An interface group for process components

We take a process component as a pair of an interface and a behaviour. We study the composition of interacting process components in the setting of process algebra. We formalize the interfaces of interacting process components by means of an interface group. An interesting feature of the interface group is that it allows for distinguishing between expectations and promises in interfaces of process components. This distinction comes into play in case components with both client and server behaviour are involved.Comment: 26 pages; section on non-associativity of component composition added, examples adde

Working with patients and the public to design an electronic health record interface: A qualitative mixed-methods study

Background Enabling patients to be active users of their own medical records may promote the delivery of safe, efficient care across settings. Patients are rarely involved in designing digital health record systems which may make them unsuitable for patient use. We aimed to develop an evidence-based electronic health record (EHR) interface and participatory design process by involving patients and the public. Methods Participants were recruited to multi-step workshops involving individual and group design activities. A mixture of quantitative and qualitative questionnaires and observational methods were used to collect participant perspectives on interface design and feedback on the workshop design process. Results 48 recruited participants identified several design principles and components of a patient-centred electronic medical record interface. Most participants indicated that an interactive timeline would be an appropriate way to depict a medical history. Several key principles and design components, including the use of specific colours and shapes for clinical events, were identified. Participants found the workshop design process utilised to be useful, interesting, enjoyable and beneficial to their understanding of the challenges of information exchange in healthcare. Conclusion Patients and the public should be involved in EHR interface design if these systems are to be suitable for use by patient-users. Workshops, as used in this study, can provide an engaging format for patient design input. Design principles and components highlighted in this study should be considered when patient-facing EHR design interfaces are being developed

Designers' models of the human-computer interface

Understanding design models of the human-computer interface (HCI) may produce two types of benefits. First, interface development often requires input from two different types of experts: human factors specialists and software developers. Given the differences in their backgrounds and roles, human factors specialists and software developers may have different cognitive models of the HCI. Yet, they have to communicate about the interface as part of the design process. If they have different models, their interactions are likely to involve a certain amount of miscommunication. Second, the design process in general is likely to be guided by designers' cognitive models of the HCI, as well as by their knowledge of the user, tasks, and system. Designers do not start with a blank slate; rather they begin with a general model of the object they are designing. The author's approach to a design model of the HCI was to have three groups make judgments of categorical similarity about the components of an interface: human factors specialists with HCI design experience, software developers with HCI design experience, and a baseline group of computer users with no experience in HCI design. The components of the user interface included both display components such as windows, text, and graphics, and user interaction concepts, such as command language, editing, and help. The judgments of the three groups were analyzed using hierarchical cluster analysis and Pathfinder. These methods indicated, respectively, how the groups categorized the concepts, and network representations of the concepts for each group. The Pathfinder analysis provides greater information about local, pairwise relations among concepts, whereas the cluster analysis shows global, categorical relations to a greater extent

Designing Explanation Interfaces for Transparency and Beyond

In this work-in-progress paper, we presented a participatory process of designing explanation interfaces for a social recommender system with multiple explanatory goals. We went through four stages to identify the key components of the recommendation model, expert mental model, user mental model, and target mental model. We reported the results of an online survey of current system users (N=14) and a controlled user study with a group of target users (N=15). Based on the findings, we proposed five set of explanation interfaces for five recommendation models (N=25) and discussed the user preference of the interface prototypes

A component-based collaboration infrastructure

Groupware applications allow geographically distributed users to collaborate on shared tasks. However, it is widely recognized that groupware applications are expensive to build due to coordination services and group dynamics, neither of which is present in single-user applications. Previous collaboration transparency systems reuse existing single-user applications as a whole for collaborative work, often at the price of inflexible coordination. Previous collaboration awareness systems, on the other hand, provide reusable coordination services and multi-user widgets, but often with two weaknesses: (1) the multi-user widgets provided are special-purpose and limited in number, while no guidelines are provided for developing multi-user interface components in general; and (2) they often fail to reach the desired level of flexibility in coordination by tightly binding shared data and coordination services. In this dissertation, we propose a component-based approach to developing group- ware applications that addresses the above two problems. To address the first prob- lem, we propose a shared component model for modeling data and graphic user inter- face(GUI) components of groupware applications. As a result, the myriad of existing single-user components can be re-purposed as shared GUI or data components. An adaptation tool is developed to assist the adaptation process. To address the second problem, we propose a coordination service framework which systematically model the interaction between user, data, and coordination protocols. Due to the clean separation of data and control and the capability to dynamically "glue" them together, the framework provides reusable services such as data distribution, persistence, and adaptable consistency control. The association between data and coordination services can be dynamically changed at runtime. An Evolvable and eXtensible Environment for Collaboration (EXEC) is built to evaluate the proposed approach. In our experiments, we demonstrate two benefits of our approach: (1) a group of common groupware features adapted from existing single- user components are plugged in to extend the functionalities of the environment itself; and (2)coordination services can be dynamically attached to and detached from these shared components at different granules to support evolving collaboration needs

Mixed-fidelity prototyping of user interfaces

This research presents a new technique for user interface prototyping, called mixed-fidelity prototyping. Mixed-fidelity prototyping combines low-, medium-, and high-fidelity interface elements within a single prototype in a lightweight manner, supporting independent refinement of individual elements. The approach allows designers to investigate alternate designs, including more innovative designs, and elicit feedback from stakeholders without having to commit too early in the process. As well, the approach encourages collaboration among a diverse group of stakeholders throughout the design process. For example, individuals who specialize in specific fidelities, such as high-fidelity components, are able to become involved earlier on in the process. We developed a conceptual model called the Region Model and implemented a proof-of-concept system called ProtoMixer. We demonstrated the mixed-fidelity approach by using ProtoMixer to design an example application. ProtoMixer has several benefits over other existing prototyping tools. With ProtoMixer, prototypes can be composed of multiple fidelities, and elements are easily refined and transitioned between different fidelities. Individual elements can be tied into data and functionality, and can be executed inside prototypes. As well, traditional informal practices such as sketching and storyboarding are supported. Furthermore, ProtoMixer is designed for collaborative use on a high-resolution, large display workspace

INVESTIGATING THE EVOLUTION OF TREE BOOSTING MODELS WITH VISUAL ANALYTICS

The present disclosure relates a method and a visual interactive system for tree boosting (VISTB). Tree boosting uses a group of sequentially generated weak learners (i.e., decision trees), each learns from the mistakes of its predecessor, to push the model’s decision boundary towards the true boundary. As the number of trees keeps increasing over training, it is important to reveal how the newly added trees change the predictions of individual data instances, and how the impact of different data features evolve. To accomplish these goals, in this present disclosure, proposes a new design of temporal confusion matrix, an effective interface is provided to users to track data instances’ predictions across the tree boosting process. Also, an improved visualization is presented the users to better illustrate and compare the impact of individual data features across time. Integrating these components with a tree structure visualization component in coordinated views

An alternative confusion matrix implementation for PreCall

In this work, we examine literature on creating visualizations for the performance of machine learning classifiers, with our target group being users with limited machine learning experience. The underlying data is taken from Wikipedia, and more specifically ORES - Wikimedia’s service, which employs a machine learning model to score edits and articles. The interface also expands on PreCall’s implementation, and features multiple interactive components allowing the user to dynamically adjust parameters and see the immediate change in the classifier’s performance. After providing a summary of the relevant literature, we go over the ORES API and its relevant endpoints and parameters. Then, we outline the most popular ways to visualize a machine learning classifier’s performance. Following that is a thorough description of our target group, goals, and requirements, as well as the reasoning behind each design decision. Finally, there is an overview of the design and development process and we conduct a feedback session with a machine learning expert with background in ORES, and the feedback we receive is mostly positive, with some suggestions for improvement