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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., Comment: 19 pages, 19 tables, 4 figures. Accepted at ACH CHI 2024

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'' $\epsilon$ 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 $\epsilon$ allotments across queries prior to an analysis. To support analysts in spending $\epsilon$ efficiently, we propose a new interactive analysis paradigm, Measure-Observe-Remeasure, where analysts ``measure'' the database with a limited amount of $\epsilon$, observe estimates and their errors, and remeasure with more $\epsilon$ as needed. We instantiate the paradigm in an interactive visualization interface which allows analysts to spend increasing amounts of $\epsilon$ 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 $\epsilon$ 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 $\epsilon$ 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 $\epsilon$., Comment: Published in IEEE Symposium on Security and Privacy (SP) 2024

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., Comment: 26 pages, 1 figure, 2 tables. Accepted to PACM HCI (CSCW 2024)

In enterprise organizations, data-driven decision making processes include the use of business intelligence dashboards and collaborative deliberation on communication platforms such as Slack or Teams. However, apart from those in data analyst roles, there is shallow engagement with dashboard content due to insufficient guidance, context, or access. Through a co-design study with nine enterprise professionals who use dashboard content to communicate with their colleagues, we identified design requirements for sharing selections from dashboards as interactive snapshots on collaboration platforms. We then developed Philo, an interactive demonstration environment centered around the template-based retargeting of dashboard content. Using Philo as a design probe, we interviewed our co-design participants and six additional data professionals, ultimately arriving at a set of design guidelines for fostering conversations around data in enterprise settings.

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 mixed-initiative 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., Comment: 10 pages, 5 figures, accepted to IEEE VIS 2023

As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning models for time series forecasting of household energy consumption. Specifically, we use Weka, a data mining tool to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. The models applied are: Multilayer Perceptron, K Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.

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., Comment: 14 pages, 15 figures, accepted to CHI 2022

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., Comment: 15 pages, 2 figures, 1 table, accepted for CHI 2022

Recently, indoor organic photovoltaics (OPVs) has attracted substantial research attention, due to the emergence of self-powered electronic devices for Internet-of-Things (IoT) applications. This progress report discusses recent developments in indoor OPVs, focusing on the strategic role of synergistic parasitic resistance in suppressing the leakage current to achieve high indoor efficiencies. Moreover, an underexplored area is presented, namely the impact of optical modulation on enhancing light absorption in indoor OPVs. First, the main advances in material design for indoor OPVs are briefly presented. This is followed by detailed discussions of the crucial strategies, including interfacial engineering, the effect of photoactive layer thickness, and the effectiveness of transparent conducting electrodes for improving the OPV performance. Overall, this review highlights that understanding the indispensable role of parasitic resistance under dim light conditions may provide new opportunities for developing efficient indoor OPVs for practical applications. Finally, after summarizing recent progress in indoor OPVs, a critical perspective is provided. © 2021 Wiley-VCH GmbH

Recently, indoor organic photovoltaics (OPVs) has attracted substantial research attention, due to the emergence of self-powered electronic devices for Internet-of-Things (IoT) applications. This progress report discusses recent developments in indoor OPVs, focusing on the strategic role of synergistic parasitic resistance in suppressing the leakage current to achieve high indoor efficiencies. Moreover, an underexplored area is presented, namely the impact of optical modulation on enhancing light absorption in indoor OPVs. First, the main advances in material design for indoor OPVs are briefly presented. This is followed by detailed discussions of the crucial strategies, including interfacial engineering, the effect of photoactive layer thickness, and the effectiveness of transparent conducting electrodes for improving the OPV performance. Overall, this review highlights that understanding the indispensable role of parasitic resistance under dim light conditions may provide new opportunities for developing efficient indoor OPVs for practical applications. Finally, after summarizing recent progress in indoor OPVs, a critical perspective is provided. © 2021 Wiley-VCH GmbH

Recently, indoor organic photovoltaics (OPVs) has attracted substantial research attention, due to the emergence of self-powered electronic devices for Internet-of-Things (IoT) applications. This progress report discusses recent developments in indoor OPVs, focusing on the strategic role of synergistic parasitic resistance in suppressing the leakage current to achieve high indoor efficiencies. Moreover, an underexplored area is presented, namely the impact of optical modulation on enhancing light absorption in indoor OPVs. First, the main advances in material design for indoor OPVs are briefly presented. This is followed by detailed discussions of the crucial strategies, including interfacial engineering, the effect of photoactive layer thickness, and the effectiveness of transparent conducting electrodes for improving the OPV performance. Overall, this review highlights that understanding the indispensable role of parasitic resistance under dim light conditions may provide new opportunities for developing efficient indoor OPVs for practical applications. Finally, after summarizing recent progress in indoor OPVs, a critical perspective is provided. © 2021 Wiley-VCH GmbH

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., Comment: 9 pages, 11 figures, 2 tables. Accepted at IEEE VIS 2021

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., Comment: 10 pages, 9 figures, accepted to Computer Graphics Forum (EuroVis 2021)

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers' thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit $100\%$ accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use $60.84\%$ fewer simulations than the brute-force method., Comment: Published (https://www.mdpi.com/1996-1073/13/7/1726), Code (https://github.com/gcunhase/GeneticAlgorithm-SolarCells)