Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for many learners. Recent advances in machine learning (ML) have shown the potential to support learners in complex writing tasks. To test the effects of ML-based support on students’ legal writing skills, we developed the intelligent writing support system LegalWriter. We evaluated the system’s effectiveness with 62 students. We showed that students who received intelligent writing support based on their errors wrote more structured and persuasive case solutions with a better quality of legal writing than the current benchmark. At the same time, our results demonstrated the positive effects on the students’ writing processes.

We introduce PQNoise, a post-quantum variant of the Noise framework. We demonstrate that it is possible to replace the Diffie-Hellman key-exchanges in Noise with KEMs in a secure way. A challenge is the inability to combine key pairs of KEMs, which can be resolved by certain forms of randomness-hardening for which we introduce a formal abstraction. We provide a generic recipe to turn classical Noise patterns into PQNoise patterns. We prove that the resulting PQNoise patterns achieve confidentiality and authenticity in the fACCE model. Moreover we show that for those classical Noise-patterns that have been conjectured or proven secure in the fACCE model our matching PQNoise patterns eventually achieve the same security. Our security proof is generic and applies to any valid PQNoise pattern. This is made possible by another abstraction, called a hash-object, which hides the exact workings of how keying material is processed in an abstract stateful object that outputs pseudorandom keys under different corruption patterns. We also show that the hash chains used in Noise are a secure hash-object. Finally, we demonstrate the practicality of PQNoise delivering benchmarks for several base patterns.

In this paper we present PQ-WireGuard, a post-quantum variant of the handshake in the WireGuard VPN protocol (NDSS 2017). Unlike most previous work on post-quantum security for real-world protocols, this variant does not only consider post-quantum confidentiality (or forward secrecy) but also post-quantum authentication. To achieve this, we replace the Diffie-Hellman-based handshake by a more generic approach only using key-encapsulation mechanisms (KEMs). We establish security of PQ-WireGuard, adapting the security proofs for WireGuard in the symbolic model and in the standard model to our construction. We then instantiate this generic construction with concrete post-quantum secure KEMs, which we carefully select to achieve high security and speed. We demonstrate competitiveness of PQ-WireGuard presenting extensive bench-marking results comparing to widely deployed VPN solutions.

In this work we take a formal look at deniability in group chat applications and introduce the concept of "epochal signatures" that allows to turn many secure group chat protocols into deniable ones. Intuitively, the transform works for protocols that use signatures for authentication and that become deniable if the signatures are removed. In contrast to previous proposals that use signatures for entity authentication, like mpOTR (CCS’09), our construction does not require pairwise key establishment of participants and allows to add and remove participants without requiring to re-initialize the chat. These properties allow the deployment in protocols that are also designed to scale to very large groups. Finally, we construct a practical epochal signature scheme from generic primitives and prove it secure.

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.

In this paper we present use cases for affective user interfaces (UIs) in cars and how they are perceived by potential users in China and Germany. Emotion-aware interaction is enabled by the improvement of ubiquitous sensing methods and provides potential benefits for both traffic safety and personal well-being. To promote the adoption of affective interaction at an international scale, we developed 20 mobile in-car use cases through an inter-cultural design approach and evaluated them with 65 drivers in Germany and China. Our data shows perceived benefits in specific areas of pragmatic quality as well as cultural differences, especially for socially interactive use cases. We also discuss general implications for future affective automotive UI. Our results provide a perspective on cultural peculiarities and a concrete starting point for practitioners and researchers working on emotion-aware interfaces.

Applications of mobile ground robots demand high speed and agility while navigating in complex indoor environments. These present an ongoing challenge in mobile robotics. A system with these specifications would be of great use for a wide range of indoor inspection tasks. This paper introduces Ascento, a compact wheeled bipedal robot that is able to move quickly on flat terrain, and to overcome obstacles by jumping. The mechanical design and overall architecture of the system is presented, as well as the development of various controllers for different scenarios. A series of experiments1with the final prototype system validate these behaviors in realistic scenarios.

Motivation: The presence of terraces in phylogenetic tree space, i.e. a potentially large number of distinct tree topologies that have exactly the same analytical likelihood score, was first described by Sanderson et al. However, popular software tools for maximum likelihood and Bayesian phylogenetic inference do not yet routinely report, if inferred phylogenies reside on a terrace, or not. We believe, this is due to the lack of an efficient library to (i) determine if a tree resides on a terrace, (ii) calculate how many trees reside on a terrace and (iii) enumerate all trees on a terrace. Results: In our bioinformatics practical that is set up as a programming contest we developed two efficient and independent C++ implementations of the SUPERB algorithm by Constantinescu and Sankoff (1995) for counting and enumerating trees on a terrace. Both implementations yield exactly the same results, are more than one order of magnitude faster, and require one order of magnitude less memory than a previous thirrd party python implementation. Availability and implementation: The source codes are available under GNU GPL at https://github.com/terraphast. Supplementary information: Supplementary data are available at Bioinformatics online.