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The huge supporting training data on the Internet has been a key factor in
the success of deep learning models. However, this abundance of
public-available data also raises concerns about the unauthorized exploitation
of datasets for commercial purposes, which is forbidden by dataset licenses. In
this paper, we propose a backdoor-based watermarking approach that serves as a
general framework for safeguarding public-available data. By inserting a small
number of watermarking samples into the dataset, our approach enables the
learning model to implicitly learn a secret function set by defenders. This
hidden function can then be used as a watermark to track down third-party
models that use the dataset illegally. Unfortunately, existing backdoor
insertion methods often entail adding arbitrary and mislabeled data to the
training set, leading to a significant drop in performance and easy detection
by anomaly detection algorithms. To overcome this challenge, we introduce a
clean-label backdoor watermarking framework that uses imperceptible
perturbations to replace mislabeled samples. As a result, the watermarking
samples remain consistent with the original labels, making them difficult to
detect. Our experiments on text, image, and audio datasets demonstrate that the
proposed framework effectively safeguards datasets with minimal impact on
original task performance. We also show that adding just 1% of watermarking
samples can inject a traceable watermarking function and that our watermarking
samples are stealthy and look benign upon visual inspection. | [
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] |
Artificial intelligence (AI) and human-machine interaction (HMI) are two
keywords that usually do not fit embedded applications. Within the steps needed
before applying AI to solve a specific task, HMI is usually missing during the
AI architecture design and the training of an AI model. The human-in-the-loop
concept is prevalent in all other steps of developing AI, from data analysis
via data selection and cleaning to performance evaluation. During AI
architecture design, HMI can immediately highlight unproductive layers of the
architecture so that lightweight network architecture for embedded applications
can be created easily. We show that by using this HMI, users can instantly
distinguish which AI architecture should be trained and evaluated first since a
high accuracy on the task could be expected. This approach reduces the
resources needed for AI development by avoiding training and evaluating AI
architectures with unproductive layers and leads to lightweight AI
architectures. These resulting lightweight AI architectures will enable HMI
while running the AI on an edge device. By enabling HMI during an AI uses
inference, we will introduce the AI-in-the-loop concept that combines AI's and
humans' strengths. In our AI-in-the-loop approach, the AI remains the working
horse and primarily solves the task. If the AI is unsure whether its inference
solves the task correctly, it asks the user to use an appropriate HMI.
Consequently, AI will become available in many applications soon since HMI will
make AI more reliable and explainable. | [
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] |
Pretrained protein structure models without labels are crucial foundations
for the majority of protein downstream applications. The conventional structure
pretraining methods follow the mature natural language pretraining methods such
as denoised reconstruction and masked language modeling but usually destroy the
real representation of spatial structures. The other common pretraining methods
might predict a fixed set of predetermined object categories, where a
restricted supervised manner limits their generality and usability as
additional labeled data is required to specify any other protein concepts. In
this work, we introduce a novel unsupervised protein structure representation
pretraining with a robust protein language model. In particular, we first
propose to leverage an existing pretrained language model to guide structure
model learning through an unsupervised contrastive alignment. In addition, a
self-supervised structure constraint is proposed to further learn the intrinsic
information about the structures. With only light training data, the pretrained
structure model can obtain better generalization ability. To quantitatively
evaluate the proposed structure models, we design a series of rational
evaluation methods, including internal tasks (e.g., contact map prediction,
distribution alignment quality) and external/downstream tasks (e.g., protein
design). The extensive experimental results conducted on multiple tasks and
specific datasets demonstrate the superiority of the proposed
sequence-structure transformation framework. | [
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] |
Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven
IoT authentication technique that leverages the unique hardware-level
manufacturing imperfections associated with a particular device to recognize
(fingerprint) the device based on variations introduced in the transmitted
waveform. The proposed SignCRF is a scalable, channel-agnostic, data-driven
radio authentication platform with unmatched precision in fingerprinting
wireless devices based on their unique manufacturing impairments and
independent of the dynamic channel irregularities caused by mobility. SignCRF
consists of (i) a baseline classifier finely trained to authenticate devices
with high accuracy and at scale; (ii) an environment translator carefully
designed and trained to remove the dynamic channel impact from RF signals while
maintaining the radio's specific signature; (iii) a Max-Rule module that
selects the highest precision authentication technique between the baseline
classifier and the environment translator per radio. We design, train, and
validate the performance of SignCRF for multiple technologies in dynamic
environments and at scale (100 LoRa and 20 WiFi devices). We demonstrate that
SignCRF significantly improves the RFFDL performance by achieving as high as 5x
and 8x improvement in correct authentication of WiFi and LoRa devices when
compared to the state-of-the-art, respectively. | [
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] |
Quantum process learning is emerging as an important tool to study quantum
systems. While studied extensively in coherent frameworks, where the target and
model system can share quantum information, less attention has been paid to
whether the dynamics of quantum systems can be learned without the system and
target directly interacting. Such incoherent frameworks are practically
appealing since they open up methods of transpiling quantum processes between
the different physical platforms without the need for technically challenging
hybrid entanglement schemes. Here we provide bounds on the sample complexity of
learning unitary processes incoherently by analyzing the number of measurements
that are required to emulate well-established coherent learning strategies. We
prove that if arbitrary measurements are allowed, then any efficiently
representable unitary can be efficiently learned within the incoherent
framework; however, when restricted to shallow-depth measurements only
low-entangling unitaries can be learned. We demonstrate our incoherent learning
algorithm for low entangling unitaries by successfully learning a 16-qubit
unitary on \texttt{ibmq\_kolkata}, and further demonstrate the scalabilty of
our proposed algorithm through extensive numerical experiments. | [
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] |
Placing a human in the loop may abate the risks of deploying AI systems in
safety-critical settings (e.g., a clinician working with a medical AI system).
However, mitigating risks arising from human error and uncertainty within such
human-AI interactions is an important and understudied issue. In this work, we
study human uncertainty in the context of concept-based models, a family of AI
systems that enable human feedback via concept interventions where an expert
intervenes on human-interpretable concepts relevant to the task. Prior work in
this space often assumes that humans are oracles who are always certain and
correct. Yet, real-world decision-making by humans is prone to occasional
mistakes and uncertainty. We study how existing concept-based models deal with
uncertain interventions from humans using two novel datasets: UMNIST, a visual
dataset with controlled simulated uncertainty based on the MNIST dataset, and
CUB-S, a relabeling of the popular CUB concept dataset with rich,
densely-annotated soft labels from humans. We show that training with uncertain
concept labels may help mitigate weaknesses of concept-based systems when
handling uncertain interventions. These results allow us to identify several
open challenges, which we argue can be tackled through future multidisciplinary
research on building interactive uncertainty-aware systems. To facilitate
further research, we release a new elicitation platform, UElic, to collect
uncertain feedback from humans in collaborative prediction tasks. | [
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] |
Predicting future resource demand in Cloud Computing is essential for
optimizing the trade-off between serving customers' requests efficiently and
minimizing the provisioning cost. Modelling prediction uncertainty is also
desirable to better inform the resource decision-making process, but research
in this field is under-investigated. In this paper, we propose univariate and
bivariate Bayesian deep learning models that provide predictions of future
workload demand and its uncertainty. We run extensive experiments on Google and
Alibaba clusters, where we first train our models with datasets from different
cloud providers and compare them with LSTM-based baselines. Results show that
modelling the uncertainty of predictions has a positive impact on performance,
especially on service level metrics, because uncertainty quantification can be
tailored to desired target service levels that are critical in cloud
applications. Moreover, we investigate whether our models benefit transfer
learning capabilities across different domains, i.e. dataset distributions.
Experiments on the same workload datasets reveal that acceptable transfer
learning performance can be achieved within the same provider (because
distributions are more similar). Also, domain knowledge does not transfer when
the source and target domains are very different (e.g. from different
providers), but this performance degradation can be mitigated by increasing the
training set size of the source domain. | [
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] |
The prospect of informed and optimal decision-making regarding the operation
and maintenance (O&M) of structures provides impetus to the development of
structural health monitoring (SHM) systems. A probabilistic risk-based
framework for decision-making has already been proposed. However, in order to
learn the statistical models necessary for decision-making, measured data from
the structure of interest are required. Unfortunately, these data are seldom
available across the range of environmental and operational conditions
necessary to ensure good generalisation of the model.
Recently, technologies have been developed that overcome this challenge, by
extending SHM to populations of structures, such that valuable knowledge may be
transferred between instances of structures that are sufficiently similar. This
new approach is termed population-based structural heath monitoring (PBSHM).
The current paper presents a formal representation of populations of
structures, such that risk-based decision processes may be specified within
them. The population-based representation is an extension to the hierarchical
representation of a structure used within the probabilistic risk-based decision
framework to define fault trees. The result is a series, consisting of systems
of systems ranging from the individual component level up to an inventory of
heterogeneous populations. The current paper considers an inventory of wind
farms as a motivating example and highlights the inferences and decisions that
can be made within the hierarchical representation. | [
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] |
In this paper, we present a demo of an intelligent personal agent called Hey
Dona (or just Dona) with virtual voice assistance in student course
registration. It is a deployed project in the theme of AI for education. In
this digital age with a myriad of smart devices, users often delegate tasks to
agents. While pointing and clicking supersedes the erstwhile command-typing,
modern devices allow users to speak commands for agents to execute tasks,
enhancing speed and convenience. In line with this progress, Dona is an
intelligent agent catering to student needs by automated, voice-operated course
registration, spanning a multitude of accents, entailing task planning
optimization, with some language translation as needed. Dona accepts voice
input by microphone (Bluetooth, wired microphone), converts human voice to
computer understandable language, performs query processing as per user
commands, connects with the Web to search for answers, models task
dependencies, imbibes quality control, and conveys output by speaking to users
as well as displaying text, thus enabling human-AI interaction by speech cum
text. It is meant to work seamlessly on desktops, smartphones etc. and in
indoor as well as outdoor settings. To the best of our knowledge, Dona is among
the first of its kind as an intelligent personal agent for voice assistance in
student course registration. Due to its ubiquitous access for educational
needs, Dona directly impacts AI for education. It makes a broader impact on
smart city characteristics of smart living and smart people due to its
contributions to providing benefits for new ways of living and assisting 21st
century education, respectively. | [
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] |
Knowledge Graph Embeddings (KGE) have become a quite popular class of models
specifically devised to deal with ontologies and graph structure data, as they
can implicitly encode statistical dependencies between entities and relations
in a latent space. KGE techniques are particularly effective for the biomedical
domain, where it is quite common to deal with large knowledge graphs underlying
complex interactions between biological and chemical objects. Recently in the
literature, the PharmKG dataset has been proposed as one of the most
challenging knowledge graph biomedical benchmark, with hundreds of thousands of
relational facts between genes, diseases and chemicals. Despite KGEs can scale
to very large relational domains, they generally fail at representing more
complex relational dependencies between facts, like logic rules, which may be
fundamental in complex experimental settings. In this paper, we exploit logic
rules to enhance the embedding representations of KGEs on the PharmKG dataset.
To this end, we adopt Relational Reasoning Network (R2N), a recently proposed
neural-symbolic approach showing promising results on knowledge graph
completion tasks. An R2N uses the available logic rules to build a neural
architecture that reasons over KGE latent representations. In the experiments,
we show that our approach is able to significantly improve the current
state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study
to experimentally compare the effect of alternative sets of rules according to
different selection criteria and varying the number of considered rules. | [
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] |
Traffic simulation plays a crucial role in evaluating and improving
autonomous driving planning systems. After being deployed on public roads,
autonomous vehicles need to interact with human road participants with
different social preferences (e.g., selfish or courteous human drivers). To
ensure that autonomous vehicles take safe and efficient maneuvers in different
interactive traffic scenarios, we should be able to evaluate autonomous
vehicles against reactive agents with different social characteristics in the
simulation environment. We propose a socially-controllable behavior generation
(SCBG) model for this purpose, which allows the users to specify the level of
courtesy of the generated trajectory while ensuring realistic and human-like
trajectory generation through learning from real-world driving data.
Specifically, we define a novel and differentiable measure to quantify the
level of courtesy of driving behavior, leveraging marginal and conditional
behavior prediction models trained from real-world driving data. The proposed
courtesy measure allows us to auto-label the courtesy levels of trajectories
from real-world driving data and conveniently train an SCBG model generating
trajectories based on the input courtesy values. We examined the SCBG model on
the Waymo Open Motion Dataset (WOMD) and showed that we were able to control
the SCBG model to generate realistic driving behaviors with desired courtesy
levels. Interestingly, we found that the SCBG model was able to identify
different motion patterns of courteous behaviors according to the scenarios. | [
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] |
Complex logical query answering (CLQA) is a recently emerged task of graph
machine learning that goes beyond simple one-hop link prediction and solves a
far more complex task of multi-hop logical reasoning over massive, potentially
incomplete graphs in a latent space. The task received a significant traction
in the community; numerous works expanded the field along theoretical and
practical axes to tackle different types of complex queries and graph
modalities with efficient systems. In this paper, we provide a holistic survey
of CLQA with a detailed taxonomy studying the field from multiple angles,
including graph types (modality, reasoning domain, background semantics),
modeling aspects (encoder, processor, decoder), supported queries (operators,
patterns, projected variables), datasets, evaluation metrics, and applications.
Refining the CLQA task, we introduce the concept of Neural Graph Databases
(NGDBs). Extending the idea of graph databases (graph DBs), NGDB consists of a
Neural Graph Storage and a Neural Graph Engine. Inside Neural Graph Storage, we
design a graph store, a feature store, and further embed information in a
latent embedding store using an encoder. Given a query, Neural Query Engine
learns how to perform query planning and execution in order to efficiently
retrieve the correct results by interacting with the Neural Graph Storage.
Compared with traditional graph DBs, NGDBs allow for a flexible and unified
modeling of features in diverse modalities using the embedding store. Moreover,
when the graph is incomplete, they can provide robust retrieval of answers
which a normal graph DB cannot recover. Finally, we point out promising
directions, unsolved problems and applications of NGDB for future research. | [
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] |
Explainable AI (XAI) has established itself as an important component of
AI-driven interactive systems. With Augmented Reality (AR) becoming more
integrated in daily lives, the role of XAI also becomes essential in AR because
end-users will frequently interact with intelligent services. However, it is
unclear how to design effective XAI experiences for AR. We propose XAIR, a
design framework that addresses "when", "what", and "how" to provide
explanations of AI output in AR. The framework was based on a
multi-disciplinary literature review of XAI and HCI research, a large-scale
survey probing 500+ end-users' preferences for AR-based explanations, and three
workshops with 12 experts collecting their insights about XAI design in AR.
XAIR's utility and effectiveness was verified via a study with 10 designers and
another study with 12 end-users. XAIR can provide guidelines for designers,
inspiring them to identify new design opportunities and achieve effective XAI
designs in AR. | [
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] |
While decades of theoretical research have led to the invention of several
classes of error-correction codes, the design of such codes is an extremely
challenging task, mostly driven by human ingenuity. Recent studies demonstrate
that such designs can be effectively automated and accelerated via tools from
machine learning (ML), thus enabling ML-driven classes of error-correction
codes with promising performance gains compared to classical designs. A
fundamental challenge, however, is that it is prohibitively complex, if not
impossible, to design and train fully ML-driven encoder and decoder pairs for
large code dimensions. In this paper, we propose Product Autoencoder
(ProductAE) -- a computationally-efficient family of deep learning driven
(encoder, decoder) pairs -- aimed at enabling the training of relatively large
codes (both encoder and decoder) with a manageable training complexity. We
build upon ideas from classical product codes and propose constructing large
neural codes using smaller code components. ProductAE boils down the complex
problem of training the encoder and decoder for a large code dimension $k$ and
blocklength $n$ to less-complex sub-problems of training encoders and decoders
for smaller dimensions and blocklengths. Our training results show successful
training of ProductAEs of dimensions as large as $k = 300$ bits with meaningful
performance gains compared to state-of-the-art classical and neural designs.
Moreover, we demonstrate excellent robustness and adaptivity of ProductAEs to
channel models different than the ones used for training. | [
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Communication load balancing aims to balance the load between different
available resources, and thus improve the quality of service for network
systems. After formulating the load balancing (LB) as a Markov decision process
problem, reinforcement learning (RL) has recently proven effective in
addressing the LB problem. To leverage the benefits of classical RL for load
balancing, however, we need an explicit reward definition. Engineering this
reward function is challenging, because it involves the need for expert
knowledge and there lacks a general consensus on the form of an optimal reward
function. In this work, we tackle the communication load balancing problem from
an inverse reinforcement learning (IRL) approach. To the best of our knowledge,
this is the first time IRL has been successfully applied in the field of
communication load balancing. Specifically, first, we infer a reward function
from a set of demonstrations, and then learn a reinforcement learning load
balancing policy with the inferred reward function. Compared to classical
RL-based solution, the proposed solution can be more general and more suitable
for real-world scenarios. Experimental evaluations implemented on different
simulated traffic scenarios have shown our method to be effective and better
than other baselines by a considerable margin. | [
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] |
An abundance of information about cancer exists online, but categorizing and
extracting useful information from it is difficult. Almost all research within
healthcare data processing is concerned with formal clinical data, but there is
valuable information in non-clinical data too. The present study combines
methods within distributed computing, text retrieval, clustering, and
classification into a coherent and computationally efficient system, that can
clarify cancer patient trajectories based on non-clinical and freely available
information. We produce a fully-functional prototype that can retrieve, cluster
and present information about cancer trajectories from non-clinical forum
posts. We evaluate three clustering algorithms (MR-DBSCAN, DBSCAN, and HDBSCAN)
and compare them in terms of Adjusted Rand Index and total run time as a
function of the number of posts retrieved and the neighborhood radius.
Clustering results show that neighborhood radius has the most significant
impact on clustering performance. For small values, the data set is split
accordingly, but high values produce a large number of possible partitions and
searching for the best partition is hereby time-consuming. With a proper
estimated radius, MR-DBSCAN can cluster 50000 forum posts in 46.1 seconds,
compared to DBSCAN (143.4) and HDBSCAN (282.3). We conduct an interview with
the Danish Cancer Society and present our software prototype. The organization
sees a potential in software that can democratize online information about
cancer and foresee that such systems will be required in the future. | [
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] |
Autonomous agents that drive on roads shared with human drivers must reason
about the nuanced interactions among traffic participants. This poses a highly
challenging decision making problem since human behavior is influenced by a
multitude of factors (e.g., human intentions and emotions) that are hard to
model. This paper presents a decision making approach for autonomous driving,
focusing on the complex task of merging into moving traffic where uncertainty
emanates from the behavior of other drivers and imperfect sensor measurements.
We frame the problem as a partially observable Markov decision process (POMDP)
and solve it online with Monte Carlo tree search. The solution to the POMDP is
a policy that performs high-level driving maneuvers, such as giving way to an
approaching car, keeping a safe distance from the vehicle in front or merging
into traffic. Our method leverages a model learned from data to predict the
future states of traffic while explicitly accounting for interactions among the
surrounding agents. From these predictions, the autonomous vehicle can
anticipate the future consequences of its actions on the environment and
optimize its trajectory accordingly. We thoroughly test our approach in
simulation, showing that the autonomous vehicle can adapt its behavior to
different situations. We also compare against other methods, demonstrating an
improvement with respect to the considered performance metrics. | [
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] |
The current state of the art systems in Artificial Intelligence (AI) enabled
intrusion detection use a variety of black box methods. These black box methods
are generally trained using Error Based Learning (EBL) techniques with a focus
on creating accurate models. These models have high performative costs and are
not easily explainable. A white box Competitive Learning (CL) based eXplainable
Intrusion Detection System (X-IDS) offers a potential solution to these
problem. CL models utilize an entirely different learning paradigm than EBL
approaches. This different learning process makes the CL family of algorithms
innately explainable and less resource intensive. In this paper, we create an
X-IDS architecture that is based on DARPA's recommendation for explainable
systems. In our architecture we leverage CL algorithms like, Self Organizing
Maps (SOM), Growing Self Organizing Maps (GSOM), and Growing Hierarchical Self
Organizing Map (GHSOM). The resulting models can be data-mined to create
statistical and visual explanations. Our architecture is tested using NSL-KDD
and CIC-IDS-2017 benchmark datasets, and produces accuracies that are 1% - 3%
less than EBL models. However, CL models are much more explainable than EBL
models. Additionally, we use a pruning process that is able to significantly
reduce the size of these CL based models. By pruning our models, we are able to
increase prediction speeds. Lastly, we analyze the statistical and visual
explanations generated by our architecture, and we give a strategy that users
could use to help navigate the set of explanations. These explanations will
help users build trust with an Intrusion Detection System (IDS), and allow
users to discover ways to increase the IDS's potency. | [
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With the rapid development of mobile networks, the people's social contacts
have been considerably facilitated. However, the rise of mobile social network
fraud upon those networks, has caused a great deal of distress, in case of
depleting personal and social wealth, then potentially doing significant
economic harm. To detect fraudulent users, call detail record (CDR) data, which
portrays the social behavior of users in mobile networks, has been widely
utilized. But the imbalance problem in the aforementioned data, which could
severely hinder the effectiveness of fraud detectors based on graph neural
networks(GNN), has hardly been addressed in previous work. In this paper, we
are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by
creatively combining cost-sensitive learning and graph neural networks. We
conduct extensive experiments on two open-source realworld mobile network fraud
datasets. The results show that CSGNN can effectively solve the graph imbalance
problem and then achieve better detection performance than the state-of-the-art
algorithms. We believe that our research can be applied to solve the graph
imbalance problems in other fields. The CSGNN code and datasets are publicly
available at https://github.com/xxhu94/CSGNN. | [
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] |
The metaverse, which is at the stage of innovation and exploration, faces the
dilemma of data collection and the problem of private data leakage in the
process of development. This can seriously hinder the widespread deployment of
the metaverse. Fortunately, federated learning (FL) is a solution to the above
problems. FL is a distributed machine learning paradigm with privacy-preserving
features designed for a large number of edge devices. Federated learning for
metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to
participate in training tasks locally using their own data, computational
power, and model-building capabilities. Applying FL to the metaverse not only
protects the data privacy of participants but also reduces the need for high
computing power and high memory on servers. Until now, there have been many
studies about FL and the metaverse, respectively. In this paper, we review some
of the early advances of FL4M, which will be a research direction with
unlimited development potential. We first introduce the concepts of metaverse
and FL, respectively. Besides, we discuss the convergence of key metaverse
technologies and FL in detail, such as big data, communication technology, the
Internet of Things, edge computing, blockchain, and extended reality. Finally,
we discuss some key challenges and promising directions of FL4M in detail. In
summary, we hope that our up-to-date brief survey can help people better
understand FL4M and build a fair, open, and secure metaverse. | [
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] |
Optimization problems involving minimization of a rank-one convex function
over constraints modeling restrictions on the support of the decision variables
emerge in various machine learning applications. These problems are often
modeled with indicator variables for identifying the support of the continuous
variables. In this paper we investigate compact extended formulations for such
problems through perspective reformulation techniques. In contrast to the
majority of previous work that relies on support function arguments and
disjunctive programming techniques to provide convex hull results, we propose a
constructive approach that exploits a hidden conic structure induced by
perspective functions. To this end, we first establish a convex hull result for
a general conic mixed-binary set in which each conic constraint involves a
linear function of independent continuous variables and a set of binary
variables. We then demonstrate that extended representations of sets associated
with epigraphs of rank-one convex functions over constraints modeling indicator
relations naturally admit such a conic representation. This enables us to
systematically give perspective formulations for the convex hull descriptions
of these sets with nonlinear separable or non-separable objective functions,
sign constraints on continuous variables, and combinatorial constraints on
indicator variables. We illustrate the efficacy of our results on sparse
nonnegative logistic regression problems. | [
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] |
Deep neural networks are proven to be vulnerable to backdoor attacks.
Detecting the trigger samples during the inference stage, i.e., the test-time
trigger sample detection, can prevent the backdoor from being triggered.
However, existing detection methods often require the defenders to have high
accessibility to victim models, extra clean data, or knowledge about the
appearance of backdoor triggers, limiting their practicality. In this paper, we
propose the test-time corruption robustness consistency evaluation (TeCo), a
novel test-time trigger sample detection method that only needs the hard-label
outputs of the victim models without any extra information. Our journey begins
with the intriguing observation that the backdoor-infected models have similar
performance across different image corruptions for the clean images, but
perform discrepantly for the trigger samples. Based on this phenomenon, we
design TeCo to evaluate test-time robustness consistency by calculating the
deviation of severity that leads to predictions' transition across different
corruptions. Extensive experiments demonstrate that compared with
state-of-the-art defenses, which even require either certain information about
the trigger types or accessibility of clean data, TeCo outperforms them on
different backdoor attacks, datasets, and model architectures, enjoying a
higher AUROC by 10% and 5 times of stability. | [
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Recently, with the rapid deployment of service APIs, personalized service
recommendations have played a paramount role in the growth of the e-commerce
industry. Quality-of-Service (QoS) parameters determining the service
performance, often used for recommendation, fluctuate over time. Thus, the QoS
prediction is essential to identify a suitable service among functionally
equivalent services over time. The contemporary temporal QoS prediction methods
hardly achieved the desired accuracy due to various limitations, such as the
inability to handle data sparsity and outliers and capture higher-order
temporal relationships among user-service interactions. Even though some recent
recurrent neural-network-based architectures can model temporal relationships
among QoS data, prediction accuracy degrades due to the absence of other
features (e.g., collaborative features) to comprehend the relationship among
the user-service interactions. This paper addresses the above challenges and
proposes a scalable strategy for Temporal QoS Prediction using Multi-source
Collaborative-Features (TPMCF), achieving high prediction accuracy and faster
responsiveness. TPMCF combines the collaborative-features of users/services by
exploiting user-service relationship with the spatio-temporal auto-extracted
features by employing graph convolution and transformer encoder with multi-head
self-attention. We validated our proposed method on WS-DREAM-2 datasets.
Extensive experiments showed TPMCF outperformed major state-of-the-art
approaches regarding prediction accuracy while ensuring high scalability and
reasonably faster responsiveness. | [
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] |
The challenges of using inadequate online recruitment systems can be
addressed with machine learning and software engineering techniques.
Bi-directional personalization reinforcement learning-based architecture with
active learning can get recruiters to recommend qualified applicants and also
enable applicants to receive personalized job recommendations. This paper
focuses on how machine learning techniques can enhance the recruitment process
in the travel nursing industry by helping speed up data acquisition using a
multi-model data service and then providing personalized recommendations using
bi-directional reinforcement learning with active learning. This need was
especially evident when trying to respond to the overwhelming needs of
healthcare facilities during the COVID-19 pandemic. The need for traveling
nurses and other healthcare professionals was more evident during the lockdown
period. A data service was architected for job feed processing using an
orchestration of natural language processing (NLP) models that synthesize
job-related data into a database efficiently and accurately. The multi-model
data service provided the data necessary to develop a bi-directional
personalization system using reinforcement learning with active learning that
could recommend travel nurses and healthcare professionals to recruiters and
provide job recommendations to applicants using an internally developed smart
match score as a basis. The bi-directional personalization reinforcement
learning-based architecture with active learning combines two personalization
systems - one that runs forward to recommend qualified candidates for jobs and
another that runs backward and recommends jobs for applicants. | [
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] |
We propose and release a new vulnerable source code dataset. We curate the
dataset by crawling security issue websites, extracting vulnerability-fixing
commits and source codes from the corresponding projects. Our new dataset
contains 18,945 vulnerable functions spanning 150 CWEs and 330,492
non-vulnerable functions extracted from 7,514 commits. Our dataset covers 295
more projects than all previous datasets combined.
Combining our new dataset with previous datasets, we present an analysis of
the challenges and promising research directions of using deep learning for
detecting software vulnerabilities. We study 11 model architectures belonging
to 4 families. Our results show that deep learning is still not ready for
vulnerability detection, due to high false positive rate, low F1 score, and
difficulty of detecting hard CWEs. In particular, we demonstrate an important
generalization challenge for the deployment of deep learning-based models. We
show that increasing the volume of training data may not further improve the
performance of deep learning models for vulnerability detection, but might be
useful to improve the generalization ability to unseen projects.
We also identify hopeful future research directions. We demonstrate that
large language models (LLMs) are a promising research direction for ML-based
vulnerability detection, outperforming Graph Neural Networks (GNNs) with
code-structure features in our experiments. Moreover, developing source code
specific pre-training objectives is a promising research direction to improve
the vulnerability detection performance. | [
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] |
Ontology-based clustering has gained attention in recent years due to the
potential benefits of ontology. Current ontology-based clustering approaches
have mainly been applied to reduce the dimensionality of attributes in text
document clustering. Reduction in dimensionality of attributes using ontology
helps to produce high quality clusters for a dataset. However, ontology-based
approaches in clustering numerical datasets have not been gained enough
attention. Moreover, some literature mentions that ontology-based clustering
can produce either high quality or low-quality clusters from a dataset.
Therefore, in this paper we present a clustering approach that is based on
domain ontology to reduce the dimensionality of attributes in a numerical
dataset using domain ontology and to produce high quality clusters. For every
dataset, we produce three datasets using domain ontology. We then cluster these
datasets using a genetic algorithm-based clustering technique called
GenClust++. The clusters of each dataset are evaluated in terms of Sum of
Squared-Error (SSE). We use six numerical datasets to evaluate the performance
of our ontology-based approach. The experimental results of our approach
indicate that cluster quality gradually improves from lower to the higher
levels of a domain ontology. | [
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] |
Neural networks (NNs) playing the role of controllers have demonstrated
impressive empirical performances on challenging control problems. However, the
potential adoption of NN controllers in real-life applications also gives rise
to a growing concern over the safety of these neural-network controlled systems
(NNCSs), especially when used in safety-critical applications. In this work, we
present POLAR-Express, an efficient and precise formal reachability analysis
tool for verifying the safety of NNCSs. POLAR-Express uses Taylor model
arithmetic to propagate Taylor models (TMs) across a neural network
layer-by-layer to compute an overapproximation of the neural-network function.
It can be applied to analyze any feed-forward neural network with continuous
activation functions. We also present a novel approach to propagate TMs more
efficiently and precisely across ReLU activation functions. In addition,
POLAR-Express provides parallel computation support for the layer-by-layer
propagation of TMs, thus significantly improving the efficiency and scalability
over its earlier prototype POLAR. Across the comparison with six other
state-of-the-art tools on a diverse set of benchmarks, POLAR-Express achieves
the best verification efficiency and tightness in the reachable set analysis. | [
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] |
The task of dimensionality reduction and visualization of high-dimensional
datasets remains a challenging problem since long. Modern high-throughput
technologies produce newer high-dimensional datasets having multiple views with
relatively new data types. Visualization of these datasets require proper
methodology that can uncover hidden patterns in the data without affecting the
local and global structures within the data. To this end, however, very few
such methodology exist, which can realise this task. In this work, we have
introduced a novel unsupervised deep neural network model, called NeuroDAVIS,
for data visualization. NeuroDAVIS is capable of extracting important features
from the data, without assuming any data distribution, and visualize
effectively in lower dimension. It has been shown theoritically that
neighbourhood relationship of the data in high dimension remains preserved in
lower dimension. The performance of NeuroDAVIS has been evaluated on a wide
variety of synthetic and real high-dimensional datasets including numeric,
textual, image and biological data. NeuroDAVIS has been highly competitive
against both t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform
Manifold Approximation and Projection (UMAP) with respect to visualization
quality, and preservation of data size, shape, and both local and global
structure. It has outperformed Fast interpolation-based t-SNE (Fit-SNE), a
variant of t-SNE, for most of the high-dimensional datasets as well. For the
biological datasets, besides t-SNE, UMAP and Fit-SNE, NeuroDAVIS has also
performed well compared to other state-of-the-art algorithms, like Potential of
Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) and the siamese
neural network-based method, called IVIS. Downstream classification and
clustering analyses have also revealed favourable results for
NeuroDAVIS-generated embeddings. | [
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] |
Bayesian inference provides a methodology for parameter estimation and
uncertainty quantification in machine learning and deep learning methods.
Variational inference and Markov Chain Monte-Carlo (MCMC) sampling techniques
are used to implement Bayesian inference. In the past three decades, MCMC
methods have faced a number of challenges in being adapted to larger models
(such as in deep learning) and big data problems. Advanced proposals that
incorporate gradients, such as a Langevin proposal distribution, provide a
means to address some of the limitations of MCMC sampling for Bayesian neural
networks. Furthermore, MCMC methods have typically been constrained to use by
statisticians and are still not prominent among deep learning researchers. We
present a tutorial for MCMC methods that covers simple Bayesian linear and
logistic models, and Bayesian neural networks. The aim of this tutorial is to
bridge the gap between theory and implementation via coding, given a general
sparsity of libraries and tutorials to this end. This tutorial provides code in
Python with data and instructions that enable their use and extension. We
provide results for some benchmark problems showing the strengths and
weaknesses of implementing the respective Bayesian models via MCMC. We
highlight the challenges in sampling multi-modal posterior distributions in
particular for the case of Bayesian neural networks, and the need for further
improvement of convergence diagnosis. | [
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] |
Graph collaborative filtering (GCF) is a popular technique for capturing
high-order collaborative signals in recommendation systems. However, GCF's
bipartite adjacency matrix, which defines the neighbors being aggregated based
on user-item interactions, can be noisy for users/items with abundant
interactions and insufficient for users/items with scarce interactions.
Additionally, the adjacency matrix ignores user-user and item-item
correlations, which can limit the scope of beneficial neighbors being
aggregated.
In this work, we propose a new graph adjacency matrix that incorporates
user-user and item-item correlations, as well as a properly designed user-item
interaction matrix that balances the number of interactions across all users.
To achieve this, we pre-train a graph-based recommendation method to obtain
users/items embeddings, and then enhance the user-item interaction matrix via
top-K sampling. We also augment the symmetric user-user and item-item
correlation components to the adjacency matrix. Our experiments demonstrate
that the enhanced user-item interaction matrix with improved neighbors and
lower density leads to significant benefits in graph-based recommendation.
Moreover, we show that the inclusion of user-user and item-item correlations
can improve recommendations for users with both abundant and insufficient
interactions. The code is in \url{https://github.com/zfan20/GraphDA}. | [
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] |
Orchestrating a high-quality data preparation program is essential for
successful machine learning (ML), but it is known to be time and effort
consuming. Despite the impressive capabilities of large language models like
ChatGPT in generating programs by interacting with users through natural
language prompts, there are still limitations. Specifically, a user must
provide specific prompts to iteratively guide ChatGPT in improving data
preparation programs, which requires a certain level of expertise in
programming, the dataset used and the ML task. Moreover, once a program has
been generated, it is non-trivial to revisit a previous version or make changes
to the program without starting the process over again. In this paper, we
present ChatPipe, a novel system designed to facilitate seamless interaction
between users and ChatGPT. ChatPipe provides users with effective
recommendation on next data preparation operations, and guides ChatGPT to
generate program for the operations. Also, ChatPipe enables users to easily
roll back to previous versions of the program, which facilitates more efficient
experimentation and testing. We have developed a web application for ChatPipe
and prepared several real-world ML tasks from Kaggle. These tasks can showcase
the capabilities of ChatPipe and enable VLDB attendees to easily experiment
with our novel features to rapidly orchestrate a high-quality data preparation
program. | [
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] |
Current state-of-the-art crowd navigation approaches are mainly deep
reinforcement learning (DRL)-based. However, DRL-based methods suffer from the
issues of generalization and scalability. To overcome these challenges, we
propose a method that includes a Collision Probability (CP) in the observation
space to give the robot a sense of the level of danger of the moving crowd to
help the robot navigate safely through crowds with unseen behaviors. We studied
the effects of changing the number of moving obstacles to pay attention during
navigation. During training, we generated local waypoints to increase the
reward density and improve the learning efficiency of the system. Our approach
was developed using deep reinforcement learning (DRL) and trained using the
Gazebo simulator in a non-cooperative crowd environment with obstacles moving
at randomized speeds and directions. We then evaluated our model on four
different crowd-behavior scenarios. The results show that our method achieved a
100% success rate in all test settings. We compared our approach with a current
state-of-the-art DRL-based approach, and our approach has performed
significantly better, especially in terms of social safety. Importantly, our
method can navigate in different crowd behaviors and requires no fine-tuning
after being trained once. We further demonstrated the crowd navigation
capability of our model in real-world tests. | [
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] |
As quantum chemical properties have a dependence on their geometries, graph
neural networks (GNNs) using 3D geometric information have achieved high
prediction accuracy in many tasks. However, they often require 3D geometries
obtained from high-level quantum mechanical calculations, which are practically
infeasible, limiting their applicability to real-world problems. To tackle
this, we propose a new training framework, GeoTMI, that employs denoising
process to predict properties accurately using easy-to-obtain geometries
(corrupted versions of correct geometries, such as those obtained from
low-level calculations). Our starting point was the idea that the correct
geometry is the best description of the target property. Hence, to incorporate
information of the correct, GeoTMI aims to maximize mutual information between
three variables: the correct and the corrupted geometries and the property.
GeoTMI also explicitly updates the corrupted input to approach the correct
geometry as it passes through the GNN layers, contributing to more effective
denoising. We investigated the performance of the proposed method using 3D GNNs
for three prediction tasks: molecular properties, a chemical reaction property,
and relaxed energy in a heterogeneous catalytic system. Our results showed
consistent improvements in accuracy across various tasks, demonstrating the
effectiveness and robustness of GeoTMI. | [
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] |
MANET is a collection of mobile nodes that communicate through wireless
networks as they move from one point to another. MANET is an
infrastructure-less network with a changeable topology; as a result, it is very
susceptible to attacks. MANET attack prevention represents a serious
difficulty. Malicious network nodes are the source of network-based attacks. In
a MANET, attacks can take various forms, and each one alters the network's
operation in its unique way. In general, attacks can be separated into two
categories: those that target the data traffic on a network and those that
target the control traffic. This article explains the many sorts of assaults,
their impact on MANET, and the MANET-based defence measures that are currently
in place. The suggested SRA that employs blockchain technology (SRABC) protects
MANET from attacks and authenticates nodes. The secure routing algorithm (SRA)
proposed by blockchain technology safeguards control and data flow against
threats. This is achieved by generating a Hash Function for every transaction.
We will begin by discussing the security of the MANET. This article's second
section explores the role of blockchain in MANET security. In the third
section, the SRA is described in connection with blockchain. In the fourth
phase, PDR and Throughput are utilised to conduct an SRA review using
Blockchain employing PDR and Throughput. The results suggest that the proposed
technique enhances MANET security while concurrently decreasing delay. The
performance of the proposed technique is analysed and compared to the routing
protocols Q-AODV and DSR. | [
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] |
Deep neural networks (DNNs) have substantial computational and memory
requirements, and the compilation of its computational graphs has a great
impact on the performance of resource-constrained (e.g., computation, I/O, and
memory-bound) edge computing systems. While efficient execution of their
computational graph requires an effective scheduling algorithm, generating the
optimal scheduling solution is a challenging NP-hard problem. Furthermore, the
complexity of scheduling DNN computational graphs will further increase on
pipelined multi-core systems considering memory communication cost, as well as
the increasing size of DNNs. Using the synthetic graph for the training
dataset, this work presents a reinforcement learning (RL) based scheduling
framework RESPECT, which learns the behaviors of optimal optimization
algorithms and generates near-optimal scheduling results with short solving
runtime overhead. Our framework has demonstrated up to $\sim2.5\times$
real-world on-chip inference runtime speedups over the commercial compiler with
ten popular ImageNet models deployed on the physical Coral Edge TPUs system.
Moreover, compared to the exact optimization methods, the proposed RL
scheduling improves the scheduling optimization runtime by up to 683$\times$
speedups compared to the commercial compiler and matches the exact optimal
solutions with up to 930$\times$ speedups. Finally, we perform a comprehensive
generalizability test, which demonstrates RESPECT successfully imitates optimal
solving behaviors from small synthetic graphs to large real-world DNNs
computational graphs. | [
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] |
Extending Moore's law by augmenting complementary-metal-oxide semiconductor
(CMOS) transistors with emerging nanotechnologies (X) has become increasingly
important. Accelerating Monte Carlo algorithms that rely on random sampling
with such CMOS+X technologies could have significant impact on a large number
of fields from probabilistic machine learning, optimization to quantum
simulation. In this paper, we show the combination of stochastic magnetic
tunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile Field
Programmable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Our
approach enables high-quality true randomness that is essential for Monte Carlo
based probabilistic sampling and learning. Our heterogeneous computer
successfully performs probabilistic inference and asynchronous Boltzmann
learning, despite device-to-device variations in sMTJs. A comprehensive
comparison using a CMOS predictive process design kit (PDK) reveals that
compact sMTJ-based p-bits replace 10,000 transistors while dissipating two
orders of magnitude of less energy (2 fJ per random bit), compared to digital
CMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnet
approach can significantly advance probabilistic computing and its applications
in various domains by providing massively parallel and truly random numbers
with extremely high throughput and energy-efficiency. | [
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] |
We applied physics-informed neural networks to solve the constitutive
relations for nonlinear, path-dependent material behavior. As a result, the
trained network not only satisfies all thermodynamic constraints but also
instantly provides information about the current material state (i.e., free
energy, stress, and the evolution of internal variables) under any given
loading scenario without requiring initial data. One advantage of this work is
that it bypasses the repetitive Newton iterations needed to solve nonlinear
equations in complex material models. Additionally, strategies are provided to
reduce the required order of derivative for obtaining the tangent operator. The
trained model can be directly used in any finite element package (or other
numerical methods) as a user-defined material model. However, challenges remain
in the proper definition of collocation points and in integrating several
non-equality constraints that become active or non-active simultaneously. We
tested this methodology on rate-independent processes such as the classical von
Mises plasticity model with a nonlinear hardening law, as well as local damage
models for interface cracking behavior with a nonlinear softening law. In order
to demonstrate the applicability of the methodology in handling complex path
dependency in a three-dimensional (3D) scenario, we tested the approach using
the equations governing a damage model for a three-dimensional interface model.
Such models are frequently employed for intergranular fracture at grain
boundaries. We have observed a perfect agreement between the results obtained
through the proposed methodology and those obtained using the classical
approach. Furthermore, the proposed approach requires significantly less effort
in terms of implementation and computing time compared to the traditional
methods. | [
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] |
Sleep abnormalities can have severe health consequences. Automated sleep
staging, i.e. labelling the sequence of sleep stages from the patient's
physiological recordings, could simplify the diagnostic process. Previous work
on automated sleep staging has achieved great results, mainly relying on the
EEG signal. However, often multiple sources of information are available beyond
EEG. This can be particularly beneficial when the EEG recordings are noisy or
even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated
Representation multimodal fusion network that is particularly focused on
improving the robustness of signal analysis on imperfect data. We demonstrate
how appropriately handling multimodal information can be the key to achieving
such robustness. CoRe-Sleep tolerates noisy or missing modalities segments,
allowing training on incomplete data. Additionally, it shows state-of-the-art
performance when testing on both multimodal and unimodal data using a single
model on SHHS-1, the largest publicly available study that includes sleep stage
labels. The results indicate that training the model on multimodal data does
positively influence performance when tested on unimodal data. This work aims
at bridging the gap between automated analysis tools and their clinical
utility. | [
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Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular due
to their effectiveness in analyzing and predicting the evolution of complex
interconnected graph-based systems. However, hardware deployment of DGNNs still
remains a challenge. First, DGNNs do not fully utilize hardware resources
because temporal data dependencies cause low hardware parallelism.
Additionally, there is currently a lack of generic DGNN hardware accelerator
frameworks, and existing GNN accelerator frameworks have limited ability to
handle dynamic graphs with changing topologies and node features. To address
the aforementioned challenges, in this paper, we propose DGNN-Booster, which is
a novel Field-Programmable Gate Array (FPGA) accelerator framework for
real-time DGNN inference using High-Level Synthesis (HLS). It includes two
different FPGA accelerator designs with different dataflows that can support
the most widely used DGNNs. We showcase the effectiveness of our designs by
implementing and evaluating two representative DGNN models on ZCU102 board and
measuring the end-to-end performance. The experiment results demonstrate that
DGNN-Booster can achieve a speedup of up to 5.6x compared to the CPU baseline
(6226R), 8.4x compared to the GPU baseline (A6000) and 2.1x compared to the
FPGA baseline without applying optimizations proposed in this paper. Moreover,
DGNN-Booster can achieve over 100x and over 1000x runtime energy efficiency
than the CPU and GPU baseline respectively. Our implementation code and
on-board measurements are publicly available at
https://github.com/sharc-lab/DGNN-Booster. | [
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] |
Robots operating in real-world environments must reason about possible
outcomes of stochastic actions and make decisions based on partial observations
of the true world state. A major challenge for making accurate and robust
action predictions is the problem of confounding, which if left untreated can
lead to prediction errors. The partially observable Markov decision process
(POMDP) is a widely-used framework to model these stochastic and
partially-observable decision-making problems. However, due to a lack of
explicit causal semantics, POMDP planning methods are prone to confounding bias
and thus in the presence of unobserved confounders may produce underperforming
policies. This paper presents a novel causally-informed extension of "anytime
regularized determinized sparse partially observable tree" (AR-DESPOT), a
modern anytime online POMDP planner, using causal modelling and inference to
eliminate errors caused by unmeasured confounder variables. We further propose
a method to learn offline the partial parameterisation of the causal model for
planning, from ground truth model data. We evaluate our methods on a toy
problem with an unobserved confounder and show that the learned causal model is
highly accurate, while our planning method is more robust to confounding and
produces overall higher performing policies than AR-DESPOT. | [
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] |
Reasoning on knowledge graphs is a challenging task because it utilizes
observed information to predict the missing one. Particularly, answering
complex queries based on first-order logic is one of the crucial tasks to
verify learning to reason abilities for generalization and composition.
Recently, the prevailing method is query embedding which learns the embedding
of a set of entities and treats logic operations as set operations and has
shown great empirical success. Though there has been much research following
the same formulation, many of its claims lack a formal and systematic
inspection. In this paper, we rethink this formulation and justify many of the
previous claims by characterizing the scope of queries investigated previously
and precisely identifying the gap between its formulation and its goal, as well
as providing complexity analysis for the currently investigated queries.
Moreover, we develop a new dataset containing ten new types of queries with
features that have never been considered and therefore can provide a thorough
investigation of complex queries. Finally, we propose a new neural-symbolic
method, Fuzzy Inference with Truth value (FIT), where we equip the neural link
predictors with fuzzy logic theory to support end-to-end learning using complex
queries with provable reasoning capability. Empirical results show that our
method outperforms previous methods significantly in the new dataset and also
surpasses previous methods in the existing dataset at the same time. | [
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The rapid adoption of Internet of Things (IoT) devices in healthcare has
introduced new challenges in preserving data privacy, security and patient
safety. Traditional approaches need to ensure security and privacy while
maintaining computational efficiency, particularly for resource-constrained IoT
devices. This paper proposes a novel hybrid approach combining federated
learning and blockchain technology to provide a secure and privacy-preserved
solution for IoT-enabled healthcare applications. Our approach leverages a
public-key cryptosystem that provides semantic security for local model
updates, while blockchain technology ensures the integrity of these updates and
enforces access control and accountability. The federated learning process
enables a secure model aggregation without sharing sensitive patient data. We
implement and evaluate our proposed framework using EMNIST datasets,
demonstrating its effectiveness in preserving data privacy and security while
maintaining computational efficiency. The results suggest that our hybrid
approach can significantly enhance the development of secure and
privacy-preserved IoT-enabled healthcare applications, offering a promising
direction for future research in this field. | [
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] |
Incentive mechanism is crucial for federated learning (FL) when rational
clients do not have the same interests in the global model as the server.
However, due to system heterogeneity and limited budget, it is generally
impractical for the server to incentivize all clients to participate in all
training rounds (known as full participation). The existing FL incentive
mechanisms are typically designed by stimulating a fixed subset of clients
based on their data quantity or system resources. Hence, FL is performed only
using this subset of clients throughout the entire training process, leading to
a biased model because of data heterogeneity. This paper proposes a game
theoretic incentive mechanism for FL with randomized client participation,
where the server adopts a customized pricing strategy that motivates different
clients to join with different participation levels (probabilities) for
obtaining an unbiased and high performance model. Each client responds to the
server's monetary incentive by choosing its best participation level, to
maximize its profit based on not only the incurred local cost but also its
intrinsic value for the global model. To effectively evaluate clients'
contribution to the model performance, we derive a new convergence bound which
analytically predicts how clients' arbitrary participation levels and their
heterogeneous data affect the model performance. By solving a non-convex
optimization problem, our analysis reveals that the intrinsic value leads to
the interesting possibility of bidirectional payment between the server and
clients. Experimental results using real datasets on a hardware prototype
demonstrate the superiority of our mechanism in achieving higher model
performance for the server as well as higher profits for the clients. | [
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] |
Enabling robots to learn novel visuomotor skills in a data-efficient manner
remains an unsolved problem with myriad challenges. A popular paradigm for
tackling this problem is through leveraging large unlabeled datasets that have
many behaviors in them and then adapting a policy to a specific task using a
small amount of task-specific human supervision (i.e. interventions or
demonstrations). However, how best to leverage the narrow task-specific
supervision and balance it with offline data remains an open question. Our key
insight in this work is that task-specific data not only provides new data for
an agent to train on but can also inform the type of prior data the agent
should use for learning. Concretely, we propose a simple approach that uses a
small amount of downstream expert data to selectively query relevant behaviors
from an offline, unlabeled dataset (including many sub-optimal behaviors). The
agent is then jointly trained on the expert and queried data. We observe that
our method learns to query only the relevant transitions to the task, filtering
out sub-optimal or task-irrelevant data. By doing so, it is able to learn more
effectively from the mix of task-specific and offline data compared to naively
mixing the data or only using the task-specific data. Furthermore, we find that
our simple querying approach outperforms more complex goal-conditioned methods
by 20% across simulated and real robotic manipulation tasks from images. See
https://sites.google.com/view/behaviorretrieval for videos and code. | [
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With the end of Moore's Law, there is a growing demand for rapid
architectural innovations in modern processors, such as RISC-V custom
extensions, to continue performance scaling. Program sampling is a crucial step
in microprocessor design, as it selects representative simulation points for
workload simulation. While SimPoint has been the de-facto approach for decades,
its limited expressiveness with Basic Block Vector (BBV) requires
time-consuming human tuning, often taking months, which impedes fast innovation
and agile hardware development. This paper introduces Neural Program Sampling
(NPS), a novel framework that learns execution embeddings using dynamic
snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding
generation, leveraging an application's code structures and runtime states.
AssemblyNet serves as NPS's graph model and neural architecture, capturing a
program's behavior in aspects such as data computation, code path, and data
flow. AssemblyNet is trained with a data prefetch task that predicts
consecutive memory addresses.
In the experiments, NPS outperforms SimPoint by up to 63%, reducing the
average error by 38%. Additionally, NPS demonstrates strong robustness with
increased accuracy, reducing the expensive accuracy tuning overhead.
Furthermore, NPS shows higher accuracy and generality than the state-of-the-art
GNN approach in code behavior learning, enabling the generation of high-quality
execution embeddings. | [
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] |
Balancing electrolytes is utmost important and essential for appropriate
functioning of organs in human body as electrolytes imbalance can be an
indication of the development of underlying pathophysiology. Efficient
monitoring of electrolytes imbalance not only can increase the chances of early
detection of disease, but also prevents the further deterioration of the health
by strictly following nutrient controlled diet for balancing the electrolytes
post disease detection. In this research, a recommender system MATURE Health is
proposed and implemented, which predicts the imbalance of mandatory
electrolytes and other substances presented in blood and recommends the food
items with the balanced nutrients to avoid occurrence of the electrolytes
imbalance. The proposed model takes user most recent laboratory results and
daily food intake into account to predict the electrolytes imbalance. MATURE
Health relies on MATURE Food algorithm to recommend food items as latter
recommends only those food items that satisfy all mandatory nutrient
requirements while also considering user past food preferences. To validate the
proposed method, particularly sodium, potassium, and BUN levels have been
predicted with prediction algorithm, Random Forest, for dialysis patients using
their laboratory reports history and daily food intake. And, the proposed model
demonstrates 99.53 percent, 96.94 percent and 95.35 percent accuracy for
Sodium, Potassium, and BUN respectively. MATURE Health is a novel health
recommender system that implements machine learning models to predict the
imbalance of mandatory electrolytes and other substances in the blood and
recommends the food items which contain the required amount of the nutrients
that prevent or at least reduce the risk of the electrolytes imbalance. | [
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] |
All data on the Internet are transferred by network traffic, thus accurately
modeling network traffic can help improve network services quality and protect
data privacy. Pretrained models for network traffic can utilize large-scale raw
data to learn the essential characteristics of network traffic, and generate
distinguishable results for input traffic without considering specific
downstream tasks. Effective pretrained models can significantly optimize the
training efficiency and effectiveness of downstream tasks, such as application
classification, attack detection and traffic generation. Despite the great
success of pretraining in natural language processing, there is no work in the
network field. Considering the diverse demands and characteristics of network
traffic and network tasks, it is non-trivial to build a pretrained model for
network traffic and we face various challenges, especially the heterogeneous
headers and payloads in the multi-pattern network traffic and the different
dependencies for contexts of diverse downstream network tasks.
To tackle these challenges, in this paper, we make the first attempt to
provide a generative pretrained model NetGPT for both traffic understanding and
generation tasks. We propose the multi-pattern network traffic modeling to
construct unified text inputs and support both traffic understanding and
generation tasks. We further optimize the adaptation effect of the pretrained
model to diversified tasks by shuffling header fields, segmenting packets in
flows, and incorporating diverse task labels with prompts. With diverse traffic
datasets from encrypted software, DNS, private industrial protocols and
cryptocurrency mining, expensive experiments demonstrate the effectiveness of
our NetGPT in a range of traffic understanding and generation tasks on traffic
datasets, and outperform state-of-the-art baselines by a wide margin. | [
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] |
We propose a model-based reinforcement learning (RL) approach for noisy
time-dependent gate optimization with improved sample complexity over
model-free RL. Sample complexity is the number of controller interactions with
the physical system. Leveraging an inductive bias, inspired by recent advances
in neural ordinary differential equations (ODEs), we use an auto-differentiable
ODE parametrised by a learnable Hamiltonian ansatz to represent the model
approximating the environment whose time-dependent part, including the control,
is fully known. Control alongside Hamiltonian learning of continuous
time-independent parameters is addressed through interactions with the system.
We demonstrate an order of magnitude advantage in the sample complexity of our
method over standard model-free RL in preparing some standard unitary gates
with closed and open system dynamics, in realistic numerical experiments
incorporating single shot measurements, arbitrary Hilbert space truncations and
uncertainty in Hamiltonian parameters. Also, the learned Hamiltonian can be
leveraged by existing control methods like GRAPE for further gradient-based
optimization with the controllers found by RL as initializations. Our algorithm
that we apply on nitrogen vacancy (NV) centers and transmons in this paper is
well suited for controlling partially characterised one and two qubit systems. | [
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] |
We present a system that enables an autonomous small-scale RC car to drive
aggressively from visual observations using reinforcement learning (RL). Our
system, FastRLAP (faster lap), trains autonomously in the real world, without
human interventions, and without requiring any simulation or expert
demonstrations. Our system integrates a number of important components to make
this possible: we initialize the representations for the RL policy and value
function from a large prior dataset of other robots navigating in other
environments (at low speed), which provides a navigation-relevant
representation. From here, a sample-efficient online RL method uses a single
low-speed user-provided demonstration to determine the desired driving course,
extracts a set of navigational checkpoints, and autonomously practices driving
through these checkpoints, resetting automatically on collision or failure.
Perhaps surprisingly, we find that with appropriate initialization and choice
of algorithm, our system can learn to drive over a variety of racing courses
with less than 20 minutes of online training. The resulting policies exhibit
emergent aggressive driving skills, such as timing braking and acceleration
around turns and avoiding areas which impede the robot's motion, approaching
the performance of a human driver using a similar first-person interface over
the course of training. | [
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] |
Conformation Generation is a fundamental problem in drug discovery and
cheminformatics. And organic molecule conformation generation, particularly in
vacuum and protein pocket environments, is most relevant to drug design.
Recently, with the development of geometric neural networks, the data-driven
schemes have been successfully applied in this field, both for molecular
conformation generation (in vacuum) and binding pose generation (in protein
pocket). The former beats the traditional ETKDG method, while the latter
achieves similar accuracy compared with the widely used molecular docking
software. Although these methods have shown promising results, some researchers
have recently questioned whether deep learning (DL) methods perform better in
molecular conformation generation via a parameter-free method. To our surprise,
what they have designed is some kind analogous to the famous infinite monkey
theorem, the monkeys that are even equipped with physics education. To discuss
the feasibility of their proving, we constructed a real infinite stochastic
monkey for molecular conformation generation, showing that even with a more
stochastic sampler for geometry generation, the coverage of the benchmark
QM-computed conformations are higher than those of most DL-based methods. By
extending their physical monkey algorithm for binding pose prediction, we also
discover that the successful docking rate also achieves near-best performance
among existing DL-based docking models. Thus, though their conclusions are
right, their proof process needs more concern. | [
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It is widely acknowledged that large models have the potential to deliver
superior performance across a broad range of domains. Despite the remarkable
progress made in the field of machine learning systems research, which has
enabled the development and exploration of large models, such abilities remain
confined to a small group of advanced users and industry leaders, resulting in
an implicit technical barrier for the wider community to access and leverage
these technologies. In this paper, we introduce PyTorch Fully Sharded Data
Parallel (FSDP) as an industry-grade solution for large model training. FSDP
has been closely co-designed with several key PyTorch core components including
Tensor implementation, dispatcher system, and CUDA memory caching allocator, to
provide non-intrusive user experiences and high training efficiency.
Additionally, FSDP natively incorporates a range of techniques and settings to
optimize resource utilization across a variety of hardware configurations. The
experimental results demonstrate that FSDP is capable of achieving comparable
performance to Distributed Data Parallel while providing support for
significantly larger models with near-linear scalability in terms of TFLOPS. | [
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] |
Pre-trained models (PTMs) have shown great promise in the speech and audio
domain. Embeddings leveraged from these models serve as inputs for learning
algorithms with applications in various downstream tasks. One such crucial task
is Speech Emotion Recognition (SER) which has a wide range of applications,
including dynamic analysis of customer calls, mental health assessment, and
personalized language learning. PTM embeddings have helped advance SER,
however, a comprehensive comparison of these PTM embeddings that consider
multiple facets such as embedding model architecture, data used for
pre-training, and the pre-training procedure being followed is missing. A
thorough comparison of PTM embeddings will aid in the faster and more efficient
development of models and enable their deployment in real-world scenarios. In
this work, we exploit this research gap and perform a comparative analysis of
embeddings extracted from eight speech and audio PTMs (wav2vec 2.0, data2vec,
wavLM, UniSpeech-SAT, wav2clip, YAMNet, x-vector, ECAPA). We perform an
extensive empirical analysis with four speech emotion datasets (CREMA-D, TESS,
SAVEE, Emo-DB) by training three algorithms (XGBoost, Random Forest, FCN) on
the derived embeddings. The results of our study indicate that the best
performance is achieved by algorithms trained on embeddings derived from PTMs
trained for speaker recognition followed by wav2clip and UniSpeech-SAT. This
can relay that the top performance by embeddings from speaker recognition PTMs
is most likely due to the model taking up information about numerous speech
features such as tone, accent, pitch, and so on during its speaker recognition
training. Insights from this work will assist future studies in their selection
of embeddings for applications related to SER. | [
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] |
Understanding the host-specificity of different families of viruses sheds
light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic
pathogens in humans. It enables epidemiologists, medical professionals, and
policymakers to curb existing epidemics and prevent future ones promptly. In
the family Coronaviridae (of which SARS-CoV-2 is a member), it is well-known
that the spike protein is the point of contact between the virus and the host
cell membrane. On the other hand, the two traditional mammalian orders,
Carnivora (carnivores) and Chiroptera (bats) are recognized to be responsible
for maintaining and spreading the Rabies Lyssavirus (RABV). We propose
Virus2Vec, a feature-vector representation for viral (nucleotide or amino acid)
sequences that enable vector-space-based machine learning models to identify
viral hosts. Virus2Vec generates numerical feature vectors for unaligned
sequences, allowing us to forego the computationally expensive sequence
alignment step from the pipeline. Virus2Vec leverages the power of both the
\emph{minimizer} and position weight matrix (PWM) to generate compact feature
vectors. Using several classifiers, we empirically evaluate Virus2Vec on
real-world spike sequences of Coronaviridae and rabies virus sequence data to
predict the host (identifying the reservoirs of infection). Our results
demonstrate that Virus2Vec outperforms the predictive accuracies of baseline
and state-of-the-art methods. | [
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Real-time safety assessment (RTSA) of dynamic systems is a critical task that
has significant implications for various fields such as industrial and
transportation applications, especially in non-stationary environments.
However, the absence of a comprehensive review of real-time safety assessment
methods in non-stationary environments impedes the progress and refinement of
related methods. In this paper, a review of methods and techniques for RTSA
tasks in non-stationary environments is provided. Specifically, the background
and significance of RTSA approaches in non-stationary environments are firstly
highlighted. We then present a problem description that covers the definition,
classification, and main challenges. We review recent developments in related
technologies such as online active learning, online semi-supervised learning,
online transfer learning, and online anomaly detection. Finally, we discuss
future outlooks and potential directions for further research. Our review aims
to provide a comprehensive and up-to-date overview of real-time safety
assessment methods in non-stationary environments, which can serve as a
valuable resource for researchers and practitioners in this field. | [
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] |
Most machine learning and data analytics applications, including performance
engineering in software systems, require a large number of annotations and
labelled data, which might not be available in advance. Acquiring annotations
often requires significant time, effort, and computational resources, making it
challenging. We develop a unified active learning framework specializing in
software performance prediction to address this task. We begin by parsing the
source code to an Abstract Syntax Tree (AST) and augmenting it with data and
control flow edges. Then, we convert the tree representation of the source code
to a Flow Augmented-AST graph (FA-AST) representation. Based on the graph
representation, we construct various graph embeddings (unsupervised and
supervised) into a latent space. Given such an embedding, the framework becomes
task agnostic since active learning can be performed using any regression
method and query strategy suited for regression. Within this framework, we
investigate the impact of using different levels of information for active and
passive learning, e.g., partially available labels and unlabeled test data. Our
approach aims to improve the investment in AI models for different software
performance predictions (execution time) based on the structure of the source
code. Our real-world experiments reveal that respectable performance can be
achieved by querying labels for only a small subset of all the data. | [
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] |
Surface roughness is a crucial factor influencing the performance and
functionality of additive manufactured components. Accurate prediction of
surface roughness is vital for optimizing manufacturing processes and ensuring
the quality of the final product. Quantum computing has recently gained
attention as a potential solution for tackling complex problems and creating
precise predictive models. In this research paper, we conduct an in-depth
comparison of three quantum algorithms i.e. the Quantum Neural Network (QNN),
Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) adapted for
regression for predicting surface roughness in additive manufactured specimens
for the first time. We assess the algorithms performance using Mean Squared
Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) as
evaluation metrics. Our findings show that the Q-Forest algorithm surpasses the
other algorithms, achieving an MSE of 56.905, MAE of 7.479, and an EVS of
0.2957. In contrast, the QNN algorithm displays a higher MSE of 60.840 and MAE
of 7.671, coupled with a negative EVS of -0.444, indicating that it may not be
appropriate for predicting surface roughness in this application. The VQC
adapted for regression exhibits an MSE of 59.121, MAE of 7.597, and an EVS of
-0.0106, suggesting its performance is also inferior to the Q-Forest algorithm. | [
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] |
Autonomous driving services rely heavily on sensors such as cameras, LiDAR,
radar, and communication modules. A common practice of processing the sensed
data is using a high-performance computing unit placed inside the vehicle,
which deploys AI models and algorithms to act as the brain or administrator of
the vehicle. The vehicular data generated from average hours of driving can be
up to 20 Terabytes depending on the data rate and specification of the sensors.
Given the scale and fast growth of services for autonomous driving, it is
essential to improve the overall energy and environmental efficiency,
especially in the trend towards vehicular electrification (e.g.,
battery-powered). Although the areas have seen significant advancements in
sensor technologies, wireless communications, computing and AI/ML algorithms,
the challenge still exists in how to apply and integrate those technology
innovations to achieve energy efficiency. This survey reviews and compares the
connected vehicular applications, vehicular communications, approximation and
Edge AI techniques. The focus is on energy efficiency by covering newly
proposed approximation and enabling frameworks. To the best of our knowledge,
this survey is the first to review the latest approximate Edge AI frameworks
and publicly available datasets in energy-efficient autonomous driving. The
insights and vision from this survey can be beneficial for the collaborative
driving service development on low-power and memory-constrained systems and
also for the energy optimization of autonomous vehicles. | [
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] |
Current space-based missions, such as the Transiting Exoplanet Survey
Satellite (TESS), provide a large database of light curves that must be
analysed efficiently and systematically. In recent years, deep learning (DL)
methods, particularly convolutional neural networks (CNN), have been used to
classify transit signals of candidate exoplanets automatically. However, CNNs
have some drawbacks; for example, they require many layers to capture
dependencies on sequential data, such as light curves, making the network so
large that it eventually becomes impractical. The self-attention mechanism is a
DL technique that attempts to mimic the action of selectively focusing on some
relevant things while ignoring others. Models, such as the Transformer
architecture, were recently proposed for sequential data with successful
results. Based on these successful models, we present a new architecture for
the automatic classification of transit signals. Our proposed architecture is
designed to capture the most significant features of a transit signal and
stellar parameters through the self-attention mechanism. In addition to model
prediction, we take advantage of attention map inspection, obtaining a more
interpretable DL approach. Thus, we can identify the relevance of each element
to differentiate a transit signal from false positives, simplifying the manual
examination of candidates. We show that our architecture achieves competitive
results concerning the CNNs applied for recognizing exoplanetary transit
signals in data from the TESS telescope. Based on these results, we demonstrate
that applying this state-of-the-art DL model to light curves can be a powerful
technique for transit signal detection while offering a level of
interpretability. | [
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] |
Two types of explanations have been receiving increased attention in the
literature when analyzing the decisions made by classifiers. The first type
explains why a decision was made and is known as a sufficient reason for the
decision, also an abductive explanation or a PI-explanation. The second type
explains why some other decision was not made and is known as a necessary
reason for the decision, also a contrastive or counterfactual explanation.
These explanations were defined for classifiers with binary, discrete and, in
some cases, continuous features. We show that these explanations can be
significantly improved in the presence of non-binary features, leading to a new
class of explanations that relay more information about decisions and the
underlying classifiers. Necessary and sufficient reasons were also shown to be
the prime implicates and implicants of the complete reason for a decision,
which can be obtained using a quantification operator. We show that our
improved notions of necessary and sufficient reasons are also prime implicates
and implicants but for an improved notion of complete reason obtained by a new
quantification operator that we also define and study. | [
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] |
Feature selection is one of the most relevant processes in any methodology
for creating a statistical learning model. Usually, existing algorithms
establish some criterion to select the most influential variables, discarding
those that do not contribute to the model with any relevant information. This
methodology makes sense in a static situation where the joint distribution of
the data does not vary over time. However, when dealing with real data, it is
common to encounter the problem of the dataset shift and, specifically, changes
in the relationships between variables (concept shift). In this case, the
influence of a variable cannot be the only indicator of its quality as a
regressor of the model, since the relationship learned in the training phase
may not correspond to the current situation. In tackling this problem, our
approach establishes a direct relationship between the Shapley values and
prediction errors, operating at a more local level to effectively detect the
individual biases introduced by each variable. The proposed methodology is
evaluated through various examples, including synthetic scenarios mimicking
sudden and incremental shift situations, as well as two real-world cases
characterized by concept shifts. Additionally, we perform three analyses of
standard situations to assess the algorithm's robustness in the absence of
shifts. The results demonstrate that our proposed algorithm significantly
outperforms state-of-the-art feature selection methods in concept shift
scenarios, while matching the performance of existing methodologies in static
situations. | [
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] |
This paper targets the perceptual task of separating the different
interacting voices, i.e., monophonic melodic streams, in a polyphonic musical
piece. We target symbolic music, where notes are explicitly encoded, and model
this task as a Multi-Trajectory Tracking (MTT) problem from discrete
observations, i.e., notes in a pitch-time space. Our approach builds a graph
from a musical piece, by creating one node for every note, and separates the
melodic trajectories by predicting a link between two notes if they are
consecutive in the same voice/stream. This kind of local, greedy prediction is
made possible by node embeddings created by a heterogeneous graph neural
network that can capture inter- and intra-trajectory information. Furthermore,
we propose a new regularization loss that encourages the output to respect the
MTT premise of at most one incoming and one outgoing link for every node,
favouring monophonic (voice) trajectories; this loss function might also be
useful in other general MTT scenarios. Our approach does not use
domain-specific heuristics, is scalable to longer sequences and a higher number
of voices, and can handle complex cases such as voice inversions and overlaps.
We reach new state-of-the-art results for the voice separation task in
classical music of different styles. | [
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] |
The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to
infer health measures (e.g., glucose level or blood pressure) is a very active
area of research. Such technology can have a significant impact on health
screening, chronic disease management and remote monitoring. A common approach
is to collect sensor data and corresponding labels from a clinical grade device
(e.g., blood pressure cuff), and train deep learning models to map one to the
other. Although well intentioned, this approach often ignores a principled
analysis of whether the input sensor data has enough information to predict the
desired metric. We analyze the task of predicting blood pressure from PPG pulse
wave analysis. Our review of the prior work reveals that many papers fall prey
data leakage, and unrealistic constraints on the task and the preprocessing
steps. We propose a set of tools to help determine if the input signal in
question (e.g., PPG) is indeed a good predictor of the desired label (e.g.,
blood pressure). Using our proposed tools, we have found that blood pressure
prediction using PPG has a high multi-valued mapping factor of 33.2% and low
mutual information of 9.8%. In comparison, heart rate prediction using PPG, a
well-established task, has a very low multi-valued mapping factor of 0.75% and
high mutual information of 87.7%. We argue that these results provide a more
realistic representation of the current progress towards to goal of wearable
blood pressure measurement via PPG pulse wave analysis. | [
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] |
Drowsy driving has a crucial influence on driving safety, creating an urgent
demand for driver drowsiness detection. Electroencephalogram (EEG) signal can
accurately reflect the mental fatigue state and thus has been widely studied in
drowsiness monitoring. However, the raw EEG data is inherently noisy and
redundant, which is neglected by existing works that just use single-channel
EEG data or full-head channel EEG data for model training, resulting in limited
performance of driver drowsiness detection. In this paper, we are the first to
propose an Interpretability-guided Channel Selection (ICS) framework for the
driver drowsiness detection task. Specifically, we design a two-stage training
strategy to progressively select the key contributing channels with the
guidance of interpretability. We first train a teacher network in the first
stage using full-head channel EEG data. Then we apply the class activation
mapping (CAM) to the trained teacher model to highlight the high-contributing
EEG channels and further propose a channel voting scheme to select the top N
contributing EEG channels. Finally, we train a student network with the
selected channels of EEG data in the second stage for driver drowsiness
detection. Experiments are designed on a public dataset, and the results
demonstrate that our method is highly applicable and can significantly improve
the performance of cross-subject driver drowsiness detection. | [
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] |
Context: Recent research has used data mining to develop techniques that can
guide developers through source code changes. To the best of our knowledge,
very few studies have investigated data mining techniques and--or compared
their results with other algorithms or a baseline. Objectives: This paper
proposes an automatic method for recommending source code changes using four
data mining algorithms. We not only use these algorithms to recommend source
code changes, but we also conduct an empirical evaluation. Methods: Our
investigation includes seven open-source projects from which we extracted
source change history at the file level. We used four widely data mining
algorithms \ie{} Apriori, FP-Growth, Eclat, and Relim to compare the algorithms
in terms of performance (Precision, Recall and F-measure) and execution time.
Results: Our findings provide empirical evidence that while some Frequent
Pattern Mining algorithms, such as Apriori may outperform other algorithms in
some cases, the results are not consistent throughout all the software
projects, which is more likely due to the nature and characteristics of the
studied projects, in particular their change history. Conclusion: Apriori seems
appropriate for large-scale projects, whereas Eclat appears to be suitable for
small-scale projects. Moreover, FP-Growth seems an efficient approach in terms
of execution time. | [
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] |
The ability to automatically learn movements and behaviors of increasing
complexity is a long-term goal in autonomous systems. Indeed, this is a very
complex problem that involves understanding how knowledge is acquired and
reused by humans as well as proposing mechanisms that allow artificial agents
to reuse previous knowledge. Inspired by Jean Piaget's theory's first three
sensorimotor substages, this work presents a cognitive agent based on CONAIM
(Conscious Attention-Based Integrated Model) that can learn procedures
incrementally. Throughout the paper, we show the cognitive functions required
in each substage and how adding new functions helps address tasks previously
unsolved by the agent. Experiments were conducted with a humanoid robot in a
simulated environment modeled with the Cognitive Systems Toolkit (CST)
performing an object tracking task. The system is modeled using a single
procedural learning mechanism based on Reinforcement Learning. The increasing
agent's cognitive complexity is managed by adding new terms to the reward
function for each learning phase. Results show that this approach is capable of
solving complex tasks incrementally. | [
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] |
The rapid rise in cloud computing has resulted in an alarming increase in
data centers' carbon emissions, which now accounts for >3% of global greenhouse
gas emissions, necessitating immediate steps to combat their mounting strain on
the global climate. An important focus of this effort is to improve resource
utilization in order to save electricity usage. Our proposed Full Scaling
Automation (FSA) mechanism is an effective method of dynamically adapting
resources to accommodate changing workloads in large-scale cloud computing
clusters, enabling the clusters in data centers to maintain their desired CPU
utilization target and thus improve energy efficiency. FSA harnesses the power
of deep representation learning to accurately predict the future workload of
each service and automatically stabilize the corresponding target CPU usage
level, unlike the previous autoscaling methods, such as Autopilot or FIRM, that
need to adjust computing resources with statistical models and expert
knowledge. Our approach achieves significant performance improvement compared
to the existing work in real-world datasets. We also deployed FSA on
large-scale cloud computing clusters in industrial data centers, and according
to the certification of the China Environmental United Certification Center
(CEC), a reduction of 947 tons of carbon dioxide, equivalent to a saving of
1538,000 kWh of electricity, was achieved during the Double 11 shopping
festival of 2022, marking a critical step for our company's strategic goal
towards carbon neutrality by 2030. | [
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] |
A novel framework for designing the molecular structure of chemical compounds
with a desired chemical property has recently been proposed. The framework
infers a desired chemical graph by solving a mixed integer linear program
(MILP) that simulates the computation process of a feature function defined by
a two-layered model on chemical graphs and a prediction function constructed by
a machine learning method. To improve the learning performance of prediction
functions in the framework, we design a method that splits a given data set
$\mathcal{C}$ into two subsets $\mathcal{C}^{(i)},i=1,2$ by a hyperplane in a
chemical space so that most compounds in the first (resp., second) subset have
observed values lower (resp., higher) than a threshold $\theta$. We construct a
prediction function $\psi$ to the data set $\mathcal{C}$ by combining
prediction functions $\psi_i,i=1,2$ each of which is constructed on
$\mathcal{C}^{(i)}$ independently. The results of our computational experiments
suggest that the proposed method improved the learning performance for several
chemical properties to which a good prediction function has been difficult to
construct. | [
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] |
Neural code intelligence models continue to be 'black boxes' to the human
programmer. This opacity limits their application towards code intelligence
tasks, particularly for applications like vulnerability detection where a
model's reliance on spurious correlations can be safety-critical. We introduce
a neuron-level approach to interpretability of neural code intelligence models
which eliminates redundancy due to highly similar or task-irrelevant neurons
within these networks. We evaluate the remaining important neurons using
probing classifiers which are often used to ascertain whether certain
properties have been encoded within the latent representations of neural
intelligence models. However, probing accuracies may be artificially inflated
due to repetitive and deterministic nature of tokens in code datasets.
Therefore, we adapt the selectivity metric originally introduced in NLP to
account for probe memorization, to formulate our source-code probing tasks.
Through our neuron analysis, we find that more than 95\% of the neurons are
redundant wrt. our code intelligence tasks and can be eliminated without
significant loss in accuracy. We further trace individual and subsets of
important neurons to specific code properties which could be used to influence
model predictions. We demonstrate that it is possible to identify 'number'
neurons, 'string' neurons, and higher level 'text' neurons which are
responsible for specific code properties. This could potentially be used to
modify neurons responsible for predictions based on incorrect signals.
Additionally, the distribution and concentration of the important neurons
within different source code embeddings can be used as measures of task
complexity, to compare source-code embeddings and guide training choices for
transfer learning over similar tasks. | [
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] |
For a complicated algorithm, its implementation by a human programmer usually
starts with outlining a rough control flow followed by iterative enrichments,
eventually yielding carefully generated syntactic structures and variables in a
hierarchy. However, state-of-the-art large language models generate codes in a
single pass, without intermediate warm-ups to reflect the structured thought
process of "outline-then-detail". Inspired by the recent success of
chain-of-thought prompting, we propose ChainCoder, a program synthesis language
model that generates Python code progressively, i.e. from coarse to fine in
multiple passes. We first decompose source code into layout frame components
and accessory components via abstract syntax tree parsing to construct a
hierarchical representation. We then reform our prediction target into a
multi-pass objective, each pass generates a subsequence, which is concatenated
in the hierarchy. Finally, a tailored transformer architecture is leveraged to
jointly encode the natural language descriptions and syntactically aligned I/O
data samples. Extensive evaluations show that ChainCoder outperforms
state-of-the-arts, demonstrating that our progressive generation eases the
reasoning procedure and guides the language model to generate higher-quality
solutions. Our codes are available at:
https://github.com/VITA-Group/ChainCoder. | [
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] |
The development of Adaptive Cruise Control (ACC) systems aims to enhance the
safety and comfort of vehicles by automatically regulating the speed of the
vehicle to ensure a safe gap from the preceding vehicle. However, conventional
ACC systems are unable to adapt themselves to changing driving conditions and
drivers' behavior. To address this limitation, we propose a Long Short-Term
Memory (LSTM) based ACC system that can learn from past driving experiences and
adapt and predict new situations in real time. The model is constructed based
on the real-world highD dataset, acquired from German highways with the
assistance of camera-equipped drones. We evaluated the ACC system under
aggressive lane changes when the side lane preceding vehicle cut off, forcing
the targeted driver to reduce speed. To this end, the proposed system was
assessed on a simulated driving environment and compared with a feedforward
Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model.
The results show that the LSTM-based system is 19.25% more accurate than the
ANN model and 5.9% more accurate than the MPC model in terms of predicting
future values of subject vehicle acceleration. The simulation is done in
Matlab/Simulink environment. | [
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] |
Multi-task learning for various real-world applications usually involves
tasks with logical sequential dependence. For example, in online marketing, the
cascade behavior pattern of $impression \rightarrow click \rightarrow
conversion$ is usually modeled as multiple tasks in a multi-task manner, where
the sequential dependence between tasks is simply connected with an explicitly
defined function or implicitly transferred information in current works. These
methods alleviate the data sparsity problem for long-path sequential tasks as
the positive feedback becomes sparser along with the task sequence. However,
the error accumulation and negative transfer will be a severe problem for
downstream tasks. Especially, at the beginning stage of training, the
optimization for parameters of former tasks is not converged yet, and thus the
information transferred to downstream tasks is negative. In this paper, we
propose a prior information merged model (\textbf{PIMM}), which explicitly
models the logical dependence among tasks with a novel prior information merged
(\textbf{PIM}) module for multiple sequential dependence task learning in a
curriculum manner. Specifically, the PIM randomly selects the true label
information or the prior task prediction with a soft sampling strategy to
transfer to the downstream task during the training. Following an
easy-to-difficult curriculum paradigm, we dynamically adjust the sampling
probability to ensure that the downstream task will get the effective
information along with the training. The offline experimental results on both
public and product datasets verify that PIMM outperforms state-of-the-art
baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and
the online experiments also demonstrate the effectiveness of PIMM. | [
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] |
Ground-air negotiation via speech communication is a vital prerequisite for
ensuring safety and efficiency in air traffic control (ATC) operations.
However, with the increase in traffic flow, incorrect instructions caused by
human factors bring a great threat to ATC safety. Existing flight trajectory
prediction (FTP) approaches primarily rely on the flight status of historical
trajectory, leading to significant delays in the prediction of real-time
maneuvering instruction, which is not conducive to conflict detection. A major
reason is that spoken instructions and flight trajectories are presented in
different modalities in the current air traffic control (ATC) system, bringing
great challenges to considering the maneuvering instruction in the FTP tasks.
In this paper, a spoken instruction-aware FTP framework, called SIA-FTP, is
innovatively proposed to support high-maneuvering FTP tasks by incorporating
instant spoken instruction. To address the modality gap and minimize the data
requirements, a 3-stage learning paradigm is proposed to implement the SIA-FTP
framework in a progressive manner, including trajectory-based FTP pretraining,
intent-oriented instruction embedding learning, and multi-modal finetuning.
Specifically, the FTP model and the instruction embedding with maneuvering
semantics are pre-trained using volumes of well-resourced trajectory and text
data in the 1st and 2nd stages. In succession, a multi-modal fusion strategy is
proposed to incorporate the pre-trained instruction embedding into the FTP
model and integrate the two pre-trained networks into a joint model. Finally,
the joint model is finetuned using the limited trajectory-instruction data to
enhance the FTP performance within maneuvering instruction scenarios. The
experimental results demonstrated that the proposed framework presents an
impressive performance improvement in high-maneuvering scenarios. | [
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] |
In recent years, neural models have been repeatedly touted to exhibit
state-of-the-art performance in recommendation. Nevertheless, multiple recent
studies have revealed that the reported state-of-the-art results of many neural
recommendation models cannot be reliably replicated. A primary reason is that
existing evaluations are performed under various inconsistent protocols.
Correspondingly, these replicability issues make it difficult to understand how
much benefit we can actually gain from these neural models. It then becomes
clear that a fair and comprehensive performance comparison between traditional
and neural models is needed.
Motivated by these issues, we perform a large-scale, systematic study to
compare recent neural recommendation models against traditional ones in top-n
recommendation from implicit data. We propose a set of evaluation strategies
for measuring memorization performance, generalization performance, and
subgroup-specific performance of recommendation models. We conduct extensive
experiments with 13 popular recommendation models (including two neural models
and 11 traditional ones as baselines) on nine commonly used datasets. Our
experiments demonstrate that even with extensive hyper-parameter searches,
neural models do not dominate traditional models in all aspects, e.g., they
fare worse in terms of average HitRate. We further find that there are areas
where neural models seem to outperform non-neural models, for example, in
recommendation diversity and robustness between different subgroups of users
and items. Our work illuminates the relative advantages and disadvantages of
neural models in recommendation and is therefore an important step towards
building better recommender systems. | [
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] |
Federated learning (FL) is the most popular distributed machine learning
technique. However, implementation of FL over modern wireless networks faces
key challenges caused by (i) dynamics of the network conditions and (ii) the
coexistence of multiple FL services/tasks and other network services in the
system, which are not jointly considered in prior works. Motivated by these
challenges, we introduce a generic FL paradigm over NextG networks, called
dynamic multi-service FL (DMS-FL). We identify three unexplored design
considerations in DMS-FL: (i) FL service operator accumulation, (ii) wireless
resource fragmentation, and (iii) signal strength fluctuations. We take the
first steps towards addressing these design considerations by proposing a novel
distributed ML architecture called elastic virtualized FL (EV-FL). EV-FL
unleashes the full potential of Open RAN (O-RAN) systems and introduces an
elastic resource provisioning methodology to execute FL services. It further
constitutes a multi-time-scale FL management system that introduces three
dimensions into existing FL architectures: (i) virtualization, (ii)
scalability, and (iii) elasticity. Through investigating EV-FL, we reveal a
series of open research directions for future work. We finally simulate EV-FL
to demonstrate its potential in saving wireless resources and increasing
fairness among FL services. | [
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] |
Most existing exoplanets are discovered using validation techniques rather
than being confirmed by complementary observations. These techniques generate a
score that is typically the probability of the transit signal being an
exoplanet (y(x)=exoplanet) given some information related to that signal
(represented by x). Except for the validation technique in Rowe et al. (2014)
that uses multiplicity information to generate these probability scores, the
existing validation techniques ignore the multiplicity boost information. In
this work, we introduce a framework with the following premise: given an
existing transit signal vetter (classifier), improve its performance using
multiplicity information. We apply this framework to several existing
classifiers, which include vespa (Morton et al. 2016), Robovetter (Coughlin et
al. 2017), AstroNet (Shallue & Vanderburg 2018), ExoNet (Ansdel et al. 2018),
GPC and RFC (Armstrong et al. 2020), and ExoMiner (Valizadegan et al. 2022), to
support our claim that this framework is able to improve the performance of a
given classifier. We then use the proposed multiplicity boost framework for
ExoMiner V1.2, which addresses some of the shortcomings of the original
ExoMiner classifier (Valizadegan et al. 2022), and validate 69 new exoplanets
for systems with multiple KOIs from the Kepler catalog. | [
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] |
In face of the pressing need of decarbonization in the power sector, the
re-design of electricity market is necessary as a Marco-level approach to
accommodate the high penetration of renewable generations, and to achieve power
system operation security, economic efficiency, and environmental friendliness.
However, existing market design methodologies suffer from the lack of
coordination among energy spot market (ESM), ancillary service market (ASM) and
financial market (FM), i.e., the "joint market", and the lack of reliable
simulation-based verification. To tackle these deficiencies, this two-part
paper develops a paradigmatic theory and detailed methods of the joint market
design using reinforcement-learning (RL)-based simulation. In Part 1, the
theory and framework of this novel market design philosophy are proposed.
First, the controversial market design options while designing the joint market
are summarized as the targeted research questions. Second, the Markov game
model is developed to describe the bidding game in the joint market,
incorporating the market design options to be determined. Third, a framework of
deploying multiple types of RL algorithms to simulate the market model is
developed. Finally, several market operation performance indicators are
proposed to validate the market design based on the simulation results. | [
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] |
Our research investigates the recommendation of code examples to aid software
developers, a practice that saves developers significant time by providing
ready-to-use code snippets. The focus of our study is Stack Overflow, a
commonly used resource for coding discussions and solutions, particularly in
the context of the Java programming language. We applied BERT, a powerful Large
Language Model (LLM) that enables us to transform code examples into numerical
vectors by extracting their semantic information. Once these numerical
representations are prepared, we identify Approximate Nearest Neighbors (ANN)
using Locality-Sensitive Hashing (LSH). Our research employed two variants of
LSH: Random Hyperplane-based LSH and Query-Aware LSH. We rigorously compared
these two approaches across four parameters: HitRate, Mean Reciprocal Rank
(MRR), Average Execution Time, and Relevance. Our study revealed that the
Query-Aware (QA) approach showed superior performance over the Random
Hyperplane-based (RH) method. Specifically, it exhibited a notable improvement
of 20\% to 35\% in HitRate for query pairs compared to the RH approach.
Furthermore, the QA approach proved significantly more time-efficient, with its
speed in creating hashing tables and assigning data samples to buckets being at
least four times faster. It can return code examples within milliseconds,
whereas the RH approach typically requires several seconds to recommend code
examples. Due to the superior performance of the QA approach, we tested it
against PostFinder and FaCoY, the state-of-the-art baselines. Our QA method
showed comparable efficiency proving its potential for effective code
recommendation. | [
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] |
We present a deep learning method for composite and task-driven motion
control for physically simulated characters. In contrast to existing
data-driven approaches using reinforcement learning that imitate full-body
motions, we learn decoupled motions for specific body parts from multiple
reference motions simultaneously and directly by leveraging the use of multiple
discriminators in a GAN-like setup. In this process, there is no need of any
manual work to produce composite reference motions for learning. Instead, the
control policy explores by itself how the composite motions can be combined
automatically. We further account for multiple task-specific rewards and train
a single, multi-objective control policy. To this end, we propose a novel
framework for multi-objective learning that adaptively balances the learning of
disparate motions from multiple sources and multiple goal-directed control
objectives. In addition, as composite motions are typically augmentations of
simpler behaviors, we introduce a sample-efficient method for training
composite control policies in an incremental manner, where we reuse a
pre-trained policy as the meta policy and train a cooperative policy that
adapts the meta one for new composite tasks. We show the applicability of our
approach on a variety of challenging multi-objective tasks involving both
composite motion imitation and multiple goal-directed control. | [
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] |
Code classification is a difficult issue in program understanding and
automatic coding. Due to the elusive syntax and complicated semantics in
programs, most existing studies use techniques based on abstract syntax tree
(AST) and graph neural network (GNN) to create code representations for code
classification. These techniques utilize the structure and semantic information
of the code, but they only take into account pairwise associations and neglect
the high-order correlations that already exist between nodes in the AST, which
may result in the loss of code structural information. On the other hand, while
a general hypergraph can encode high-order data correlations, it is homogeneous
and undirected which will result in a lack of semantic and structural
information such as node types, edge types, and directions between child nodes
and parent nodes when modeling AST. In this study, we propose to represent AST
as a heterogeneous directed hypergraph (HDHG) and process the graph by
heterogeneous directed hypergraph neural network (HDHGN) for code
classification. Our method improves code understanding and can represent
high-order data correlations beyond paired interactions. We assess
heterogeneous directed hypergraph neural network (HDHGN) on public datasets of
Python and Java programs. Our method outperforms previous AST-based and
GNN-based methods, which demonstrates the capability of our model. | [
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] |
When autonomous vehicles are deployed on public roads, they will encounter
countless and diverse driving situations. Many manually designed driving
policies are difficult to scale to the real world. Fortunately, reinforcement
learning has shown great success in many tasks by automatic trial and error.
However, when it comes to autonomous driving in interactive dense traffic, RL
agents either fail to learn reasonable performance or necessitate a large
amount of data. Our insight is that when humans learn to drive, they will 1)
make decisions over the high-level skill space instead of the low-level control
space and 2) leverage expert prior knowledge rather than learning from scratch.
Inspired by this, we propose ASAP-RL, an efficient reinforcement learning
algorithm for autonomous driving that simultaneously leverages motion skills
and expert priors. We first parameterized motion skills, which are diverse
enough to cover various complex driving scenarios and situations. A skill
parameter inverse recovery method is proposed to convert expert demonstrations
from control space to skill space. A simple but effective double initialization
technique is proposed to leverage expert priors while bypassing the issue of
expert suboptimality and early performance degradation. We validate our
proposed method on interactive dense-traffic driving tasks given simple and
sparse rewards. Experimental results show that our method can lead to higher
learning efficiency and better driving performance relative to previous methods
that exploit skills and priors differently. Code is open-sourced to facilitate
further research. | [
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] |
Applications in fields ranging from home care to warehouse fulfillment to
surgical assistance require robots to reliably manipulate the shape of 3D
deformable objects. Analytic models of elastic, 3D deformable objects require
numerous parameters to describe the potentially infinite degrees of freedom
present in determining the object's shape. Previous attempts at performing 3D
shape control rely on hand-crafted features to represent the object shape and
require training of object-specific control models. We overcome these issues
through the use of our novel DeformerNet neural network architecture, which
operates on a partial-view point cloud of the manipulated object and a point
cloud of the goal shape to learn a low-dimensional representation of the object
shape. This shape embedding enables the robot to learn a visual servo
controller that computes the desired robot end-effector action to iteratively
deform the object toward the target shape. We demonstrate both in simulation
and on a physical robot that DeformerNet reliably generalizes to object shapes
and material stiffness not seen during training. Crucially, using DeformerNet,
the robot successfully accomplishes three surgical sub-tasks: retraction
(moving tissue aside to access a site underneath it), tissue wrapping (a
sub-task in procedures like aortic stent placements), and connecting two
tubular pieces of tissue (a sub-task in anastomosis). | [
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] |
Graph Contrastive Learning (GCL) is an effective way to learn generalized
graph representations in a self-supervised manner, and has grown rapidly in
recent years. However, the underlying community semantics has not been well
explored by most previous GCL methods. Research that attempts to leverage
communities in GCL regards them as having the same influence on the graph,
leading to extra representation errors. To tackle this issue, we define
''community strength'' to measure the difference of influence among
communities. Under this premise, we propose a Community-Strength-enhanced Graph
Contrastive Learning (CSGCL) framework to preserve community strength
throughout the learning process. Firstly, we present two novel graph
augmentation methods, Communal Attribute Voting (CAV) and Communal Edge
Dropping (CED), where the perturbations of node attributes and edges are guided
by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive
learning scheme, where community strength is used to progressively fine-tune
the contrastive objective. We report extensive experiment results on three
downstream tasks: node classification, node clustering, and link prediction.
CSGCL achieves state-of-the-art performance compared with other GCL methods,
validating that community strength brings effectiveness and generality to graph
representations. Our code is available at
https://github.com/HanChen-HUST/CSGCL. | [
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] |
Accurately predicting the prices of financial time series is essential and
challenging for the financial sector. Owing to recent advancements in deep
learning techniques, deep learning models are gradually replacing traditional
statistical and machine learning models as the first choice for price
forecasting tasks. This shift in model selection has led to a notable rise in
research related to applying deep learning models to price forecasting,
resulting in a rapid accumulation of new knowledge. Therefore, we conducted a
literature review of relevant studies over the past three years with a view to
aiding researchers and practitioners in the field. This review delves deeply
into deep learning-based forecasting models, presenting information on model
architectures, practical applications, and their respective advantages and
disadvantages. In particular, detailed information is provided on advanced
models for price forecasting, such as Transformers, generative adversarial
networks (GANs), graph neural networks (GNNs), and deep quantum neural networks
(DQNNs). The present contribution also includes potential directions for future
research, such as examining the effectiveness of deep learning models with
complex structures for price forecasting, extending from point prediction to
interval prediction using deep learning models, scrutinising the reliability
and validity of decomposition ensembles, and exploring the influence of data
volume on model performance. | [
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] |
The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International
Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging,
multi-month, research and development competition. TDC'22 focuses on real-world
medical problems that require the innovation and implementation of artificial
intelligence/machine learning (AI/ML) algorithms on implantable devices. The
challenge problem of TDC'22 is to develop a novel AI/ML-based real-time
detection algorithm for life-threatening ventricular arrhythmia over low-power
microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs).
The dataset contains more than 38,000 5-second intracardiac electrograms
(IEGMs) segments over 8 different types of rhythm from 90 subjects. The
dedicated hardware platform is NUCLEO-L432KC manufactured by
STMicroelectronics. TDC'22, which is open to multi-person teams world-wide,
attracted more than 150 teams from over 50 organizations. This paper first
presents the medical problem, dataset, and evaluation procedure in detail. It
further demonstrates and discusses the designs developed by the leading teams
as well as representative results. This paper concludes with the direction of
improvement for the future TinyML design for health monitoring applications. | [
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] |
This study explores the vaccine prioritization strategy to reduce the overall
burden of the pandemic when the supply is limited. Existing methods conduct
macro-level or simplified micro-level vaccine distribution by assuming the
homogeneous behavior within subgroup populations and lacking mobility dynamics
integration. Directly applying these models for micro-level vaccine allocation
leads to sub-optimal solutions due to the lack of behavioral-related details.
To address the issue, we first incorporate the mobility heterogeneity in
disease dynamics modeling and mimic the disease evolution process using a
Trans-vaccine-SEIR model. Then we develop a novel deep reinforcement learning
to seek the optimal vaccine allocation strategy for the high-degree
spatial-temporal disease evolution system. The graph neural network is used to
effectively capture the structural properties of the mobility contact network
and extract the dynamic disease features. In our evaluation, the proposed
framework reduces 7% - 10% of infections and deaths than the baseline
strategies. Extensive evaluation shows that the proposed framework is robust to
seek the optimal vaccine allocation with diverse mobility patterns in the
micro-level disease evolution system. In particular, we find the optimal
vaccine allocation strategy in the transit usage restriction scenario is
significantly more effective than restricting cross-zone mobility for the top
10% age-based and income-based zones. These results provide valuable insights
for areas with limited vaccines and low logistic efficacy. | [
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Many organisations manage service quality and monitor a large set devices and
servers where each entity is associated with telemetry or physical sensor data
series. Recently, various methods have been proposed to detect behavioural
anomalies, however existing approaches focus on multivariate time series and
ignore communication between entities. Moreover, we aim to support end-users in
not only in locating entities and sensors causing an anomaly at a certain
period, but also explain this decision. We propose a scalable approach to
detect anomalies using a two-step approach. First, we recover relations between
entities in the network, since relations are often dynamic in nature and caused
by an unknown underlying process. Next, we report anomalies based on an
embedding of sequential patterns. Pattern mining is efficient and supports
interpretation, i.e. patterns represent frequent occurring behaviour in time
series. We extend pattern mining to filter sequential patterns based on
frequency, temporal constraints and minimum description length. We collect and
release two public datasets for international broadcasting and X from an
Internet company. \textit{BAD} achieves an overall F1-Score of 0.78 on 9
benchmark datasets, significantly outperforming the best baseline by 3\%.
Additionally, \textit{BAD} is also an order-of-magnitude faster than
state-of-the-art anomaly detection methods. | [
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] |
N-ary facts composed of a primary triple (head entity, relation, tail entity)
and an arbitrary number of auxiliary attribute-value pairs, are prevalent in
real-world knowledge graphs (KGs). Link prediction on n-ary facts is to predict
a missing element in an n-ary fact. This helps populate and enrich KGs and
further promotes numerous downstream applications. Previous studies usually
require a substantial amount of high-quality data to understand the elements in
n-ary facts. However, these studies overlook few-shot relations, which have
limited labeled instances, yet are common in real-world scenarios. Thus, this
paper introduces a new task, few-shot link prediction on n-ary facts. It aims
to predict a missing entity in an n-ary fact with limited labeled instances. We
further propose a model for Few-shot Link prEdict on N-ary facts, thus called
FLEN, which consists of three modules: the relation learning, support-specific
adjusting, and query inference modules. FLEN captures relation meta information
from limited instances to predict a missing entity in a query instance. To
validate the effectiveness of FLEN, we construct three datasets based on
existing benchmark data. Our experimental results show that FLEN significantly
outperforms existing related models in both few-shot link prediction on n-ary
facts and binary facts. | [
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] |
Traditional drug discovery programs are being transformed by the advent of
machine learning methods. Among these, Generative AI methods (GM) have gained
attention due to their ability to design new molecules and enhance specific
properties of existing ones. However, current GM methods have limitations, such
as low affinity towards the target, unknown ADME/PK properties, or the lack of
synthetic tractability. To improve the applicability domain of GM methods, we
have developed a workflow based on a variational autoencoder coupled with
active learning steps. The designed GM workflow iteratively learns from
molecular metrics, including drug likeliness, synthesizability, similarity, and
docking scores. In addition, we also included a hierarchical set of criteria
based on advanced molecular modeling simulations during a final selection step.
We tested our GM workflow on two model systems, CDK2 and KRAS. In both cases,
our model generated chemically viable molecules with a high predicted affinity
toward the targets. Particularly, the proportion of high-affinity molecules
inferred by our GM workflow was significantly greater than that in the training
data. Notably, we also uncovered novel scaffolds significantly dissimilar to
those known for each target. These results highlight the potential of our GM
workflow to explore novel chemical space for specific targets, thereby opening
up new possibilities for drug discovery endeavors. | [
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] |
Due to the unavailability of routing information in design stages prior to
detailed routing (DR), the tasks of timing prediction and optimization pose
major challenges. Inaccurate timing prediction wastes design effort, hurts
circuit performance, and may lead to design failure. This work focuses on
timing prediction after clock tree synthesis and placement legalization, which
is the earliest opportunity to time and optimize a "complete" netlist. The
paper first documents that having "oracle knowledge" of the final post-DR
parasitics enables post-global routing (GR) optimization to produce improved
final timing outcomes. To bridge the gap between GR-based parasitic and timing
estimation and post-DR results during post-GR optimization, machine learning
(ML)-based models are proposed, including the use of features for macro
blockages for accurate predictions for designs with macros. Based on a set of
experimental evaluations, it is demonstrated that these models show higher
accuracy than GR-based timing estimation. When used during post-GR
optimization, the ML-based models show demonstrable improvements in post-DR
circuit performance. The methodology is applied to two different tool flows -
OpenROAD and a commercial tool flow - and results on 45nm bulk and 12nm FinFET
enablements show improvements in post-DR slack metrics without increasing
congestion. The models are demonstrated to be generalizable to designs
generated under different clock period constraints and are robust to training
data with small levels of noise. | [
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] |
This two-part paper develops a paradigmatic theory and detailed methods of
the joint electricity market design using reinforcement-learning (RL)-based
simulation. In Part 2, this theory is further demonstrated by elaborating
detailed methods of designing an electricity spot market (ESM), together with a
reserved capacity product (RC) in the ancillary service market (ASM) and a
virtual bidding (VB) product in the financial market (FM). Following the theory
proposed in Part 1, firstly, market design options in the joint market are
specified. Then, the Markov game model is developed, in which we show how to
incorporate market design options and uncertain risks in model formulation. A
multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a
practical implementation of the generalized market simulation method developed
in Part 1. Finally, the case study demonstrates how to pick the best market
design options by using some of the market operation performance indicators
proposed in Part 1, based on the simulation results generated by implementing
the MAPPO algorithm. The impacts of different market design options on market
participants' bidding strategy preference are also discussed. | [
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] |
The Nash Equilibrium (NE) estimation in bidding games of electricity markets
is the key concern of both generation companies (GENCOs) for bidding strategy
optimization and the Independent System Operator (ISO) for market surveillance.
However, existing methods for NE estimation in emerging modern electricity
markets (FEM) are inaccurate and inefficient because the priori knowledge of
bidding strategies before any environment changes, such as load demand
variations, network congestion, and modifications of market design, is not
fully utilized. In this paper, a Bayes-adaptive Markov Decision Process in FEM
(BAMDP-FEM) is therefore developed to model the GENCOs' bidding strategy
optimization considering the priori knowledge. A novel Multi-Agent Generative
Adversarial Imitation Learning algorithm (MAGAIL-FEM) is then proposed to
enable GENCOs to learn simultaneously from priori knowledge and interactions
with changing environments. The obtained NE is a Bayesian Nash Equilibrium
(BNE) with priori knowledge transferred from the previous environment. In the
case study, the superiority of this proposed algorithm in terms of convergence
speed compared with conventional methods is verified. It is concluded that the
optimal bidding strategies in the obtained BNE can always lead to more profits
than NE due to the effective learning from the priori knowledge. Also, BNE is
more accurate and consistent with situations in real-world markets. | [
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Deep learning-based recommendation systems (e.g., DLRMs) are widely used AI
models to provide high-quality personalized recommendations. Training data used
for modern recommendation systems commonly includes categorical features taking
on tens-of-millions of possible distinct values. These categorical tokens are
typically assigned learned vector representations, that are stored in large
embedding tables, on the order of 100s of GB. Storing and accessing these
tables represent a substantial burden in commercial deployments. Our work
proposes MEM-REC, a novel alternative representation approach for embedding
tables. MEM-REC leverages bloom filters and hashing methods to encode
categorical features using two cache-friendly embedding tables. The first table
(token embedding) contains raw embeddings (i.e. learned vector representation),
and the second table (weight embedding), which is much smaller, contains
weights to scale these raw embeddings to provide better discriminative
capability to each data point. We provide a detailed architecture, design and
analysis of MEM-REC addressing trade-offs in accuracy and computation
requirements, in comparison with state-of-the-art techniques. We show that
MEM-REC can not only maintain the recommendation quality and significantly
reduce the memory footprint for commercial scale recommendation models but can
also improve the embedding latency. In particular, based on our results,
MEM-REC compresses the MLPerf CriteoTB benchmark DLRM model size by 2900x and
performs up to 3.4x faster embeddings while achieving the same AUC as that of
the full uncompressed model. | [
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] |
Applications of Reinforcement Learning in the Finance Technology (Fintech)
have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning,
through its vast competence and proficiency, has aided remarkable results in
the field of Fintech. The objective of this systematic survey is to perform an
exploratory study on a correlation between reinforcement learning and Fintech
to highlight the prediction accuracy, complexity, scalability, risks,
profitability and performance. Major uses of reinforcement learning in finance
or Fintech include portfolio optimization, credit risk reduction, investment
capital management, profit maximization, effective recommendation systems, and
better price setting strategies. Several studies have addressed the actual
contribution of reinforcement learning to the performance of financial
institutions. The latest studies included in this survey are publications from
2018 onward. The survey is conducted using PRISMA technique which focuses on
the reporting of reviews and is based on a checklist and four-phase flow
diagram. The conducted survey indicates that the performance of RL-based
strategies in Fintech fields proves to perform considerably better than other
state-of-the-art algorithms. The present work discusses the use of
reinforcement learning algorithms in diverse decision-making challenges in
Fintech and concludes that the organizations dealing with finance can benefit
greatly from Robo-advising, smart order channelling, market making, hedging and
options pricing, portfolio optimization, and optimal execution. | [
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] |
Deep reinforcement learning (DRL) has emerged as a promising approach for
developing more intelligent autonomous vehicles (AVs). A typical DRL
application on AVs is to train a neural network-based driving policy. However,
the black-box nature of neural networks can result in unpredictable decision
failures, making such AVs unreliable. To this end, this work proposes a method
to identify and protect unreliable decisions of a DRL driving policy. The basic
idea is to estimate and constrain the policy's performance uncertainty, which
quantifies potential performance drop due to insufficient training data or
network fitting errors. By constraining the uncertainty, the DRL model's
performance is always greater than that of a baseline policy. The uncertainty
caused by insufficient data is estimated by the bootstrapped method. Then, the
uncertainty caused by the network fitting error is estimated using an ensemble
network. Finally, a baseline policy is added as the performance lower bound to
avoid potential decision failures. The overall framework is called
uncertainty-bound reinforcement learning (UBRL). The proposed UBRL is evaluated
on DRL policies with different amounts of training data, taking an unprotected
left-turn driving case as an example. The result shows that the UBRL method can
identify potentially unreliable decisions of DRL policy. The UBRL guarantees to
outperform baseline policy even when the DRL policy is not well-trained and has
high uncertainty. Meanwhile, the performance of UBRL improves with more
training data. Such a method is valuable for the DRL application on real-road
driving and provides a metric to evaluate a DRL policy. | [
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] |
Protein-protein interactions (PPIs) are crucial in various biological
processes and their study has significant implications for drug development and
disease diagnosis. Existing deep learning methods suffer from significant
performance degradation under complex real-world scenarios due to various
factors, e.g., label scarcity and domain shift. In this paper, we propose a
self-ensembling multigraph neural network (SemiGNN-PPI) that can effectively
predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we
not only model the protein correlations but explore the label dependencies by
constructing and processing multiple graphs from the perspectives of both
features and labels in the graph learning process. We further marry GNN with
Mean Teacher to effectively leverage unlabeled graph-structured PPI data for
self-ensemble graph learning. We also design multiple graph consistency
constraints to align the student and teacher graphs in the feature embedding
space, enabling the student model to better learn from the teacher model by
incorporating more relationships. Extensive experiments on PPI datasets of
different scales with different evaluation settings demonstrate that
SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly
in challenging scenarios such as training with limited annotations and testing
on unseen data. | [
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] |
Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT)
System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and
nano-robotics need to run many diverse tasks within a power envelope of a few
tens of mW over a wide range of operating conditions: compute-intensive but
strongly quantized Deep Neural Network (DNN) inference, as well as signal
processing and control requiring high-precision floating-point. We present
Marsellus, an all-digital heterogeneous SoC for AI-IoT end-nodes fabricated in
GlobalFoundries 22nm FDX that combines 1) a general-purpose cluster of 16
RISC-V Digital Signal Processing (DSP) cores attuned for the execution of a
diverse range of workloads exploiting 4-bit and 2-bit arithmetic extensions
(XpulpNN), combined with fused MAC&LOAD operations and floating-point support;
2) a 2-8bit Reconfigurable Binary Engine (RBE) to accelerate 3x3 and 1x1
(pointwise) convolutions in DNNs; 3) a set of On-Chip Monitoring (OCM) blocks
connected to an Adaptive Body Biasing (ABB) generator and a hardware control
loop, enabling on-the-fly adaptation of transistor threshold voltages.
Marsellus achieves up to 180 Gop/s or 3.32 Top/s/W on 2-bit precision
arithmetic in software, and up to 637 Gop/s or 12.4 Top/s/W on
hardware-accelerated DNN layers. | [
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] |
Quantifying forecast uncertainty is a key aspect of state-of-the-art
numerical weather prediction and data assimilation systems. Ensemble-based data
assimilation systems incorporate state-dependent uncertainty quantification
based on multiple model integrations. However, this approach is demanding in
terms of computations and development. In this work a machine learning method
is presented based on convolutional neural networks that estimates the
state-dependent forecast uncertainty represented by the forecast error
covariance matrix using a single dynamical model integration. This is achieved
by the use of a loss function that takes into account the fact that the
forecast errors are heterodastic. The performance of this approach is examined
within a hybrid data assimilation method that combines a Kalman-like analysis
update and the machine learning based estimation of a state-dependent forecast
error covariance matrix. Observing system simulation experiments are conducted
using the Lorenz'96 model as a proof-of-concept. The promising results show
that the machine learning method is able to predict precise values of the
forecast covariance matrix in relatively high-dimensional states. Moreover, the
hybrid data assimilation method shows similar performance to the ensemble
Kalman filter outperforming it when the ensembles are relatively small. | [
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In the current landscape of ever-increasing levels of digitalization, we are
facing major challenges pertaining to scalability. Recommender systems have
become irreplaceable both for helping users navigate the increasing amounts of
data and, conversely, aiding providers in marketing products to interested
users. The growing awareness of discrimination in machine learning methods has
recently motivated both academia and industry to research how fairness can be
ensured in recommender systems. For recommender systems, such issues are well
exemplified by occupation recommendation, where biases in historical data may
lead to recommender systems relating one gender to lower wages or to the
propagation of stereotypes. In particular, consumer-side fairness, which
focuses on mitigating discrimination experienced by users of recommender
systems, has seen a vast number of diverse approaches for addressing different
types of discrimination. The nature of said discrimination depends on the
setting and the applied fairness interpretation, of which there are many
variations. This survey serves as a systematic overview and discussion of the
current research on consumer-side fairness in recommender systems. To that end,
a novel taxonomy based on high-level fairness interpretation is proposed and
used to categorize the research and their proposed fairness evaluation metrics.
Finally, we highlight some suggestions for the future direction of the field. | [
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] |
Antibodies are crucial proteins produced by the immune system in response to
foreign substances or antigens. The specificity of an antibody is determined by
its complementarity-determining regions (CDRs), which are located in the
variable domains of the antibody chains and form the antigen-binding site.
Previous studies have utilized complex techniques to generate CDRs, but they
suffer from inadequate geometric modeling. Moreover, the common iterative
refinement strategies lead to an inefficient inference. In this paper, we
propose a \textit{simple yet effective} model that can co-design 1D sequences
and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple
the antibody CDR design problem into two stages: (i) geometric modeling of
protein complex structures and (ii) sequence-structure co-learning. We develop
a novel macromolecular structure invariant embedding, typically for protein
complexes, that captures both intra- and inter-component interactions among the
backbone atoms, including C$\alpha$, N, C, and O atoms, to achieve
comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP
for sequence-structure co-learning, allowing sequence and structure
representations to implicitly refine each other. This enables our model to
design desired sequences and structures in a one-shot manner. Extensive
experiments are conducted to evaluate our results at both the sequence and
structure level, which demonstrate that our model achieves superior performance
compared to the state-of-the-art antibody CDR design methods. | [
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Application security is an essential part of developing modern software, as
lots of attacks depend on vulnerabilities in software. The number of attacks is
increasing globally due to technological advancements. Companies must include
security in every stage of developing, testing, and deploying their software in
order to prevent data breaches. There are several methods to detect software
vulnerability Non-AI-based such as Static Application Security Testing (SAST)
and Dynamic Application Security Testing (DAST). However, these approaches have
substantial false-positive and false-negative rates. On the other side,
researchers have been interested in developing an AI-based vulnerability
detection system employing deep learning models like BERT, BLSTM, etc. In this
paper, we proposed a two-stage solution, two deep learning models were proposed
for vulnerability detection in C/C++ source codes, the first stage is CNN which
detects if the source code contains any vulnerability (binary classification
model) and the second stage is CNN-LTSM that classifies this vulnerability into
a class of 50 different types of vulnerabilities (multiclass classification
model). Experiments were done on SySeVR dataset. Results show an accuracy of
99% for the first and 98% for the second stage. | [
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