Urban Emotions – Tools of Integrating People’s Perception into Urban Planning

This paper introduces the research field “Urban Emotions” – an interdisciplinary approach combining not only spatial planning and (geo-) informatics, but also computer linguistics and sensor technology methods. A new set of methods will be formed for the area of urban and spatial planning, resulting in a fundamental change of the understanding of planning. One of the main objectives is the involvement of citizens into planning processes. Therefore, new techniques are developed to collect and analyse data on the emotional perception of space and provide it to the people and also planners. Not only the human perception in the context of the city, and the combination with human sensory processes are contents of this paper, but also the critical discussion of these effects to privacy issues. Based on the topics “mental maps” and psychogeography in combination with the field of digital emotional urban tagging, the potential of integrating objectively quantified emotions in the context of citizen participation will be explained. In the following, partly established and partly experimental methods for collecting and analysing “Urban Emotions” will be introduced. Based on two studies, the possibilities of transfering these methodsinto the planning praxis will be shown on the one hand and on the other hand the potential for further development for other disciplines will be more evident

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

Clustering Methods for Network Data Analysis in Programming

In the modern world, data volumes are constantly increasing, and clustering has become an essential tool for identifying patterns and regularities in large datasets. The relevance of this study is associated with the growing need for effective data analysis methods in programming. The objective is to evaluate different clustering techniques within the programming domain and explore their suitability for analysing a wide range of datasets. Inductive and deductive methodologies, concrete illustrations, and visual techniques were employed. The clustering techniques were implemented using RStudio and Matlab tools. The study's findings facilitated the identification of crucial attributes of clustering techniques, including hierarchical structure, cluster quantity, and similarity metrics. The application of several data analysis and visualisation approaches, including k-means, c-means, hierarchical, least spanning tree, and linked component extraction, was illustrated. This study elucidated the clustering approaches that may be optimally employed in various contexts, resulting in enhanced precision in analyses and data-informed decision-making. The study's practical significance is in enhancing programmers' methodological toolset with tools for data analysis and processing

The role of motivation in regulating the extent to which data visualisation literacy influences business intelligence and analytics use in organisations

Dissertation (MCom (Informatics))--University of Pretoria 2022.The ability to read and interpret visualised data is a critical skill to have in this information age where business intelligence and analytics (BI&A) systems are increasingly used to support decision-making. Data visualisation literacy is seen as the foundation of analytics. Moreover, there is great hype about data-driven analytical culture and data democratisation, where users are encouraged to have wide access to data and fully use BI&A to reap the benefits. Motivation is a stimulant to the richer use of any information system (IS), yet literature provides a limited understanding of the evaluation of data visualisation literacy and the effect of motivation in the BI&A context. Thus, this study aims to explain the role of motivation in regulating the extent to which data visualisation literacy influences BI&A’s exploitative and explorative use in organisations. Data visualisation literacy is measured using six data visualisations that focus on the five cognitive basic intelligent analytical tasks that assess the user's ability to read and interpret visualised data. Two types of motivations are assessed using perceived enjoyment as an intrinsic motivator and perceived usefulness as an extrinsic motivator. The model is tested using quantitative data collected from 111 users, applying Structural Equation Modelling (SEM). The results indicate that intrinsic motivation exerts a positive effect on BI&A exploitative and explorative use while extrinsic motivation has a positive effect on BI&A exploitative use but weakens innovation with a negative effect on explorative use. The results further show an indirect relationship between data visualisation literacy with BI&A use through motivation. In addition, exploitation leads to creativity with exploitation positively being associated with exploration.InformaticsMCom (Informatics)Unrestricte

Supporting the externalisation of thinking in criminal intelligence analysis

At the end of the criminal intelligence analysis process there are relatively well established and understood approaches to explicit externalisation and representation of thought that include theories of argumentation, narrative and hybrid approaches that include both of these. However the focus of this paper is on the little understood area of how to support users in the process of arriving at such representations from an initial starting point where little is given. The work is based on theoretical considerations and some initial studies with end users. In focusing on process we discuss the requirements of fluidity and rigor and how to gain traction in investigations, the processes of thinking involved including abductive, deductive and inductive reasoning, how users may use thematic sorting in early stages of investigation and how tactile reasoning may be used to externalize and facilitate reasoning in a productive way. In the conclusion section we discuss the issues raised in this work and directions for future work

Supporting the externalisation of thinking in criminal intelligence analysis

At the end of the criminal intelligence analysis process there are relatively well established and understood approaches to explicit externalisation and representation of thought that include theories of argumentation, narrative and hybrid approaches that include both of these. However the focus of this paper is on the little understood area of how to support users in the process of arriving at such representations from an initial starting point where little is given. The work is based on theoretical considerations and some initial studies with end users. In focusing on process we discuss the requirements of fluidity and rigor and how to gain traction in investigations, the processes of thinking involved including abductive, deductive and inductive reasoning, how users may use thematic sorting in early stages of investigation and how tactile reasoning may be used to externalize and facilitate reasoning in a productive way. In the conclusion section we discuss the issues raised in this work and directions for future work

A framework for innovation outsourcing

This paper proposes a framework for the facilitation of organisational capability for outsourcing innovation, enabling firms to take advantage of its many benefits, (e.g., reduced costs, increased flexibility, access to better expertise and increased business focus), whilst mitigating its risks. In this framework a generic holistic model is developed to aid firms to successfully outsource innovation. The model is realised in two stages using a qualitative theory-building research design. The initial stage develops a preliminary model which is subsequently validated and refined during the second stage. The propositions which form the preliminary model are deductively explored to identify whether they also exist in a second data set. A semi-structured interview survey is executed with the aid of a rich picture survey instrument to gather data for this purpose. The model developed by this study describes innovation outsourcing as an open system of interrelated activities that takes established company strategy, (in terms of people, organisational structures, environment, and technology), and transforms it into improved firm performance through innovation. The model achieves this through a three-stage process which enables the alignment of capability to outsourced innovation activity, and makes actual performance outcomes, rather than expected benefits, the focus of innovation outsourcing aims

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

How does neoliberal performance management affect teachers’ perceived motivations to ‘improve’?

This study investigates teachers’ perceptions of the factors that motivate them to ‘improve’ or develop their practice. In the neoliberal policy context of the education system in England, performance management represents a motivational approach because of its linkage of outcomes with reward or punishment. This study contributes by evaluating the intrinsic and extrinsic motivations which might incentivise (or dis-incentivise) teachers to change their practice. The concept of performativity is considered, examining the extent to which teachers perceive they are motivated by influences such as the school inspection system, performance-related pay and numeric targets. The fraught concept of ‘improvement’ is discussed, including the potentially conflicting notions of ‘performance management’ and ‘professional development’. The emphasis of this mixed methods study is on teachers’ voices, which are often silenced or marginalised within the present neoliberal policy context. The study therefore conducts an ordinal factor analysis of a survey of qualified teachers, using self-determination theory as the underlying construct. This is further subject to Kruskal-Wallis tests for variance between groups. Qualitative data is gathered from semi-structured interviews with working teachers and examined using a hybrid inductive/deductive thematic approach to analysis. Based on a complementary synthesis and drawing on an integrative theorisation of motivation extending beyond self-determination theory, two overarching areas of teacher motivation are identified as being in tension: constitutive motivation, which includes the educational best interests of children, the pleasure of teaching and a sense of autonomous mastery, and instrumental motivation, arising from external impetuses to ‘perform’. Teachers’ constitutive motivations are found to engender authentic professional development and this has clear implications for effective school leadership