Design for the Right to the Smart City in More-than-Human Worlds

Environmental concerns have driven an interest in sustainable smart cities, through the monitoring and optimisation of networked infrastructure processes. At the same time, there are concerns about who these interventions and services are for, and who benefits. HCI researchers and designers interested in civic life have started to call for the democratisation of urban space through resistance and political action to challenge state and corporate claims. This paper aims to add to the growing body of critical and civic led smart city literature in HCI by leveraging concepts from the environmental humanities about more than human worlds, as a way to shift understandings within HCI of smart cities away from the exceptional and human centered, towards a more inclusive understanding that incorporates and designs for other others and other species. We illustrate through a case study that involved codesigning Internet of Things with urban agricultural communities, possibilities for creating more environmentally and socially just smart cities

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

The world is confronted with the grand challenge of food insecurity amidst growing populations and the climate crisis. Artificial intelligence (AI) deployed in agricultural decision support systems (AgriDSS) raises both hopes and concerns for increasing agricultural productivity in sustainable ways. In this paper, we conduct a scoping review to uncover the roadblocks to the use of AI-supported AgriDSS in sustainable agriculture. Based on the corpus of 121 articles, we find that the effective use of AI-supported AgriDSS is hindered at technical, social, ethical, and ecological levels. Then, drawing on the experiential learning perspective, we propose how conjoint experiential learning (CEL) can enhance sustainable agricultural practices by enhancing both AI and human learning and overcoming roadblocks in using AgriDSS. Based on this conceptual framework, we build a research agenda that suggests blind spots and possible directions for future research

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

Reusable and Sharable Learning Objects Supporting Students’ Learning of Data Structures in University Courses

Data structures are a conceptually demanding topic which confronts many computer science students early in their course. The topic has a strong conceptual basis and often proves difficult for many to grasp. This paper reports on a project which has developed a range of learning objects to help students learn about the different data structures and the algorithms by which they are controlled. Called VIDSAA, the suite of learning objects provides a visual representation which enables students to observe and interact with a large number of data structure algorithms as they are run and to observe and view the outcomes. The objects have been designed to enable students to explore and investigate the data structures as a means of developing their knowledge and understanding. The paper describes the design and development strategies that underpinned the development of the learning objects and showcases the resulting products. It discusses a project to explore how teachers and students might use the objects and the support they provide for learning

Reusable and Sharable Learning Objects Supporting Students’ Learning of Data Structures in University Courses

Data structures are a conceptually demanding topic which confronts many computer science students early in their course. The topic has a strong conceptual basis and often proves difficult for many to grasp. This paper reports on a project which has developed a range of learning objects to help students learn about the different data structures and the algorithms by which they are controlled. Called VIDSAA, the suite of learning objects provides a visual representation which enables students to observe and interact with a large number of data structure algorithms as they are run and to observe and view the outcomes. The objects have been designed to enable students to explore and investigate the data structures as a means of developing their knowledge and understanding. The paper describes the design and development strategies that underpinned the development of the learning objects and showcases the resulting products. It discusses a project to explore how teachers and students might use the objects and the support they provide for learning