Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1–15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.

Visualization knowledge bases enable computational reasoning and recommendation over a visualization design space. These systems evaluate design trade-offs using numeric weights assigned to different features (e.g., binning a variable). Feature weights can be learned automatically by fitting a model to a collection of chart pairs, in which one chart is deemed preferable to the other. To date, labeled chart pairs have been drawn from published empirical research results; however, such pairs are not comprehensive, resulting in a training corpus that lacks many design variants and fails to systematically assess potential trade-offs. To improve knowledge base coverage and accuracy, we contribute data augmentation techniques for generating and labeling chart pairs. We present methods to generate novel chart pairs based on design permutations and by identifying under-assessed features-leading to an expanded corpus with thousands of new chart pairs, now in need of labels. Accordingly, we next compare varied methods to scale labeling efforts to annotate chart pairs, in order to learn updated feature weights. We evaluate our methods in the context of the Draco knowledge base, demonstrating improvements to both feature coverage and chart recommendation performance.

By leveraging quantum-mechanical properties like superposition, entanglement, and interference, quantum computing (QC) offers promising solutions for problems that classical computing has not been able to solve efficiently, such as drug discovery, cryptography, and physical simulation. Unfortunately, adopting QC remains difficult for potential users like QC beginners and application-specific domain experts, due to limited theoretical and practical knowledge, the lack of integrated interface-wise support, and poor documentation. For example, to use quantum computers, one has to convert conceptual logic into low-level codes, analyze quantum program results, and share programs and results. To support the wider adoption of QC, we, as designers and QC experts, propose interaction techniques for QC through design iterations. These techniques include writing quantum codes conceptually, comparing initial quantum programs with optimized programs, sharing quantum program results, and exploring quantum machines. We demonstrate the feasibility and utility of these techniques via use cases with high-fidelity prototypes.

Data sonification—mapping data variables to auditory variables, such as pitch or volume—is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has been made on effective and intuitive sonification design, software support is not commensurate, limiting researchers from fully exploring its capabilities. We contribute Erie, a declarative grammar for data sonification, that enables abstractly expressing auditory mappings. Erie supports specifying extensible tone designs (e.g., periodic wave, sampling, frequency/amplitude modulation synthesizers), various encoding channels, auditory legends, and composition options like sequencing and overlaying. Using standard Web Audio and Web Speech APIs, we provide an Erie compiler for web environments. We demonstrate the expressiveness and feasibility of Erie by replicating research prototypes presented by prior work and provide a sonification design gallery. We discuss future steps to extend Erie toward other audio computing environments and support interactive data sonification.

Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive data, but requires analysts to judiciously spend a limited "privacy loss budget" ϵ across queries. Analysts conducting exploratory analyses do not, however, know all queries in advance and seldom have DP expertise. Thus, they are limited in their ability to specify ϵ allotments across queries prior to an analysis. To support analysts in spending ϵ efficiently, we propose a new interactive analysis paradigm, Measure-Observe-Remeasure, where analysts "measure" the database with a limited amount of ϵ, observe estimates and their errors, and remeasure with more ϵ as needed.We instantiate the paradigm in an interactive visualization interface which allows analysts to spend increasing amounts of ϵ under a total budget. To observe how analysts interact with the Measure-Observe-Remeasure paradigm via the interface, we conduct a user study that compares the utility of ϵ allocations and findings from sensitive data participants make to the allocations and findings expected of a rational agent who faces the same decision task. We find that participants are able to use the workflow relatively successfully, including using budget allocation strategies that maximize over half of the available utility stemming from ϵ allocation. Their loss in performance relative to a rational agent appears to be driven more by their inability to access information and report it than to allocate ϵ.

To enable data-driven decision-making across organizations, data professionals need to share insights with their colleagues in context-appropriate communication channels. Many of their colleagues rely on data but are not themselves analysts; furthermore, their colleagues are reluctant or unable to use dedicated analytical applications or dashboards, and they expect communication to take place within threaded collaboration platforms such as Slack or Microsoft Teams. In this paper, we introduce a set of six strategies for adapting content from business intelligence (BI) dashboards into appropriate formats for sharing on collaboration platforms, formats that we refer to as dashboard snapshots. Informed by prior studies of enterprise communication around data, these strategies go beyond redesigning or restyling by considering varying levels of data literacy across an organization, introducing affordances for self-service question-answering, and anticipating the post-sharing lifecycle of data artifacts. These strategies involve the use of templates that are matched to common communicative intents, serving to reduce the workload of data professionals. We contribute a formal representation of these strategies and demonstrate their applicability in a comprehensive enterprise communication scenario featuring multiple stakeholders that unfolds over the span of months.

People with dementia (PwD) often present verbal agitation such as cursing, screaming, and persistently complaining. Verbal agitation can impose mental distress on informal caregivers (e.g., family, friends), which may cause severe mental illnesses, such as depression and anxiety disorders. To improve informal caregivers' mental health, we explore design opportunities by interviewing 11 informal caregivers suffering from verbal agitation of PwD. In particular, we first characterize how the predictability of verbal agitation impacts informal caregivers' mental health and how caregivers' coping strategies vary before, during, and after verbal agitation. Based on our findings, we propose design opportunities to improve the mental health of informal caregivers suffering from verbal agitation: distracting PwD (in-situ support; before), prompting just-in-time maneuvers (information support; during), and comfort and education (social & information support; after). We discuss our reflections on cultural disparities between participants. Our work envisions a broader design space for supporting informal caregivers' well-being and describes when and how that support could be provided.

Designing responsive visualizations for various screen types can be tedious as authors must manage multiple chart versions across design iterations. Automated approaches for responsive visualization must take into account the user's need for agency in exploring possible design ideas and applying customizations based on their own goals. We design and implement Dupo, a mixedinitiative approach to creating responsive visualizations that combines the agency afforded by a manual interface with automation provided by a recommender system. Given an initial design, users can browse automated design suggestions for a different screen type and make edits to a chosen design, thereby supporting quick prototyping and customizability. Dupo employs a two-step recommender pipeline that first suggests significant design changes (Exploration) followed by more subtle changes (Alteration). We evaluated Dupo with six expert responsive visualization authors. While creating responsive versions of a source design in Dupo, participants could reason about different design suggestions without having to manually prototype them, and thus avoid prematurely fixating on a particular design. This process led participants to create designs that they were satisfied with but which they had previously overlooked.

Inverted structured organic solar cells (OSCs) have recently attracted attention due to their greater stability and performance above conventional structured OSCs. Consequently, under indoor lighting, OSCs have excellent power conversion efficiency (PCE). Indoor OSC may be a useful power source for a variety of electrical devices connected to the Internet of Things network. An inverted structured OSC, the electron transport layer (ETL) is crucial. Inverted OSCs have been constructed with variety of metal oxides-based ETL. Zinc oxide (ZnO) is one of the best options among them because of its high transparency and stability, but its lower electron mobility is the key reason for the ZnO ETL-based inverted OSCs’ lower PCE. Moreover, very few studies have been conducted on the application of inverted structured OSCs for indoor light harvesting. We fabricate ZnO and gallium (Ga)-doped ZnO (GZO) ETL-based inverted structured OSCs with an poly [(2,6-(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)-benzo]1,2-b:4,5-b’]dithiophene))-alt-(5,5-(1’,3’-di-2-thienyl-5’,7’-bis(2-ethylhexyl)benzo[1’,2’-c:4’,5’-c’]dithiophene-4,8-dione)]:[6],[6]-phenyl-C71-butyric acid methyl ester (PBDB-T:PC70BM) active layer and evaluate them using a light-emitting diode (LED) (1000 lx). To optimize the doping concentration, we have varied the concentration of Ga in the GZO. The findings demonstrate that only by doping the ETL of the inverted structured OSC, the PCE may be significantly enhanced. Under a 1000-lx LED illumination, the device using undoped ZnO as the ETL shows a maximum of 15.56% PCE. The OSC with GZO as the ETL with an optimized concentration of Ga (2 at.%) exhibits a maximum of 23.42% PCE under a 1000-lx LED.

The lack of reliable, personalized information often complicates sexual violence survivors’ support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively.

Designing responsive visualizations can be cast as applying transformations to a source view to render it suitable for a different screen size. However, designing responsive visualizations is often tedious as authors must manually apply and reason about candidate transformations. We present Cicero, a declarative grammar for concisely specifying responsive visualization transformations which paves the way for more intelligent responsive visualization authoring tools. Cicero’s flexible specifier syntax allows authors to select visualization elements to transform, independent of the source view’s structure. Cicero encodes a concise set of actions to encode a diverse set of transformations in both desktop-first and mobile-first design processes. Authors can ultimately reuse design-agnostic transformations across different visualizations. To demonstrate the utility of Cicero, we develop a compiler to an extended version of Vega-Lite, and provide principles for our compiler. We further discuss the incorporation of Cicero into responsive visualization authoring tools, such as a design recommender.

Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.

Abstract Increased access to mobile devices motivates the need to design communicative visualizations that are responsive to varying screen sizes. However, relatively little design guidance or tooling is currently available to authors. We contribute a detailed characterization of responsive visualization strategies in communication‐oriented visualizations, identifying 76 total strategies by analyzing 378 pairs of large screen (LS) and small screen (SS) visualizations from online articles and reports. Our analysis distinguishes between the Targets of responsive visualization, referring to what elements of a design are changed and Actions representing how targets are changed. We identify key trade‐offs related to authors' need to maintain graphical density, referring to the amount of information per pixel, while also maintaining the “message” or intended takeaways for users of a visualization. We discuss implications of our findings for future visualization tool design to support responsive transformation of visualization designs, including requirements for automated recommenders for communication‐oriented responsive visualizations.