Abstract: We previously developed shinyCircos, an interactive web application for creating Circos diagrams, which has been widely recognized for its graphical user interface and ease of use. Here, we introduce shinyCircos-V2.0, an upgraded version of shinyCircos that includes a new user interface with enhanced usability and many new features for creating advanced Circos plots. To help users get started with shinyCircos-V2.0, we provide detailed tutorials and example input data sets. The application is available online at https://venyao.xyz/shinyCircos/ and https://asiawang.shinyapps.io/shinyCircos/, or can be installed locally using the source code deposited in GitHub (https://github.com/YaoLab-Bioinfo/shinyCircos-V2.0).

Abstract: eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface.

Abstract: Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the upgrading of mobile devices and the development of aesthetics, the visual effects of the GUI are more and more attracting, and users pay more attention to the accessibility and usability of applications. However, such GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, component occlusion, missing image always occur during GUI rendering on different devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a fully automated approach, Nighthawk , based on deep learning for modelling visual information of the GUI screenshot. Nighthawk can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. At the same time, training the model needs a large amount of labeled buggy screenshots, which requires considerable manual effort to prepare them. We therefore propose a heuristic-based training data auto-generation method to automatically generate the labeled training data. The evaluation demonstrates that our Nighthawk can achieve average 0.84 precision and 0.84 recall in detecting UI display issues, average 0.59 AP and 0.60 AR in localizing these issues. We also evaluate Nighthawk with popular Android apps on Google Play and F-Droid, and successfully uncover 151 previously-undetected UI display issues with 75 of them being confirmed or fixed so far.

Abstract: This study examined user preferences for ChatGPT-powered conversational interfaces vs traditional techniques. The study collected data from 175 selected volunteers utilizing a survey questionnaire. Descriptive and inferential statistics were used to detect user preferences and compare them to the literature review. The study found that 70% of users chose ChatGPT-powered conversational interfaces over traditional techniques, citing convenience, efficiency, and personalization. Demographic data was explored. The participants were evenly distributed between male and female (50%) and aged 18 to 55 (mean = 35 years). This study affects ChatGPT and conversational AI development. The results indicate that users want to use these technologies in their daily lives. To improve ChatGPT, further study is needed in this area. However, this study's tiny sample size must be considered. To confirm these findings and investigate other factors affecting conversational interface user preferences, bigger and more diverse samples are needed.

Abstract: Abstract Rapid GUI prototyping has evolved into a widely applied technique in early stages of software development to facilitate the clarification and refinement of requirements. Especially high-fidelity GUI prototyping has shown to enable productive discussions with customers and mitigate potential misunderstandings, however, the benefits of applying high-fidelity GUI prototypes are accompanied by the disadvantage of being expensive and time-consuming in development and requiring experience to create. In this work, we show RaWi , a data-driven GUI prototyping approach that effectively retrieves GUIs for reuse from a large-scale semi-automatically created GUI repository for mobile apps on the basis of Natural Language (NL) searches to facilitate GUI prototyping and improve its productivity by leveraging the vast GUI prototyping knowledge embodied in the repository. Retrieved GUIs can directly be reused and adapted in the graphical editor of RaWi . Moreover, we present a comprehensive evaluation methodology to enable (i) the systematic evaluation of NL-based GUI ranking methods through a novel high-quality gold standard and conduct an in-depth evaluation of traditional IR and state-of-the-art BERT-based models for GUI ranking, and (ii) the assessment of GUI prototyping productivity accompanied by an extensive user study in a practical GUI prototyping environment.

Abstract: The field of generative artificial intelligence has seen significant advancements in recent years with the advent of large language models, which have shown impressive results in software engineering tasks but not yet in engineering user interfaces. Thus, we raise a specific research question: would an LLM-based system be able to search for relevant GUI layouts? To address this question, we conducted a controlled study evaluating how Instigator, an LLM-based system for searching GUI layouts of web pages by generative pre-trained training, would return GUI layouts that are relevant to a given instruction and what would be the user experience of (N =34) practitioners interacting with Instigator. Our results identify a very high similarity and a moderate correlation between the rankings of the GUI layouts generated by Instigator and the rankings of the practitioners with respect to their relevance to a given design instruction. We highlight the results obtained through thirteen UEQ+ scales that characterize the user experience of the practitioner with Instigator, which we use to discuss perspectives for improving such future tools.

Abstract: Large Language Models have enabled novices without machine learning (ML) experience to quickly prototype ML functionalities with prompt programming. This paper investigates incorporating prompt-based prototyping into designing functional user interface (UI) mock-ups. To understand how infusing LLM prompts into UI mock-ups might affect the prototyping process, we conduct a exploratory study with five designers, and find that this capability might significantly speed up creating functional prototypes, inform designers earlier on how their designs will integrate ML, and enable user studies with functional prototypes earlier. From these findings, we built PromptInfuser, a Figma plugin for authoring LLM-infused mock-ups. PromptInfuser introduces two novel LLM-interactions: input-output, which makes content interactive and dynamic, and frame-change, which directs users to different frames depending on their natural language input. From initial observations, we find that PromptInfuser has the potential to transform the design process by tightly integrating UI and AI prototyping in a single interface.

Abstract: One of the important components of the lifelog systems is the user interface which provides the ability to quickly and easily find a specific image or set of images. Although lifelogging is a mature field in the information retrieval domain, the focus on user interfaces is not explored extensively. We start by identifying the common issues with existing lifelog systems from the user interface and user experience perspective. Following the exploration, we present a set of guidelines for designing a user interface for Lifelog systems. We introduce LifeLens- a novel minimalist user interface design specifically designed to improve the usability and ease of use of an interactive lifelog system. The initial version of the LifeLens system provides several improvements over existing lifelog systems addressing the design issues identified during the exploration. The proposed system presents several features that not only enable the users of the system to easily navigate the interface with minimal effort on the user’s part to learn and understand the features offered but also provide a minimal way to gather user feedback.

Abstract: This study examined user preferences for ChatGPT-powered conversational interfaces vs traditional techniques. The study collected data from 175 selected volunteers utilizing a survey questionnaire. Descriptive and inferential statistics were used to detect user preferences and compare them to the literature review. The study found that 70% of users chose ChatGPT-powered conversational interfaces over traditional techniques, citing convenience, efficiency, and personalization. Demographic data was explored. The participants were evenly distributed between male and female (50%) and aged 18 to 55 (mean = 35 years). This study affects ChatGPT and conversational AI development. The results indicate that users want to use these technologies in their daily lives. To improve ChatGPT, further study is needed in this area. However, this study's tiny sample size must be considered. To confirm these findings and investigate other factors affecting conversational interface user preferences, bigger and more diverse samples are needed.

Abstract: Building on the success of the first workshop on understanding, generating, and adapting user interfaces at CHI2022, this workshop will advance this research area further by looking at existing results and exploring new research directions. Computational approaches for user interfaces have been used in adapting interfaces for different devices, modalities, and user preferences. Recent work has made significant progress in understanding and adapting user interfaces with traditional constraint/rule-based optimization and machine learning-based data-driven approaches; however, these two approaches remain separate. Combining the two approaches has great potential to advance the area but remains under-explored and challenging. Other contributions, such as datasets for potential applications, novel representations of user interfaces, the analysis of human traces, and models with multi-modalities, will also open up future research options. The proposed workshop seeks to bring together researchers interested in computational approaches for user interfaces to discuss the needs and opportunities for future user interface algorithms, models, and applications.

Abstract: Cognitive accessibility aims to make content more accessible for people with cognitive impairments, such as the elderly and people with intellectual and learning disabilities. In this sense, it is possible to design an accessible user interface from a cognitive point of view. As a contribution, this article presents cognitive accessibility design patterns and their application in designing the Easier web system's user interface. The Easier web system provides a tool that assists in the understanding and readability of text content geared towards people with intellectual disabilities. It detects complex words and offers easier replacements and other resources such as a definition of the complex word. In addition to applying the design patterns, user tests with people with intellectual disabilities and older people have been carried out to evaluate the cognitive accessibility of the Easier system's interface. The results indicate that people with cognitive impairments know how to use the interfaces and have a satisfactory experience. In addition, a design proposal to provide a glossary mechanism to be used in web interfaces with simplified texts is presented and validated.

Abstract: Early As we all know, how life is interlinked with the technology and the use of AI. AI-powered voice assistants have become an integral part of our lives, intertwining technology and daily tasks. A Personal Virtual Assistant allows a user to command or ask questions in the same manner that they would do with another human and are even capable of doing some basic tasks like opening apps, doing Wikipedia searches without opening a browser, playing music etc, with just a voice command. This project presents the development of a personal desktop assistant using Python, aiming to provide convenience, automation, and assistance to users in their computer-related activities. The assistant incorporates features such as voice recognition, natural language processing, and integration with external APIs to enhance its functionality and user experience. The assistant differentiates itself from existing solutions by offering a highly customizable and extensible platform. Users can tailor the assistant's behavior and functionality to their specific needs, while also benefiting from integration with popular tools and services. The user interface is designed to be intuitive and user-friendly, providing a seamless experience for both novice and experienced users. By creating a personal desktop assistant that combines convenience, automation, and personalized features, this project aims to enhance users' productivity and efficiency in their day-to-day computer tasks.

Abstract: Cookies have been used by websites to store information about user behavior. Although they provide several benefits, including improving user experience, they can threaten user privacy, particularly when websites use third-party cookies for data analysis. Websites must inform their users about what data are collected and how they are used through the cookie interface. Thus, it is important to understand the effects of cookie interface design on user behavior to verify whether these interfaces provide users with the required information to make an informed decision. In this paper, we evaluated the cookie interfaces of 243 E-Government websites based on well-defined guidelines to understand the critical factors designers should consider when designing cookie interfaces. To evaluate the cookies interfaces’ usability, we selected one of the inspection-based methods called the individual expert review method. The results showed that European websites are more compliant with the adopted guidelines. Surprisingly, more than 50% of the websites did not provide a cookie interface to their users, while more than 40% did not provide a privacy policy. The primary finding of this study is that over 90% of the websites use dark patterns in their interfaces. The study concludes with some recommendations to help in designing a usable privacy interface.

Abstract: Abstract This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.

Abstract: Abstract Background A variety of human computer interfaces are used by robotic surgical systems to control and actuate camera scopes during minimally invasive surgery. The purpose of this review is to examine the different user interfaces used in both commercial systems and research prototypes. Methods A comprehensive scoping review of scientific literature was conducted using PubMed and IEEE Xplore databases to identify user interfaces used in commercial products and research prototypes of robotic surgical systems and robotic scope holders. Papers related to actuated scopes with human–computer interfaces were included. Several aspects of user interfaces for scope manipulation in commercial and research systems were reviewed. Results Scope assistance was classified into robotic surgical systems (for multiple port, single port, and natural orifice) and robotic scope holders (for rigid, articulated, and flexible endoscopes). Benefits and drawbacks of control by different user interfaces such as foot, hand, voice, head, eye, and tool tracking were outlined. In the review, it was observed that hand control, with its familiarity and intuitiveness, is the most used interface in commercially available systems. Control by foot, head tracking, and tool tracking are increasingly used to address limitations, such as interruptions to surgical workflow, caused by using a hand interface. Conclusion Integrating a combination of different user interfaces for scope manipulation may provide maximum benefit for the surgeons. However, smooth transition between interfaces might pose a challenge while combining controls.

Abstract: The design of recommender systems' graphical user interfaces (GUIs) is critical for a user's experience with these systems. However, most research into recommenders focuses on algorithms, overlooking the design of their interfaces. Additionally, the studies on the design of recommender interfaces that do exist do not always manage to cross the research-practice gap. This disconnect may be due to a lack of alignment between academic focus and the most pressing needs of practitioners, as well as the way research findings are communicated. To address these issues, this paper presents the results of a comprehensive study involving 215 designers worldwide, aiming to identify the primary challenges in designing recommender GUIs and the resources practitioners need to tackle those challenges. Building on these findings, this paper proposes a practice-led research agenda for the human-computer interaction community on designing recommender interfaces and suggestions for more accessible and actionable ways of disseminating research results in this domain.

Abstract: Virtual reality and eye-tracking technologies are nowadays standard research tools. A growing number of researchers from different disciplines are utilizing these technologies. Currently, access to eye-tracking hardware in virtual reality glasses is usually provided as APIs to call functions of the eye-tracking device. Proper implementation is device-specific and left to the user. Especially non-computer scientists are left alone with this problem, which impedes eye-tracking research in virtual reality for many scientists. This paper describes a generic open-source interface for everyone to efficiently and easily utilize common eye trackers in virtual reality. The interface is published under a friendly CC BY 4.0 license that allows for integration, modification, and extension of the code. It includes a standardized interface for several eye-tracking devices in virtual reality, is ready to be used out of the box, and allows easy addition of APIs from other manufacturers. The code is available at https://bitbucket.org/benediktwhosp/zvsl-zero

Abstract: Abstract The emotional experience of the driver is influenced by the design of the in-vehicle interaction interface. User experience journey maps are commonly used by designers to reveal interface design pain points and refine user needs, and further studies are required to effectively characterize and quantify user emotional needs. This study provides a method for accurately presenting a driver’s emotional experience through a human–machine interface using Kansei engineering and user experience journey. Firstly, the semantic difference approach was used to match the relationship between user behavioral touchpoints and Kansei imagery words of the interface. And then the emotional quantification curve was built to generate an average value for Kansei imagery word evaluation. Finally, design pain points were identified and iterative design was carried out. A validation study was implemented to ensure the method’s validity. The study demonstrated that a quantitative map of user emotional experience could efficiently quantify and depict the findings of emotional quantification. This method enables designers to accurately recognize user needs while also facilitating product iterations.

Abstract: Sign language can make possible effective communication between hearing and deaf-mute people. Despite years of extensive pedagogical research, learning sign language remains a formidable task, with the majority of the current systems relying extensively on online learning resources, presuming that users would regularly access them; yet, this approach can feel monotonous and repetitious. Recently, gamification has been proposed as a solution to the problem, however, the research focus is on game design, rather than user experience design. In this work, we present a system for user-defined interaction for learning static American Sign Language (ASL), supporting gesture recognition for user experience design, and enabling users to actively learn through involvement with user-defined gestures, rather than just passively absorbing knowledge. Early findings from a questionnaire-based survey show that users are more motivated to learn static ASL through user-defined interactions.

Abstract: Voice user interfaces (VUI) come in various forms of software or hardware, are controlled by voice, and can help the user in their daily life. Despite VUIs being readily available on smartphones, they have a low adoption rate. This can be attributed to challenges such as the misunderstanding of voice commands as well as privacy and data security concerns. Still, there are intensive VUI users, but they also raise concerns that may be independent of culture. Hence, we will discuss in our paper the various areas that should be considered when developing VUIs to increase user acceptance and foster a positive user experience (UX). We propose exploring the context of use and UX aspects to understand users’ needs while using VUIs. All of our suggestions can help VUI developers to design better VUIs.

Abstract: Although mobile phone application developers aim to design software to be user-friendly, elderly users report having problems with flexible user interfaces. The purpose of this study was to examine a range of flexible user interface designs for mobile phones with the aim of locating and addressing the limitations reported by elderly users. Accordingly, a fuzzy model drawing on a range of variables was developed from an age/vision impaired related data set for the development of a variety of basic design elements for user interfaces. The model was tested to assess the preciseness and accuracy of its functions, achieving a Mean Absolute Error (MAE) close to 0 and an Effectiveness Index (EI) close to 1, giving the model a high value for effectiveness. A subsequent usability test of the generated design interfaces using four types of mobile phones (18 screens in all) was conducted among 25 elderly users with vision impairment. The findings showed that the size and shape of both numeric and function buttons was a significant factor in assessing phone usability both for communication and for social media use, as was text and number size, although, significantly, the latter was qualified by screen size. Recommended numeric and dial function button sizes are 15.6mm and 16.2 mm, respectively. Likewise, recommended text and numbers sizes are 14 and 25 points, respectively. Furthermore, square-shaped buttons with rounded-edge buttons are the most suitable for elderly users, as is a background in a light shade, with texts and icons in dark colors. The model demonstrates that it is possible to design user interfaces with particular groups in mind such as the elderly and vision-impaired, in order to enhance mobile phone usability for these groups.

Abstract: This paper describes a lab demo by the User Interfaces group at Aalto University. The demo allows attendees to interactively experience recent research prototypes aiming to facilitate designers’ creative and problem-solving capabilities in user interface (UI) design. Empirical work on designers suggests that UI design is challenging, partially because of the presence of very large design spaces, multiple and ill-defined objectives, design fixation and biases, as well as multiple requirements that need to to kept in mind. At the exhibition, members of the lab provide live demonstrations of six computational features, with a special focus on plug-ins created for Figma, a popular UI design tool. The demos draw from the group’s latest research published at HCI conferences. They demonstrate how to interactively exploit machine learning methods ranging from deep nets to Bayesian inference and NLP. We also present our design approach and provide a summary of findings from empirical evaluations with designers.

Abstract: This paper describes prototyping of the draft Solid application interoperability specification (INTEROP). We developed and evaluated a dynamic user interface (UI) for the new Solid application access request and authorization extended with the Data Privacy Vocabulary. Solid places responsibility on users to control their data. INTEROP adds new declarative access controls. Solid applications to date have provided few policy interfaces with high usability. GDPR controls on usage are rarely addressed. Implementation identified specification and Semantic Web tool issues and also in the understandability of declarative policies, a key concern under GDPR or data ethics best practices. The prototype was evaluated in a usability and task accuracy experiment, where the UI enabled users to create access and usage control policies with an accuracy of between 72 and 37%. Overall, the UI had a poor usability rating, with a median SUS (system usability scale) score of 37.67. Experimental participants were classified according to the Westin privacy scale to investigate the impact of user attitudes to privacy on the results. The paper discusses the findings of the study and their consequences for future data sovereignty access request and authorization UI designs.

Abstract: Abstract Computational design tools allow the generation of vast numbers of possible designs, entrusting the human designer with describing constraints or specifications to guide exploration of the design space. Designers can have many different decision considerations when conducting this type of exploration, including form, function, users, or context. In this work, we investigate strategies that emerge when people are tasked with exploring a large design space within either a non-immersive (2D) or immersive (VR) interface and equipped with action-based interactions to set or envision specifications related to their considerations. Results from a 28 participant user study uncovers that people have varying strategies to enact their decision considerations that are not unique to the type of interface. However, the interfaces differ in perceptions of enabling breadth or depth of exploration holistically, with preference towards 2D interfaces to compare options, and VR to understand single designs. These results have implications for the user experience of systems that allow designers to explore the outputs of large design spaces, both at the interaction and interface levels.

Abstract: A graphical user interface presented is intended for fast design, symbolic analysis, accurate simulation, exact verification, and test report preparation. It helps to skip the gap between theory and practice in electrical engineering because the numeric analysis is usually approximate, and the power of symbolic systems has insufficient speed even, for simple engineering problems. The software is written using a computer algebra system that is free on small computers. The mathematical representation of the system can be obtained automatically from the schematic description. Further automated symbolic manipulations are possible according to the user’s aspirations.

Abstract: Abstract This study investigated the impact of prior experience and education levels on user expectations in Artificial Intelligence (AI) based systems. The research aimed to determine whether these factors, individually or interactively, significantly influenced user expectations. Moreover, the effects of system interface, system feedback and system responsiveness on user comfort in AI-based systems were determined as well. The findings highlighted the importance of prior experience in shaping user expectations. It also suggests that educational level may have limited influence on user expectations. The choice of system interface and the responsiveness of the AI-based system significantly impact user comfort. The findings suggest for the creation of more user-friendly and comfortable interfaces. Understanding the various factors that influence user comfort and expectation, can aid the design and development of AI systems tailored to user backgrounds that better meet user needs and enhance their overall experience.