We are used to the conventional model in which access control is provided by a trusted central entity or by a set of distributed entities that coordinate to mimic a central entity.Authorization decisions are based on a single, append-only total ordering of all actions, including policy updates, which leads to strong consistency guarantees.Recent systems based on conflict-free replicated data types (CRDTs) break with this conceptual model to gain fundamental advantages in latency, availability, resilience, and Byzantine fault tolerance.These systems replace up-front coordination with subsequent reconciliation of decentralized authorization decisions and policy updates.One of these is the Matrix group communication system, whose massive public sector deployments in Europe necessitate timely characterization of the underlying alternative conceptual model.Similarly to eventually consistent replication in CRDTs, we define 'eventually consistent access control' and present its consequences.Our model postulates thinking in two orderings of actions with different consequences for authorization: a partial ordering for storage, where the past of an action is final knowledge, and a total ordering for execution, where the past of an action is a mutable belief.

Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore the novel field of incorporating privacy, security, and access control constraints with robot task planning approaches. We report preliminary results on the classical symbolic approach, deep-learned neural networks, and modern ideas using large language models as knowledge base. From analyzing their trade-offs, we conclude that a hybrid approach is necessary, and thereby present a new use case for the emerging field of neuro-symbolic artificial intelligence.

Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore incorporating privacy and security constraints (Activity-Centric Access Control and Deep Learning Based Access Control) with robot task planning approaches (classical symbolic planning and end-to-end learning-based planning). We report preliminary results on their respective trade-offs and conclude that a hybrid approach will most likely be the method of choice.

In the not too distant future, assistive humanoid robots will provide versatile assistance for coping with everyday life. In their interactions with humans, not only safety, but also security and privacy issues need to be considered. In this Blue Sky paper, we therefore argue that it is time to bring task planning and execution as a well-established field of robotics with access and usage control in the field of security and privacy closer together. In particular, the recently proposed activity-based view on access and usage control provides a promising approach to bridge the gap between these two perspectives. We argue that humanoid robots provide for specific challenges due to their task-universality and their use in both, private and public spaces. Furthermore, they are socially connected to various parties and require policy creation at runtime due to learning. We contribute first attempts on the architecture and enforcement layer as well as on joint modeling, and discuss challenges and a research roadmap also for the policy and objectives layer. We conclude that the underlying combination of decentralized systems' and smart environments' research aspects provides for a rich source of challenges that need to be addressed on the road to deployment.

The Matrix message-oriented middleware (see https://matrix.org) is gaining momentum as a basis for a decentralized, secure messaging system as shown, for example, by its deployment within the French government and by the Mozilla foundation. Thus, understanding the corresponding access control approach is important. This paper provides an ab- straction and an analysis of the access control approach followed by Matrix. We show that Matrix can be seen as a form of Distributed Ledger Technology (DLT) based on Transaction-based Directed Acyclic Graphs (TDAGs). TDAGs connect individual transactions to form a DAG, instead of collecting transactions in blocks as in blockchains. These TDAGs only provide causal order, eventual consistency, and no finality. However, unlike conventional DLTs, Matrix does not aim for a strict system-wide consensus. Thus, there is also no guarantee for a strict consensus on access rights. By de- composition of the Matrix approach, we show that a sound decen- tralized access control can be implemented for TDAGs in general, and for Matrix in particular, despite those weak guarantees. In ad- dition, we discovered security issues in popular implementations and emphasize the need for a formal verification of the employed conflict resolution mechanism.

Graphics processing units (GPUs) are increasingly applied to accelerate tasks such as graph problems and discreteevent simulation that are characterized by irregularity, i.e., a strong dependence of the control flow and memory accesses on the input. The core data structure in many of these irregular tasks are priority queues that guide the progress of the computations and which can easily become the bottleneck of an application. To our knowledge, currently no systematic comparison of priority queue implementations on GPUs exists in the literature. We close this gap by a performance evaluation of GPU-based priority queue implementations for two applications: discrete-event simulation and parallel A* path searches on grids. We focus on scenarios requiring large numbers of priority queues holding up to a few thousand items each. We present performance measurements covering linear queue designs, implicit binary heaps, splay trees, and a GPU-specific proposal from the literature. The measurement results show that up to about 500 items per queue, circular buffers frequently outperform tree-based queues for the considered applications, particularly under a simple parallelization of individual item enqueue operations. We analyze profiling metrics to explore classical queue designs in light of the importance of high hardware utilization as well as homogeneous computations and memory accesses across GPU threads.

In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach.

In this paper we propose a procedure for estimating the geometric configuration of an arbitrary acoustic sensor placement. It determines the position and the orientation of microphone arrays in 2D while locating a source by direction-of-arrival (DoA) estimation. Neither artificial calibration signals nor unnatural user activity are required. The problem of scale indeterminacy inherent to DoA-only observations is solved by adding time difference of arrival (TDOA) measurements. The geometry calibration method is numerically stable and delivers precise results in moderately reverberated rooms. Simulation results are confirmed by laboratory experiments.