Modelling Clustering of Wireless Sensor Networks with Synchronised Hyperedge Replacement

This paper proposes Synchronised Hyperedge Replacement (SHR) as a suitable modelling framework for Wireless Sensor Networks (WSNs). SHR facilitates explicit modelling of WSNs applications environmental conditions (that significantly affect applications performance) while providing a sufficiently high level of abstraction for the specification of the underling coordination mechanisms. Because it is an intractable problem to solve in distributed manner, and distribution is important, we propose a new Nutrient-flow-based Distributed Clustering (NDC) algorithm to be used as a working example. The key contribution of this work is to demonstrate that SHR is sufficiently expressive to describe WSNs algorithms and their behaviour at a suitable level of abstraction to allow onward analysis

A distributed data extraction and visualisation service for wireless sensor networks

With the increase in applications of wireless sensor networks, data extraction and visualisation have become a key issue to develop and operate these networks. Wireless sensor networks typically gather data at a discrete number of locations. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. The aim of this thesis is to develop a service for maximising information return from large scale wireless sensor networks. This aim will be achieved through the development of a distributed information extraction and visualisation service called the mapping service. In the distributed mapping service, groups of network nodes cooperate to produce local maps which are cached and merged at a sink node, producing a map of the global network. Such a service would greatly simplify the production of higher-level information-rich representations suitable for informing other network services and the delivery of field information visualisations. The proposed distributed mapping service utilises a blend of both inductive and deductive models to successfully map sense data and the universal physical principles. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of a sense modality. Furthermore, the proposed mapping service responds to changes in the environmental conditions that may impact the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a newdistributed self-adaptation algorithm, Virtual Congress Algorithm,which is based on the concept of virtual congress is proposed, with the goal of saving more power and generating more accurate data visualisation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

The Origin of Data: Enabling the Determination of Provenance in Multi-institutional Scientific Systems through the Documentation of Processes

The Oxford English Dictionary defines provenance as (i) the fact of coming from some particular source or quarter; origin, derivation. (ii) the history or pedigree of a work of art, manuscript, rare book, etc.; concr., a record of the ultimate derivation and passage of an item through its various owners. In art, knowing the provenance of an artwork lends weight and authority to it while providing a context for curators and the public to understand and appreciate the work’s value. Without such a documented history, the work may be misunderstood, unappreciated, or undervalued. In computer systems, knowing the provenance of digital objects would provide them with greater weight, authority, and context just as it does for works of art. Specifically, if the provenance of digital objects could be determined, then users could understand how documents were produced, how simulation results were generated, and why decisions were made. Provenance is of particular importance in science, where experimental results are reused, reproduced, and verified. However, science is increasingly being done through large-scale collaborations that span multiple institutions, which makes the problem of determining the provenance of scientific results significantly harder. Current approaches to this problem are not designed specifically for multi-institutional scientific systems and their evolution towards greater dynamic and peer-to-peer topologies. Therefore, this thesis advocates a new approach, namely, that through the autonomous creation, scalable recording, and principled organisation of documentation of systems’ processes, the determination of the provenance of results produced by complex multi-institutional scientific systems is enabled. The dissertation makes four contributions to the state of the art. First is the idea that provenance is a query performed over documentation of a system’s past process. Thus, the problem is one of how to collect and collate documentation from multiple distributed sources and organise it in a manner that enables the provenance of a digital object to be determined. Second is an open, generic, shared, principled data model for documentation of processes, which enables its collation so that it provides high-quality evidence that a system’s processes occurred. Once documentation has been created, it is recorded into specialised repositories called provenance stores using a formally specified protocol, which ensures documentation has high-quality characteristics. Furthermore, patterns and techniques are given to permit the distributed deployment of provenance stores. The protocol and patterns are the third contribution. The fourth contribution is a characterisation of the use of documentation of process to answer questions related to the provenance of digital objects and the impact recording has on application performance. Specifically, in the context of a bioinformatics case study, it is shown that six different provenance use cases are answered given an overhead of 13% on experiment run-time. Beyond the case study, the solution has been applied to other applications including fault tolerance in service-oriented systems, aerospace engineering, and organ transplant management

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Proceedings of EVA London 2024

The Electronic Visualisation and the Arts London 2024 Conference (EVA London 2024) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. As for 2022, the EVA London 2023 Conference is a physical and online “hybrid” conference. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, in full colour. The main conference presentations run during 10–13 July 2023, with workshops and other activities, especially for students, on 14 July 2023