A Novel Computational Model for Social Isolation Detection in Social Networks

The human being is a social creature and needs to communicate with others to share information, emotions, and fulfill its basic needs. Social isolation can be considered as a serious health risk issue which not only has unignorably negative impacts on the well-being and quality of life of individuals, but also it is harmful to healthy human development. In this research, a computational model and a couple of novel algorithms are proposed to address social isolation detection in social networks. In our model, a given community is represented by a weighted-directed social graph. An algorithm, SBSID (Structure-based Social Isolation Detection), is proposed to detect socially isolated individuals based on the graph\u27s structure by finding the number of each individual\u27s active friends and their influence on each other. On the other hand, each individual\u27s demographic characteristics in our model are represented by a set of binary attributes. Consequently, another algorithm is proposed, FBSID (Feature-based Social Isolation Detection), to address social isolation based on the nodes\u27 features in the social graph.We propose a couple of metrics and formulas to calculate society\u27s norms based on the overall structure and attributes of the social graph. Structural characteristics and attributes of each individual are compared with the norm of society to identify socially isolated individuals. We have evaluated the performance of our proposed model and algorithms on a set of synthetic networks. The results show that our model is capable of finding socially isolated nodes in various sizes of graphs with high accuracy and efficiency

Tracing the evolution of teachers' mathematical knowledge and pedagogy through programming: Learning from Scratch

This thesis is based on research to explore the role of primary school teachers’ mathematical and pedagogical knowledge in their engagement with computer-based microworlds that formed part of ScratchMaths (SM). SM is a two-year mathematics and computing curriculum designed for pupils aged nine to eleven years old. The aims of the research were to trace the evolution of teachers’ mathematical knowledge, as they taught SM microworlds designed for exploration and reasoning about place value, variable and angle through computer programming. The study adopted a multiple-case study approach with augmenting teacher episodes situated in the English primary school setting. The thirteen Year 6 teachers of the study were selected from national participants of the second year of the two-year SM intervention. Data collection involved video-recorded classroom observations, audio-recorded post-lesson semi-structured teacher interviews, and ‘think aloud’ while engaging with computer-based tasks. The conceptual framework for the thesis incorporated the Mathematical Pedagogical Technology Knowledge (MPTK) framework and the Instrumental Orchestration model. The findings reveal the knowledge required to teach at the intersection of programming and mathematics, and crucially, how the ideas mediate and are mediated by engagement with the SM curriculum. The findings also illustrate how teaching mathematics through computer programming requires the teacher to bridge between the computing and the mathematics domains and how some teachers managed to do this while creating new connections within and between the knowledge domains. The study contributes to the literature of teachers’ mathematical knowledge of place value, variable, and angle as well as teachers’ ability to (re-) express mathematics through computer programming. The thesis makes an original contribution to the literature with the specification of a theoretical model for analysing teachers’ knowledge for teaching mathematics through programming in the primary setting

INSAM Journal of Contemporary Music, Art and Technology 9 (II/2022)

The Editorial Board of the INSAM Journal of Contemporary Music, Art and Technology decided that both issues of 2022 will be dedicated to one main theme, namely, “Fighting for the attention: Music and art on social media”. We can say that this call for papers went very successfully, as we are now presenting to you INSAM Journal No. 9. In a year which has seen many grave turbulences on socio-economic and political levels on a global scale, we have once again confirmed the importance of social media for communication and the spreading of news, and we have also seen the limitations of these tools. Turning to music and art on social media, our Main Theme section consists of five intriguing papers, Beyond the Main Theme section has two articles, (Inter)Views bring three exciting pieces, and the Reviews one festival report

Compositions Utilizing Fractal Flame Algorithms

“Music, by its very abstract nature, is the first of the arts to have attempted reconciliation of artistic creation with scientific thought” – Xenakis, 1992 This portfolio explores how the iterative and recursive processes employed within fractal flame algorithms can be used to create new and aesthetically pleasing micro and macro sounds from which coherent compositions can be created. A variety of existing electronic compositional procedures, including wave-set substitution and granular synthesis, as well as a number of classical compositional practices, such as hocketing, are deployed to generate a complex and diverse set of compositions. The portfolio shows how marrying these sound manipulating techniques and compositional processes with the sonic events produced by the unexplored field of fractal flame algorithms has allowed me to generate – in the words of Iannis Xenakis – ‘sounds that have never existed before’. The portfolio shows the creative potential fractal flame programs have for electronic music generation and how they offer a terra nova (new earth) upon which computergenerated music can lay down solid foundations and expand in new directions to harvest exciting results

Pragmatic enrichment in language processing and development

The goal of language comprehension for humans is not just to decode the semantic content of sentences, but rather to grasp what speakers intend to communicate. To infer speaker meaning, listeners must at minimum assess whether and how the literal meaning of an utterance addresses a question under discussion in the conversation. In cases of implicature, where the speaker intends to communicate more than just the literal meaning, listeners must access additional relevant information in order to understand the intended contribution of the utterance. I argue that the primary challenge for inferring speaker meaning is in identifying and accessing this relevant contextual information. In this dissertation, I integrate evidence from several different types of implicature to argue that both adults and children are able to execute complex pragmatic inferences relatively efficiently, but encounter some difficulty finding what is relevant in context. I argue that the variability observed in processing costs associated with adults' computation of scalar implicatures can be better understood by examining how the critical contextual information is presented in the discourse context. I show that children's oft-cited hyper-literal interpretation style is limited to scalar quantifiers. Even 3-year-olds are adept at understanding indirect requests and "parenthetical" readings of belief reports. Their ability to infer speaker meanings is limited only by their relative inexperience in conversation and lack of world knowledge

2019 Academic Excellence Showcase Abstracts

Abstracts for the 2019 Academic Excellence Showcase

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

Studies in ambient intelligent lighting

The revolution in lighting we are arguably experiencing is led by technical developments in the area of solid state lighting technology. The improved lifetime, efficiency and environmentally friendly raw materials make LEDs the main contender for the light source of the future. The core of the change is, however, not in the basic technology, but in the way users interact with it and the way the quality of the produced effect on the environment is judged. With the new found freedom the users can switch their focus from the confines of the technology to the expression of their needs, regardless of the details of the lighting system. Identifying the user needs, creating an effective language to communicate them to the system, and translating them to control signals that fulfill them, as well as defining the means to measure the quality of the produced result are the topic of study of a new multidisciplinary area of study, Ambient Intelligent Lighting. This thesis describes a series of studies in the field of Ambient Intelligent Lighting, divided in two parts. The first part of the thesis demonstrates how, by adopting a user centric design philosophy, the traditional control paradigms can be superseded by novel, so-called effect driven controls. Chapter 3 describes an algorithm that, using statistical methods and image processing, generates a set of colors based on a term or set of terms. The algorithm uses Internet image search engines (Google Images, Flickr) to acquire a set of images that represent a term and subsequently extracts representative colors from the set. Additionally, an estimate of the quality of the extracted set of colors is computed. Based on the algorithm, a system that automatically enriches music with lyrics based images and lighting was built and is described. Chapter 4 proposes a novel effect driven control algorithm, enabling users easy, natural and system agnostic means to create a spatial light distribution. By using an emerging technology, visible light communication, and an intuitive effect definition, a real time interactive light design system was developed. Usability studies on a virtual prototype of the system demonstrated the perceived ease of use and increased efficiency of an effect driven approach. In chapter 5, using stochastic models, natural temporal light transitions are modeled and reproduced. Based on an example video of a natural light effect, a Markov model of the transitions between colors of a single light source representing the effect is learned. The model is a compact, easy to reproduce, and as the user studies show, recognizable representation of the original light effect. The second part of the thesis studies the perceived quality of one of the unique capabilities of LEDs, chromatic temporal transitions. Using psychophysical methods, existing spatial models of human color vision were found to be unsuitable for predicting the visibility of temporal artifacts caused by the digital controls. The chapters in this part demonstrate new perceptual effects and make the first steps towards building a temporal model of human color vision. In chapter 6 the perception of smoothness of digital light transitions is studied. The studies presented demonstrate the dependence of the visibility of digital steps in a temporal transition on the frequency of change, chromaticity, intensity and direction of change of the transition. Furthermore, a clear link between the visibility of digital steps and flicker visibility is demonstrated. Finally, a new, exponential law for the dependence of the threshold speed of smooth transitions on the changing frequency is hypothesized and proven in subsequent experiments. Chapter 7 studies the discrimination and preference of different color transitions between two colors. Due to memory effects, the discrimination threshold for complete transitions was shown to be larger than the discrimination threshold for two single colors. Two linear transitions in different color spaces were shown to be significantly preferred over a set of other, curved, transitions. Chapter 8 studies chromatic and achromatic flicker visibility in the periphery. A complex change of both the absolute visibility thresholds for different frequencies, as well as the critical flicker frequency is observed. Finally, an increase in the absolute visibility thresholds caused by an addition of a mental task in central vision is demonstrated