Data-Driven Transition Matrix Estimation in Probabilistic Learning Models for Autonomous Driving

AbstractMoral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.

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A Survey on Data-driven Network Intrusion Detection

ACM Computing Surveys ◽

10.1145/3472753 ◽

2022 ◽

Vol 54 (9) ◽

pp. 1-36

Author(s):

Dylan Chou ◽

Meng Jiang

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Real World ◽

Data Driven ◽

Network Intrusion Detection ◽

Large Network ◽

Learning Models ◽

Simulated Environments ◽

Network Intrusion ◽

Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

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Comparison of Machine Learning Models for Data-Driven Aircraft Icing Severity Evaluation

Journal of Aerospace Information Systems ◽

10.2514/1.i011047 ◽

2021 ◽

pp. 1-5

Author(s):

Sibo Li ◽

Roberto Paoli

Keyword(s):

Machine Learning ◽

Data Driven ◽

Aircraft Icing ◽

Learning Models ◽

Machine Learning Models

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Learning Damage Representations with Sequence-to-Sequence Models

Sensors ◽

10.3390/s22020452 ◽

2022 ◽

Vol 22 (2) ◽

pp. 452

Author(s):

Qun Yang ◽

Dejian Shen

Keyword(s):

Damage Detection ◽

Health Monitoring ◽

Research Community ◽

Regions Of Interest ◽

Data Driven ◽

Economic Losses ◽

Shake Table ◽

Learning Models ◽

Shake Table Tests ◽

Damage States

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.

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Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing

Journal of Semiconductors ◽

10.1088/1674-4926/42/12/124101 ◽

2021 ◽

Vol 42 (12) ◽

pp. 124101

Author(s):

Thomas Hirtz ◽

Steyn Huurman ◽

He Tian ◽

Yi Yang ◽

Tian-Ling Ren

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Information Technologies ◽

Deep Neural Networks ◽

State Of The Art ◽

Data Driven ◽

Sufficient Data ◽

Learning Models ◽

Simulation Tools ◽

New Information

Abstract In a world where data is increasingly important for making breakthroughs, microelectronics is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices. This infrastructure is crucial for generating sufficient data for the use of new information technologies. This situation generates a cleavage between most of the researchers and the industry. To address this issue, this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing. The multi-I–V curves that we obtained were processed simultaneously using convolutional neural networks, which gave us the ability to predict a full set of device characteristics with a single inference. We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data. We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research, such as device engineering, yield engineering or process monitoring. Moreover, this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics, without the need for expensive experimentation infrastructure.

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