Graphical user interface redefinition addressing users’ diversity

Improvements can still be made in the development of Interactive Computing Systems (ICSs) aiming to ease their use. This is particularly true when trying to address users’ diversity. Most ICSs do not adjust themselves to the user nor consider user’s particularities. However, some provide solutions to address better specificities of expert and novice users. Others adjust themselves based on user’s interaction history, but this does not always lead to improvements in use. An aspect that prevents to address users’ diversity broadly is the fact that most of existing ICSs do not provide source code access. This means that only owners can introduce improvements on them. This paper proposes an approach (based on both affective computing and computer vision) to broadly improve design for diversity (without source code access) for both existing and to be developed ICSs. The results are twofold: (i) example of an initial set of design guidelines; (ii) opens the way to runtime Graphical User Interface (GUI) redefinition and adjustment based on both user’s features and emotions reducing therefore designers’ restrictions when addressing users’ diversity.info:eu-repo/semantics/acceptedVersio

Towards graphical user interface redefinition without source code access: System design and evaluation

Nowadays several interactive computing systems (ICSs) still have Graphical User Interfaces (GUIs) that are inadequate in terms of usability and user experience. Numerous improvements were made in the development of better GUIs however, little has been done to improve existing ones. This might be explained by the fact that most ICSs do not provide source code access. In most cases, this means that only persons with source code access can (easily) enhance the respective GUI. This paper presents a tool using computer vision (CV) algorithms to semi-automatically redefine existing GUIs without accessing their source code. The evaluation of a new GUI obtained from the redefinition of an existing GUI using the tool is described. Results show statistically significant improvements in usability (reduction of interaction mistakes), improved task completion success rate and improved user satisfaction.info:eu-repo/semantics/acceptedVersio

Comprehensive data infrastructure for plant bioinformatics

The iPlant Collaborative is a 5-year, National Science Foundation-funded effort to develop cyberinfrastructure to address a series of grand challenges in plant science. The second of these grand challenges is the Genotype-to- Phenotype project, which seeks to provide tools, in the form of a web-based Discovery Environment, for understanding the developmental process from DNA to a full-grown plant. Addressing this challenge requires the integration of multiple data types that may be stored in multiple formats, with varying levels of standardization. Providing for reproducibility requires that detailed information documenting the experimental provenance of data, and the computational transformations applied to data once it is brought into the iPlant environment. Handling the large quantities of data involved in high-throughput sequencing and other experimental sources of bioinformatics data requires a robust infrastructure for storing and reusing large data objects. We describe the currently planned workflows to be developed for the Genotype-to-Phenotype discovery environment, the data types and formats that must be imported and manipulated within the environment, and we describe the data model that has been developed to express and exchange data within the Discovery Environment, along with the provenance model defined for capturing experimental source and digital transformation descriptions. Capabilities for interaction with reference databases are addressed, focusing not just on the ability to retrieve data from such data sources, but on the ability to use the iPlant Discovery Environment to further populate these important resources. Future activities and the challenges they will present to the data infrastructure of the iPlant Collaborative are also described. © 2010 IEEE

Integrating online learning into workplace information systems – Supporting the goal of lifelong learning

The authors have a significant history of research in online learning, and in recent years have published their work at the LACCEI conference. This paper focuses on the issues caused by the major skills gaps in STEM industries identified in the popular press, and confirmed through empirical research, and the changes to the workforce occasioned by the growing tide of automation. Both of these issues have a similar result on the STEM workforce, in the need to upskill and reskill staff to be productive in the changed landscape brought about by these issues and the accelerating pace of change through technological development. Fundamentally, they argue that there is a need to change the existing model of workplace learning to integrate learning as an integral component of the workplace, rather than an activity that takes place separate from that workplace, often in a separate location. From their own previous research they have encountered both worker and employer resistance to such integration, but they argue the need for cultural and technological change, to enable greater efficiency and availability of appropriate learning resources directly related to workplace activities. By integrating learning environments and resources with the primary information systems in the workplace, and enabling ""drill-down"" capability to link specific working issues with relevant learning resources, staff efficiency can be improved, better solutions can be developed, and acquisition of skills and knowledge can be improved. The authors provide an example of a working solution, in the tourism domain, from the Tourism Monitor project, which provides both an exemplar and some practical lessons that are relevant to the STEM industries. The Tourism Monitor dashboard provides statistical information collected by the Norwegian government on a number of key tourism datasets, providing information to support the management of tourism services and service organisations. By linking it to an instance of the Sakai VLE, and through that to learning materials developed by the project partners and the local University College, staff development can run alongside the creation and management of tourism services. The authors suggest that a similar model could be used for workplace learning in STEM industries, referencing their own work on developing online learning for collaborative heavyweight processes, such as dCCD, as reported at LACCEI last year. The model developed is novel and built from a considerable body of prior research, and provides an answer to the research question “How could the model of workforce education be changed to ensure the supply of IT and engineering skills in the workforce keeps up with demand?

Newsbots That Mediate Journalist and Audience Relationships

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. News media organisations are experimenting with a new generation of newsbots that move beyond automated headline delivery to the delivery of news according to a conversational format within the context of private messaging services. To build the newsbot, journalists craft statements and answers to users’ questions that mimic a natural conversation between a journalist and user. In so doing, journalists are experimenting with styles of communication that reflect very particular journalistic personas. We investigate the persona of the news chatbot created by the Australian Broadcasting Corporation (ABC), the better to understand how the public broadcaster’s forays into social media service delivery and automation are shaping new relationships between public service broadcasters and their audiences. We find that, for a section of the audience that uses it, the friendly newsbot contrasts favourably with their previous experience with news and the journalists who produce it. The public service journalists who operate the bot are, in turn, using the bot to try to reach new audiences by experimenting with a more informal, intimate relationship with citizen users. The supposedly “intelligent” (but in actual fact very much human-crafted) newsbot is the vehicle through which this new relationship is being forged

Constraint-handling techniques for generative product design systems in the mass customization context

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a timeconsuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms

NASA RECON: Course Development, Administration, and Evaluation

The R and D activities addressing the development, administration, and evaluation of a set of transportable, college-level courses to educate science and engineering students in the effective use of automated scientific and technical information storage and retrieval systems, and, in particular, in the use of the NASA RECON system, are discussed. The long-range scope and objectives of these contracted activities are overviewed and the progress which has been made toward these objectives during FY 1983-1984 is highlighted. In addition, the results of a survey of 237 colleges and universities addressing course needs are presented

A methodology for adaptable and robust ecosystem services assessment

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant one model fits all paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts

Computers for learning : an empirical modelling perspective

In this thesis, we explore the extent to which computers can provide support for domain learning. Computer support for domain learning is prominent in two main areas: in education, through model building and the use of educational software; and in the workplace, where models such as spreadsheets and prototypes are constructed. We shall argue that computerbased learning has only realised a fraction of its full potential due to the limited scope for combining domain learning with conventional computer programming. In this thesis, we identify some of the limitations in the current support that computers offer for learning, and propose Empirical Modelling (EM) as a way of overcoming them. We shall argue that, if computers are to be successfully used for learning, they must support the widest possible range of learning activities. We introduce an Experiential Framework for Learning (EFL) within which to characterise learning activities that range from the private to the public, from the empirical to the theoretical, and from the concrete to the abstract. The term ‘experiential’ reflects a view of knowledge as rooted in personal experience. We discuss the merits of computer-based modelling methods with reference to a broad constructionist perspective on learning that encompasses bricolage and situated learning. We conclude that traditional programming practice is not well-suited to supporting bricolage and situated learning since the principles of program development inhibit the essential cognitive model building activity that informs domain learning. In contrast, the EM approach to model construction directly targets the semantic relation between the computer model and its domain referent and exploits principles that are closely related to the modeller’s emerging understanding or construal. In this way, EM serves as a uniform modelling approach to support and integrate learning activities across the entire spectrum of the EFL. This quality makes EM a particularly suitable approach for computer-based model construction to support domain learning. In the concluding chapters of the thesis, we demonstrate the qualities of EM for educational technology with reference to practical case studies. These include: a range of EM models that have advantages over conventional educational software due to their particularly open-ended and adaptable nature and that serve to illustrate a variety of ways in which learning activities across the EFL can be supported and scaffolded