Interface design: An educational perspective

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

Telehealth has played a critical role in supporting healthcare delivery during the COVID-19 pandemic. Mitigation practices of physical distancing and sheltering-in-place continue to impose a significant burden on face-to-face healthcare visits. As a result, telehealth adoption and utilization have increased significantly after the U.S. Department of Health and Human Services waived telehealth payment requirements. These changes have been rapid and affect both individuals and organizations, ranging from healthcare and technology to news and policy. We turn to Twitter, as a broad-reaching communication platform, to elucidate and characterize Telehealth communication patterns. While different aspects of tweeting about telehealth have been used to answer specific questions, we sought to understand the collective telehealth tweeting behavior as a system of different types of users tweeting with various intent and sentiment. Such an approach combines the typically independent analysis of structure, using social network analysis, and purpose or function of communication, with topic analysis and sentiment analysis. In this paper, we apply a systems-based approach-called hetero-functional graph theory-to comprehensively describe the structure and function of the Twitter Telehealth communication system. We modeled 13 different types of telehealth users and 15 combinations of tweet intent and sentiment, which together describe 195 system capabilities. We applied machine learning to classify individual user tweeting behavior for different classes of telehealth stakeholders. We identified several patterns of longitudinal tweeting behaviors that varied by system form and function. By applying an interdependent methodology to understand both form and function, we provide deeper insights into tweeting behaviors between different classes of stakeholders. In doing so, we achieve a greater understanding of the various needs of different stakeholders, which can be leveraged as a valuable resource in informing more equitable policies as telehealth becomes a more permanent and scaled healthcare delivery service.

BackgroundPhantom limb pain is a frequent and persistent problem following amputation. Achieving sustainable favorable effects on phantom limb pain requires therapeutic interventions such as mirror therapy that target maladaptive neuroplastic changes in the central nervous system. Unfortunately, patients’ adherence to unsupervised exercises is generally poor and there is a need for effective strategies such as telerehabilitation to support long-term self-management of patients with phantom limb pain.ObjectiveThe main aim of this study was to describe the user-centered approach that guided the design and development of a telerehabilitation platform for patients with phantom limb pain. We addressed 3 research questions: (1) Which requirements are defined by patients and therapists for the content and functions of a telerehabilitation platform and how can these requirements be prioritized to develop a first prototype of the platform? (2) How can the user interface of the telerehabilitation platform be designed so as to match the predefined critical user requirements and how can this interface be translated into a medium-fidelity prototype of the platform? (3) How do patients with phantom limb pain and their treating therapists judge the usability of the medium-fidelity prototype of the telerehabilitation platform in routine care and how can the platform be redesigned based on their feedback to achieve a high-fidelity prototype?MethodsThe telerehabilitation platform was developed using an iterative user-centered design process. In the first phase, a questionnaire followed by a semistructured interview was used to identify the user requirements of both the patients and their physical and occupational therapists, which were then prioritized using a decision matrix. The second phase involved designing the interface of the telerehabilitation platform using design sketches, wireframes, and interface mock-ups to develop a low-fidelity prototype. Heuristic evaluation resulted in a medium-fidelity prototype whose usability was tested in routine care in the final phase, leading to the development of a high-fidelity prototype.ResultsA total of 7 categories of patient requirements were identified: monitoring, exercise programs, communication, settings, background information, log-in, and general requirements. One additional category emerged for therapists: patient management. Based on these requirements, patient and therapist interfaces for the telerehabilitation platform were developed and redesigned by the software development team in an iterative process, addressing the usability problems that were reported by the users during 4 weeks of field testing in routine care.ConclusionsOur findings underline the importance of involving the users and other stakeholders early and continuously in an iterative design process, as well as the need for clear criteria to identify critical user requirements. A decision matrix is presented that incorporates the views of various stakeholders in systematically rating and prioritizing user requirements. The findings and lessons learned might help health care providers, researchers, software designers, and other stakeholders in designing and evaluating new teletreatments, and hopefully increase the likelihood of user acceptance.

Important goals for interface designers are to determine when it is beneficial to use iconic or command-based menus and how these traditional menu systems can be enhanced by the use of sound. To investigate this topic, 28 subjects were asked to maneuver through one of four different implementations of an interactive software package in order to execute a target command using either an iconic or command-based menu with or without the use of sound. The sounds descended in pitch as the depth of the menu increased, and were used as an auditory cue to code the location of a particular menu item. The results indicate that icons were more easily remembered than commands, but that commands were easier to locate under the highest level branch of the menu. The results also indicate a trend toward faster performance times with the use of command-based menus in comparison to iconic-based menus designed with the same structure. However, there was no difference in performance within iconic or command-based menus when sound was included in the user interface. Implications for user interface design are discussed. >

Considering the characteristics of different types of users in hybrid carsharing systems, in which sharing autonomous vehicles (SAVs) and conventional sharing cars (CSCs) coexist, a tailored pricing strategy (TPS) is proposed to maximize the operator’s profit and minimize all users’ costs. The fleet sizes and sizes of SAVs’ stations are also determined simultaneously. A bi-objective nonlinear programming model is established, and a genetic algorithm is applied to solve it. Based on the operational data in Lanzhou, China, carsharing users are clustered into three types. They are loyal users, losing users, and potential users, respectively. Results show the application of the TPS can help the operator increase profit and attract more users. The loyal users are assigned the highest price, while they still contribute the most to the operator’s profit with the highest number of carsharing trips. The losing users and potential users are comparable in terms of the number of trips, while the latter still makes more profit.

An information-pattern-based approach to novelty detection

Concept design of the DEMO divertor cassette-to-vacuum vessel locking system adopting a systems engineering approach

This study was conducted to provide evidence for the nature of visual search processes with menus, and to derive design principles for menu-based natural language (MBNL) interfaces to databases. The effects of window size, window activity, and query length were investigated. It was found that longer queries were performed faster with single active windows, but multiple active windows were rated as more 'natural'. Query times increased with query length, as did eye fixation frequencies, fixation durations, and dwell times. Errors were most likely to occur on the longest query. Fixation durations also varied with window size. However, visual behavior depended on the area being viewed and on the interaction between window activity and query length. In contrast with previous studies, it was also found that menus were not scanned randomly. However, scanpaths were less deterministic with multiple active windows and became even more unconstrained as query length increased. User interface design recommendations were derived from the findings.

The purpose of this paper is to introduce an iterative interaction design process that can be used to facilitate the design of silent interaction. An innovative communication facilitator that can assist users who are hearing and/or speech impaired in interacting with other persons is used as an example to help explain how the interaction design concepts were incorporated into this iterative design process. The author hopes that by using this user-friendly communication facilitator, users who are hearing and/or speech impaired can interact with others in a more efficient and effective way.

This study explores whether social roles mentioned in jihadist literature can be used linguistically to profile the behavior of its consumers. It seeks to empirically understand extremist forums, where such ideological communication is widely disseminated, and to identify different types of user behavior through their discussions in these communities. An Arabic-language corpus was constructed with 1050 users categorized into seven behavioral profiles derived from distinct patterns of communication observed among users in forum networks. Statistical key term extraction was used to find significant social roles referenced in users’ posts as these were proposed to differentiate their behavioral profiles. Multinomial logistic regression and post hoc tests were used to find the frequency of identified roles as the highest-ranking terms capable of prediction. Role terms produced high accuracy scores across classification experiments in identifying behavioral profiles (95% CI, 0.92–0.98), with varying intra- and inter-behavioral differentiation abilities emerging from the authority, religion, closeness, and conflict themes of social roles. This suggests simple and complex predictive potential exist in these constructs for separating profiles based on nuanced expressions of social roles by different types of users in extremist contexts, as was the case for distinguishing extremist organizations through their ideological communication in earlier work.

Towards optimal customized electricity pricing via iterative two-layer optimization for consumers and prosumers

Based on the investigation and analysis of typical users in Beijing and Guangzhou, a comparative analysis of the load characteristics of different types of users is carried out, and the basic characteristics of the load characteristics of various types of users are summarized. In the calculation example, the hourly load of the typical buildings in Beijing and Guangzhou is simulated and analyzed for the whole year. The load stability of the hotel and hospital buildings is poor and the continuity is strong. The load stability of shopping malls, residential and office buildings Stronger and weaker in persistence; the imbalance rate of the cumulative cooling and heating load in the summer hot winter and warm winter area and the cumulative cooling and heating load in the air conditioning season is higher, while the unbalance rate in the cold area is lower.

We propose a framework for describing, comparing and understanding tools for the mining of software repositories. The fundamental premise of this framework is that mining should be done by considering the specific needs of the users and the tasks to be supported by the mined information. First, different types of users have distinct needs, and these needs should be taken into account by tool designers. Second, the data sources available, and mined, will determine if those needs can be satisfied. Our framework is based upon three main principles: the type of user, the objective of the user, and the mined information. This framework has the following purposes: to help tool designers in the understanding and comparison of different tools, to assist users in the assessment of a potential tool; and to identify new research areas. We use this framework to describe several mining tools and to suggest future research directions.

As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the ‘how’ and ‘why’ of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) What does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of high-level reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

Because of the rapidly growing access of social media platforms, it provides convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, characterizing user types to understand people’s lifestyle decisions shared in social media is challenging task due to diverse information. In this paper, we propose a weakly supervised graph embedding based framework for creating user representation to understand user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on `Yoga’, `Keto diet’. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.