A methodological approach for user interface development of collaborative applications: A case study

In the last few years, the production of systems which support learning and group work has been high. However, the design and development of these types of systems are difficult, mainly due to the multidisciplinarity involved. Furthermore, the Graphic User Interface (GUI) of an application is receiving greater attention, since it can be decisive in determining if the application is accepted or rejected by users. Model-based design is a widespread technique in the user interface development process. While reviewing approaches that deal with the modeling and design of user interfaces supporting collaborative tasks, we have detected that there is no proposal that links interactive and collaborative issues. We have introduced a methodological approach to solve this shortcoming. This approach is called CIAM (Collaborative Interactive Application Methodology) and it is composed of several stages in which conceptual models are created using CIAN (Collaborative Interactive Application Notation). These models start by modeling the organization in which the application will be used, as well as the tasks that must be supported. In the initial stages, the organization and the collaborative tasks are modeled using high-level specifications. In the following stages, the level of detail increases and, finally, the interaction between the individual users and the application is modeled using ConcurTaskTrees (CTT) notation. The interaction model acts as a bridge between the design and the implementation of the Graphic User Interface. In this paper we present our methodological approach and an example of applying this method for user interface design of collaborative and interactive applications.     

Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis

Data driven-based intelligent fault diagnosis methods, as a promising approach, have been widely employed in the health management and maintenance decision of rotating machinery. However, the domain shift phenomenon caused by internal and external interference inevitably exists in practical application scenarios, which significantly deteriorates the performances of the intelligent diagnosis model. And the preparation of label information in real complex scenes is usually time-consuming and expensive. To overcome these challenges, a novel unsupervised domain adaptation framework named deep multi-scale adversarial network with attention (MSANA) is introduced for machinery fault diagnosis. It is established based on two main components, one is the shared feature generator, which is constructed by two novel multi-scale modules with attention mechanism, and the other part is a fault pattern recognition module composed of two differentiated discriminators. While the multi-scale module is used to obtain rich features through different internal perceptual scales, the attention mechanism determines the weights of different scales, which promotes the dynamic adjustment performance and adaptive ability of the model. Then, decision boundary assisted adversarial learning strategy is employed to eliminate domain distribution differences and obtain domain-invariant features. A total of ten rolling bearing-based transfer scenarios and six gearbox-based transfer scenarios are adopted to evaluate the transferability of the proposed MSANA model, and the cross-domain transfer results show that it has superior transferability and stability.     

MTi: A method for user identification for multitouch displays

This paper describes MTi, a biometric method for user identification on multitouch displays. The method is based on features obtained only from the coordinates of the 5 touchpoints of one of the user's hands. This makes MTi applicable to all multitouch displays large enough to accommodate a human hand and detect 5 or more touchpoints without requiring additional hardware and regardless of the display's underlying sensing technology. MTi only requests that the user places his hand on the display with the fingers comfortably stretched apart. A dataset of 34 users was created on which our method reported 94.69% identification accuracy. The method's scalability was tested on a subset of the Bosphorus hand database (100 users, 94.33% identification accuracy) and a usability study was performed.     

Effects of split attention revisited: A new display technology for troubleshooting tasks

Knowledge acquisition processes benefit from presentations that depict related information – like diagrams and formulas – in an integrated way. In contrast, disconnecting interrelated information impairs learning. However, not every task is suitable for an integrated presentation. In the current study, 80 naïve college students were presented with a troubleshooting task preventing an integrated presentation format. We compared performance in a split screen setup depicting two images side-by side with performance in a vexing-image setup. The latter enables the presentation of two images such that participants are able to switch between them without moving the visual focus. Compared to the split screen condition in which participants have to shift their visual focus frequently, troubleshooting performance of correctly identified malfunctioning mechanical devices was higher in the vexing image mode. Thus, not the separation of external representations but re-orientation processes after switching the visual focus seems to be responsible for split attention effects.     

A meta-model for dataflow-based rules in smart environments: Evaluating user comprehension and performance

A considerable part of the behavior in smart environments relies on event-driven and rule specification. Rules are the mechanism most often used to enable user customization of the environment. However, the expressiveness of the rules available to users in editing and other tools is usually either limited or the available rule editing interfaces are not designed for end-users with low skills in programming. This means we have to look for interaction techniques and new ways to define user customization rules. This paper describes a generic and flexible meta-model to support expressive rules enhanced with data flow expressions that will graphically support the definition of rules without writing code. An empirical study was conducted on the ease of understanding of the visual data flow expressions, which are the key elements in our rule proposal. The visual dataflow language was compared to its corresponding textual version in terms of comprehension and ease of learning by teenagers in exercises involving calculations, modifications, writing and detecting equivalences in expressions in both languages. Although the subjects had some previous experience in editing mathematical expressions on spreadsheets, the study found their performance with visual dataflows to be significantly better in calculation and modification exercises. This makes our dataflow approach a promising mechanism for expressing user-customized reactive behavior in Ambient Intelligence (AmI) environments. The performance of the rule matching processor was validated by means of two stress tests to ensure that the meta-model approach adopted would be able to scale up with the number of types and instances in the space.     

UX Curve: A method for evaluating long-term user experience

The goal of user experience design in industry is to improve customer satisfaction and loyalty through the utility, ease of use, and pleasure provided in the interaction with a product. So far, user experience studies have mostly focused on short-term evaluations and consequently on aspects relating to the initial adoption of new product designs. Nevertheless, the relationship between the user and the product evolves over long periods of time and the relevance of prolonged use for market success has been recently highlighted. In this paper, we argue for the cost-effective elicitation of longitudinal user experience data. We propose a method called the “UX Curve” which aims at assisting users in retrospectively reporting how and why their experience with a product has changed over time. The usefulness of the UX Curve method was assessed in a qualitative study with 20 mobile phone users. In particular, we investigated how users’ specific memories of their experiences with their mobile phones guide their behavior and their willingness to recommend the product to others. The results suggest that the UX Curve method enables users and researchers to determine the quality of long-term user experience and the influences that improve user experience over time or cause it to deteriorate. The method provided rich qualitative data and we found that an improving trend of perceived attractiveness of mobile phones was related to user satisfaction and willingness to recommend their phone to friends. This highlights that sustaining perceived attractiveness can be a differentiating factor in the user acceptance of personal interactive products such as mobile phones. The study suggests that the proposed method can be used as a straightforward tool for understanding the reasons why user experience improves or worsens in long-term product use and how these reasons relate to customer loyalty.