Goal-oriented design of value and process models from patterns

This thesis defines a design framework and a method for modelling networked businesses. The intended application domain is electronic businesses that extensively use information and communication technology to coordinate work. The key property of the proposed approach is the reuse of design knowledge in the form of design patterns. Design patterns are extracted from models of existing electronic intermediaries considered successful. These businesses have been reverse-engineered to two types of models: economic value exchange models and business process models. The identified patterns comprise two libraries of value exchange and business process patterns, respectively. Patterns are catalogued with, among others, their context, solved problem, and proposed solution. Most importantly, they are annotated with a machine-readable\ud capability model used as a search key in the library. Capability models are part of the goal-modelling technique for business requirements proposed here. Our goal-modelling technique operationalizes each business goal with a variable and an evaluation function: the evaluation function determines when a measured variable value satisfies the goal. A goal model represents requirements if goals are assigned evaluation functions but the variable values are unknown. In such a case, the goal model specifies what is desired to happen. If, on the other hand, variable values are known, the goal model documents the capabilities of a pattern. The proposed design framework structures the development process into: (1) available design knowledge in libraries of value and process patterns, (2) business requirements captured in a goal model, and (3) economic value and business process perspectives to look at a business system. The design method prescribes steps to transform patterns and requirements into a system specification. These include: (i) identification of relevant pattern based on matching capability and requirements goal models; (ii) synthesis of value and process patterns into value and process models, respectively; and (iii) consistency check procedure for value and process model.\ud The usefulness of the approach is demonstrated in a real-life example, which shows that the framework and method exhibit a predefined set of desired properties

Pattern Research Project: An Investigation of The Pattern And Printing Process - Layers Vineyard

2017 Pattern Research Project Jasmine Zheng - Layers Vineyard The Pattern Research Project involves research and analysis of contemporary patterns found in the textiles and wallcoverings of the built interior environment. Patterns use motif, repetition, color, geometry, craft, technology, and space to communicate place, time, and concept. Through this research and analysis, built environments - their designers, occupants, construction, and context - can be better understood. Jasmine Zheng, VCU Interior Design BFA 2020, selected the “Layers Vineyard” pattern by Hella Jongerius for Maraham for the 2017 Pattern Research Project. The text below is excerpted from the student’s work: “This was made by Hella Jongerius for a company called Maraham. They reached out to her searching to create an upholstery textile. They wanted something that would flow seamlessly into each other for several meters of repeat. The pattern that I picked was one of three different patterns in the series called Layers. For this textile, Hella Jongerius and Maharam Design Studio came up with a way to bind multiple layers of wool felt with embroidery. The methods that she used were based off research that she conducted on the basis of needle-punched felt panels that she made as a guest curator at the Cooper-Hewitt, National Design Museum in 2005, and as an installation at Villa Noailles, the Robert Mallet-Stevens house in Hyeres, France.”https://scholarscompass.vcu.edu/prp/1010/thumbnail.jp

Innovation and design change strategies for learning technologies at Warwick : towards a ‘design capabilities’ heuristic for guiding practice and evaluating change.

This report gives a narrative account of an investigation into design and design capability in teaching and learning in a research-intensive university. It begins, in the Introduction, with definitions of key concepts: design, designing, successful design (achieving fit, stick, spread and growth), design change and design capability (although this last term is only really fleshed­out in Reading the Case Studies and the Conclusion). These words are common currency, but rarely used with precision. When clearly defined they provide a lens through which we can attain more clarity and granularity in analysing attempts at enhancing practice. In the second part, on the Origins of the Investigation and Earlier Experiments, we examine the limitations of a techno­centric approach to understanding, predicting and supporting the uptake of technology enhanced learning. It is argued that a design capability approach is needed, in which the ability of all people (including students) to discover, create, adopt, adapt designs that fit, stick, spread and grow is of prime value. In part 3, the design of the investigation is explained, with its focus upon discovering, creating and using design patterns as a key facilitating aspect of design capability. In part 4, this is put to the test, with an attempt at creatively reading the 23 mini case studies produced in interviews with academics. However, design patterns do not emerge easily from the cases, and we see that design and designing in this setting is more diverse and complex than expected. It is argued that a design patterns based approach will be useful, but much more work needs to be done before design patterns can become the lingua franca of teaching and learning design and development. This leads to a more sophisticated view of design capability, presented in the Conclusion. Drawing upon the experiences of the academics interviewed in the case studies, especially experienced and confident senior academics, it is conjectured that we need to increase the intensity with which academics encounter and reflect upon design challenges, designerly approaches, suboptimal and successful designs and design patterns. An integrated combination of Design Thinking and the Higher Education Academy Fellowship framework is recommended as a way of achieving this

Accessible Spectrum Analyser

Presented at the 22nd International Conference on Auditory Display (ICAD-2016)This paper presents the Accessible Spectrum Analyser (ASA) developed as part of the DePic project (Design Patterns for Inclusive collaboration) at Queen Mary University of London. The ASA uses sonification to provide an accessible representation of frequency spectra to visually impaired audio engineers. The software is free and open source and is distributed as a VST plug-in under OSX and Windows. The aim of reporting this work at the ICAD 2016 conference is to solicit feedback about the design of the present tool and its more generalized counterpart, as well as to invite ideas for other possible applications where it is thought that auditory spectral analysis may be useful, for example in situations where line of sight is not always possible

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201