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In-vehicle network monitoring is one of the important elements in vehicular network management and security. Most of the existing network monitoring approaches rely on measuring every part of the network. Such approaches overburden the network by transmitting active probes. In this work, we propose a new in-vehicle network monitoring approach that benefits from network tomography and the advances in deep learning to infer the network delay performance. Specifically, the available measurements can be used to estimate the performance of the remaining network where direct measurements cannot be applied. Performance evaluation has been conducted using in-vehicle network simulation with different TSN (Time-Sensitive Network) traffics and the proposed monitoring approach shows the delay estimation accuracy of up to 99%.

The Software-Defined Networking (SDN) technology is a network management method that allows dynamic, programmatically efficient network planning to improve its performance and monitoring. Given that SDN at each passing day becomes more prominent, a framework that can ensure reliable communication and a global state among devices become more important. We propose a state consistency framework that leverages a state machine abstraction using network updates among adjacent switches through packet piggybacking. We also use a moving average that when a condition is met, P4's packet cloning is triggered, hence ensuring that all packets arrive at their destination in the presence of link failures.

Addressing the needs of visually impaired people is of continued interest in Human Computer Interaction (HCI) research. Yet, one of the major challenges facing researchers in this field continues to be how to design adequate quantitative empirical evaluation for these users in HCI. In this paper, we analyse a corpus of 178 papers on technologies designed for people with visual impairments, published since 1988, and including at least one quantitative empirical evaluation (243 evaluations in total). To inform future research in this area, we provide\ud an overview, historic trends and a unified terminology to design and report quantitative empirical evaluations. We identify open issues and propose a set of guidelines to address them. Our analysis aims to facilitate and stimulate future research on this topic.

An anthology of critical writing, creative writing, historic images and commissioned artwork on the subject of colour and colour theory.

We have proposed a preliminary design and computation results of a 20 by 20 elements reconfigurable liquid crystal (LC) based binary phase reflectarray metasurface for applications in THz wireless communication. Specifically, we performed full-wave simulations and theoretical calculations on the far-field patterns of our proposed device, and we also developed optimisation routines for both the antenna unit cell design as well as far-field pattern synthesis algorithms. The LC used was modelled after Merk's model GT3-23001. At optimal operation frequency of 108 GHz, the phase difference between ON and OFF state is 177 degrees, and the reflection amplitudes of both states are 0.88. We were able to demonstrate 60 degrees of beam steering, which was achieved together with a (single beam) RCS of -5.8 dBm2, when the device dimension is only 20mm by 20mm. The operational bandwidth is plus or minus 2 GHz from central frequency of 108 GHz.

The exploitation of syntactic graphs (SyGs) as a word's context has been shown to be beneficial for distributional semantic models (DSMs), both at the level of individual word representations and in deriving phrasal representations via composition. However, notwithstanding the potential performance benefit, the syntactically-aware DSMs proposed to date have huge numbers of parameters (compared to conventional DSMs) and suffer from data sparsity. Furthermore, the encoding of the SyG links (i.e., the syntactic relations) has been largely limited to linear maps. The knowledge graphs' literature, on the other hand, has proposed light-weight models employing different geometric transformations (GTs) to encode edges in a knowledge graph (KG). Our work explores the possibility of adopting this family of models to encode SyGs. Furthermore, we investigate which GT better encodes syntactic relations, so that these representations can be used to enhance phrase-level composition via syntactic contextualisation.

Mathematics and computing students learn new concepts and fortify their expertise by solving problems. The representation of a problem, be it through algebra, diagrams, or code, is key to understanding and solving it. Multiple-representation interactive environments are a promising approach, but the task of choosing an appropriate representation is largely placed on the user. We propose a new method to recommend representations based on correspondences: conceptual links between domains. Correspondences can be used to analyse, identify, and construct analogies even when the analogical target is unknown. This paper explains how correspondences build on probability theory and Gentner's structure-mapping framework; proposes rules for semi-automated correspondence discovery; and describes how correspondences can explain and construct analogies.

The assumption that action and perception can be investigated independently is entrenched in theories, models and experimental approaches across the brain and mind sciences. In cognitive science, this has been a central point of contention between computationalist and 4Es (enactive, embodied, extended and embedded) theories of cognition, with the former embracing the “classical sandwich”, modular, architecture of the mind and the latter actively denying this separation can be made. In this work we suggest that the modular independence of action and perception strongly resonates with the separation principle of control theory and furthermore that this principle provides formal criteria within which to evaluate the implications of the modularity of action and perception. We will also see that real-time feedback with the environment, often considered necessary for the definition of 4Es ideas, is not however a sufficient condition to avoid the “classical sandwich”. Finally, we argue that an emerging framework in the cognitive and brain sciences, active inference, extends ideas derived from control theory to the study of biological systems while disposing of the separation principle, describing non-modular models of behaviour strongly aligned with 4Es theories of cognition.