Monocular 3D object detection is essential for identifying objects in road images, thus offering valuable environmental perception data that are crucial for human-centric autonomous driving systems. However, due to the inherent limitations of camera ...

With the improvement of edge-based autonomous systems such as mobile Industrial IoT(IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the ...

Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-powered autonomous and adaptive systems (AASs). However, a discernible disconnect exists among research efforts across different communities. The machine learning ...

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area ...

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty because Online RL can leverage data only available at run time. With Deep RL gaining interest, the learned knowledge is no ...

Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require ...

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-...

Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and ...

This article focuses on the problem of optimizing the system utility of Machine Learning (ML)-based systems in the presence of ML mispredictions. This is achieved via the use of self-adaptive systems and through the execution of adaptation tactics, such ...

The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with ...

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been ...

When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of ...

As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and ...

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data ...

The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s ...

We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more ...

Power capping is an important technique for high-density servers to safely oversubscribe the power infrastructure in a data center. However, power capping is commonly accomplished by dynamically lowering the server processors’ frequency levels, which can ...

Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions are ...

Various methods have been proposed in the literature for identifying subgoals in discrete reinforcement learning (RL) tasks. Once subgoals are discovered, task decomposition methods can be employed to improve the learning performance of agents. In this ...

Self-governing hybrid societies are multi-agent systems where humans and machines interact by adapting to each other’s behaviour. Advancements in Artificial Intelligence (AI) have brought an increasing hybridisation of our societies, where one particular ...