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Safe Session-Based Concurrency with Shared Linear State
We introduce \(\textsf{CLASS}\) , a session-typed,...
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The Physical Principles of the Construction of Systems for Safe Monitoring of the State of a Human Operator
AbstractBased on an analysis of statistical data on railway and road traffic, as well as laboratory studies, mathematical models are developed that...
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Safe Contrastive Clustering
Contrastive clustering is an effective deep clustering approach, which learns both instance-level consistency and cluster-level consistency in a...
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Improving Time to Take Over Through HMI Strategies Nudging a Safe Driving State
In scenarios of partially autonomous driving, drivers can easily become distracted and engage in secondary activities unrelated to driving. However,...
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SAFe transformation in a large financial corporation
As agile software development is increasingly adopted in the software industry, the popularity of scaling frameworks supporting adoption in large...
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Time Travel and Fail-Safe
Data is an asset that has a major impact on the long-term success of an enterprise. Access to the right data at the right time is extremely important...
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SASH: Safe Autonomous Self-Healing
With the large scale and user demands on modern cloud systems there is a need for autonomous approaches to self-healing. When there is no operator in...
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Safe Autonomous Decision-Making with vGOAL
Safety is one of the crucial features of autonomous systems. Safe decision-making is a critical and challenging task in develo** such systems. To...
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Safe Distributional Reinforcement Learning
Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this...
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The Solvability of Consensus in Iterated Models Extended with Safe-Consensus
The safe-consensus task was introduced by Afek, Gafni and Lieber (DISC’ 09) as a weakening of the classic consensus. When there is concurrency, the...
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Nudging the Safe Zone: Design and Assessment of HMI Strategies Based on Intelligent Driver State Monitoring Systems
Dangerous driver behavior can arise from different factors: distraction, sleepiness, and emotional states like anger, anxiety, boredom, or happiness....
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Safe and Robust Transfer Learning
In this chapter, we discuss the safety and robustness of transfer learning. By safety, we refer to its defense and solutions against attack and data...
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A refinement-based approach to safe smart contract deployment and evolution
In our previous work, we proposed a verification framework that shifts from the “code is law” to a new “specification is law” paradigm related to the...
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Neural-network-based safe learning control for non-zero-sum differential games of nonlinear systems with asymmetric input constraints
This paper primarily investigates a neural-network-based safe control scheme for solving the optimal control problem of continuous-time (CT)...
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Safe optimal robust control of nonlinear systems with asymmetric input constraints using reinforcement learning
External disturbances and asymmetric input constraints may cause a major problem to the optimal control of the system. Aiming at such problem, this...
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State
The State Pattern encapsulates state expressions in objects. This can be useful if an object shows different behavior depending on its state. With...
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Safe Exploration Method for Reinforcement Learning Under Existence of Disturbance
Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their...
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Online disinformation in the 2020 U.S. election: swing vs. safe states
For U.S. presidential elections, most states use the so-called winner-take-all system, in which the state’s presidential electors are awarded to the...
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Generic Security of the SAFE API and Its Applications
We provide security foundations for SAFE, a recently introduced API framework for sponge-based hash functions tailored to prime-field-based...
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SAMBA: safe model-based & active reinforcement learning
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information...
