Shah Rukh Humayoun

Syed Ahsan Ali Dilawer, Shah Rukh Humayoun

Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.

Akila de Silva, Isabel Hyo Jung Song, Hui Yang et al.

Generative artificial intelligence (GenAI) tools are increasingly present in higher education, yet their adoption has been largely student-driven, requiring instructors to respond to technologies already embedded in classroom practices. While some faculty have embraced GenAI for pedagogical purposes such as content generation, assessment support, and curriculum design, others approach these tools with caution, citing concerns about student learning, assessment validity, and academic integrity. Understanding faculty perspectives is therefore essential for informing effective pedagogical strategies and institutional policies. In this paper, we present findings from a focus group study with 29 STEM faculty members at a large public university in the United States. We examine how faculty integrate GenAI into their courses, the benefits and challenges they perceive for student learning, and the institutional support they identify as necessary for effective and responsible adoption. Our findings highlight key patterns in how STEM faculty engage with GenAI, reflecting both active adoption and cautious use. Faculty described a range of pedagogical applications alongside concerns about student learning, assessment, and academic integrity. Overall, the results suggest that effective integration of GenAI in higher education requires rethinking assessment, pedagogy, and institutional governance in addition to technical adoption.

Nina Mir, Ragaad AlTarawneh, Shah Rukh Humayoun

The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of classes and inter-model comparison in such classification problems are two areas that have not received much attention in the literature, despite the ever-increasing use of classification models to address problems with very large class categories. In this paper, we present our interactive visual analytics tool, called Circles, that allows a visual inter-model comparison of numerous classification models with 1K classes in one view. To mitigate the tricky issue of visual clutter, we chose concentric a radial line layout for our inter-model comparison task. Our prototype shows the results of 9 models with 1K classes

Shah Rukh Humayoun, Ibrahim Mansour, Ragaad AlTarawneh

Recently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in understanding the changes in people's feedback and reaction over time. Targeting this, we present our visual analytics tool, called TEVisE. It uses an enhanced adjacency matrix diagram to overcome the cluttering problem in TExVis and visualizes the evolution of frequent keywords and the relations between these keywords over time. We conducted two user studies to find answers of our two formulated research questions. In the first user study, we focused on evaluating the used visualization layouts in both tools from the perspectives of common usability metrics and cognitive load theory. We found better accuracy in our TEVisE tool for tasks related to reading exploring relations between frequent keywords. In the second study, we collected users' feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. In the second study, we collected users' feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. We found that participants preferred both view, one to get overall glance while the other to get the trends changes over time.

Dylan Cashman, Shenyu Xu, Subhajit Das et al.

Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.

Razan N. Jaber, Ragaad AlTarawneh, Shah Rukh Humayoun

Recent advancements in mobile devices encourage researchers to utilize them in collaborative environments as a medium to interact with large shared wall-displays. In this paper, we focus on a semi-controlled user study that we conducted to measure the collaborative coupling ratio between partners working in pairs in a collaborative setup equipped with a shared tiled-wall display and multiple mobile devices. We invited 36 participants in 18 pairs to take part in our experiment in order to analyze how they communicate and collaborate with each other during the experiment. We observed their collaborative coupling by measuring how often they verbally and visually communicated. Further, we found frequently used collaborative physical position patterns by observing the pairs' physical arrangements and standing positions. Moreover, we combined these factors to gain a clearer understanding of coupling in our setup, taking into account the mobility factor offered by the mobile devices. Results of the study show interesting findings about the coupling factors between the partners mainly due to the flexibility offered by including mobile devices in our collaborative setup.

Dylan Cashman, Shah Rukh Humayoun, Florian Heimerl et al.

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.

Ragaad AlTarawneh, Jens Bauer, Nicole Menck et al.

In this position paper, we present our approach of utilizing mobile devices (i.e., mobile phones and tablets) for assisting engineers and experts in understanding and maintaining the factory pipelines. For this, we present a platform, called assistME, that is composed of three main components: the assistME Server, the assistME mobile infrastructure, and the co-assistME collaborative environment. In order to get full utilization of the assistME platform, we assume that an initial setup is made in the factory in such a way that it is equipped with different sensors to collect data about specific events in the factory pipeline together with the corresponding locations of these events. The assistME Server works as a central control unit in the platform and collects data from the installed sensors in the factory pipeline. In the case of any unexpected behavior or any critical situation in the factory pipeline, notification and other details are sent to the related group of engineers and experts through the assistME mobile app. Further, the co-assistME collaborative environment, equipped with a large shared screen and multiple mobile devices, helps the engineers and experts to collaborate with to understand and analyze the current situation in the factory pipeline in order to maintain it accurately.