensive investigation and evaluation of the
current model and capabilities of quantum computers, we designed and trained a
novel hybrid Quantum neural network based on the latest Qiskit and PyTorch
framework. We compared its performance with a full-classical DNN with and
without an integrated PQC. Our research provides insights into the potential of
deep quantum learning to solve a maze problem and, potentially, other
reinforcement learning problems. We conclude that various reinforcement
learning problems can be effective with reasonable training epochs. Moreover, a
comparative discussion of the various quantum reinforcement learning model on
maze problems is discussed to evaluate our research's overall potential and
advantages. | [
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] |
An ever-growing number of vulnerabilities are reported every day. Yet these
vulnerabilities are not all the same; Some are more targeted than others.
Correctly estimating the likelihood of a vulnerability being exploited is a
critical task for system administrators. This aids the system administrators in
prioritizing and patching the right vulnerabilities. Our work makes use of
outlier detection techniques to predict vulnerabilities that are likely to be
exploited in highly imbalanced and high-dimensional datasets such as the
National Vulnerability Database. We propose a dimensionality reduction
technique, OutCenTR, that enhances the baseline outlier detection models. We
further demonstrate the effectiveness and efficiency of OutCenTR empirically
with 4 benchmark and 12 synthetic datasets. The results of our experiments show
on average a 5-fold improvement of F1 score in comparison with state-of-the-art
dimensionality reduction techniques such as PCA and GRP. | [
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] |
Evaluating the relevance of an exogenous data series is the first step in
improving the prediction capabilities of a forecast algorithm. Inspired by
existing metrics for time series similarity, we introduce a new approach named
FARM - Forward Aligned Relevance Metric. Our forward method relies on an
angular measure that compares changes in subsequent data points to align
time-warped series in an efficient way. The proposed algorithm combines local
and global measures to provide a balanced relevance metric. This results in
considering also partial, intermediate matches as relevant indicators for
exogenous data series significance. As a first validation step, we present the
application of our FARM approach to synthetic but representative signals. While
demonstrating the improved capabilities with respect to existing approaches, we
also discuss existing constraints and limitations of our idea. | [
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] |
Hardening cyber physical assets is both crucial and labor-intensive.
Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more
specifically has shown great promise to automate tasks that otherwise would
require significant human insight/intelligence. The development of autonomous
RL agents requires a suitable training environment that allows us to quickly
evaluate various alternatives, in particular how to arrange training scenarios
that pit attackers and defenders against each other. CyberBattleSim is a
training environment that supports the training of red agents, i.e., attackers.
We added the capability to train blue agents, i.e., defenders. The paper
describes our changes and reports on the results we obtained when training blue
agents, either in isolation or jointly with red agents. Our results show that
training a blue agent does lead to stronger defenses against attacks. In
particular, training a blue agent jointly with a red agent increases the blue
agent's capability to thwart sophisticated red agents. | [
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] |
Federated Learning with Model Distillation (FedMD) is a nascent collaborative
learning paradigm, where only output logits of public datasets are transmitted
as distilled knowledge, instead of passing on private model parameters that are
susceptible to gradient inversion attacks, a known privacy risk in federated
learning. In this paper, we found that even though sharing output logits of
public datasets is safer than directly sharing gradients, there still exists a
substantial risk of data exposure caused by carefully designed malicious
attacks. Our study shows that a malicious server can inject a PLI
(Paired-Logits Inversion) attack against FedMD and its variants by training an
inversion neural network that exploits the confidence gap between the server
and client models. Experiments on multiple facial recognition datasets validate
that under FedMD-like schemes, by using paired server-client logits of public
datasets only, the malicious server is able to reconstruct private images on
all tested benchmarks with a high success rate. | [
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