Browsing by Author "El-Assady, Mennatallah"
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Item CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling(The Eurographics Association and John Wiley & Sons Ltd., 2021) Fischer, Maximilian T.; Seebacher, Daniel; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonCommunication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.Item CorpusVis: Visual Analysis of Digital Sheet Music Collections(The Eurographics Association and John Wiley & Sons Ltd., 2022) Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, TobiasManually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair-analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.Item Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models(The Eurographics Association and John Wiley & Sons Ltd., 2023) Schetinger, Victor; Bartolomeo, Sara Di; El-Assady, Mennatallah; McNutt, Andrew; Miller, Matthias; Passos, João Paulo Apolinário; Adams, Jane L.; Bujack, Roxana; Archambault, Daniel; Schreck, TobiasGenerative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains-from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization.We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.Item EuroVa 2023: Frontmatter(The Eurographics Association, 2023) Angelini, Marco; El-Assady, Mennatallah; Angelini, Marco; El-Assady, MennatallahItem The Future of Interactive Data Analysis and Visualization(The Eurographics Association, 2023) Bernard, Jürgen; El-Assady, Mennatallah; Hotz, Ingrid; Schulz, H.-J.The interactive data analysis and visualization (VIS) community has prospered for over thirty years. Generation after generation, the community has evolved its understanding of research problems and, along the way, contributed various techniques, applications, and research methods. While some of the developed techniques have stood the test of time, we will consider what else needs to be remembered or even revitalized from the good old days in this panel. Further, VIS is currently facing exciting times, with great changes and trends within and outside the community. Thus, in this panel, we want to analyze current research trends and discuss our most exciting ideas and directions. Looking ahead, it can already be anticipated that the future of VIS is subject to change. In this panel, we want to map out future research directions for our community. Along these three lines, the guiding theme of our interactive panel will be three types of (provoking) statements: (i) In the good old days, I liked when we did . . . (ii) Currently, a most exciting trend is ... and (iii) In the future, we will be doing . . . Come and join us to reflect on past and present trends, daring a look ahead to an exciting future for the interactive data analysis and visualization community!Item How Effective are Uni- and Multivariate Typographic Encodings? Studying the Usage of FontWeight, Oblique Angle, and Spacing(The Eurographics Association, 2022) Bäuerle, Andreas; Brath, Richard; El-Assady, Mennatallah; Agus, Marco; Aigner, Wolfgang; Hoellt, ThomasText is one of the most commonly used ways to transmit information. It is widely used in various visualizations and determines our understanding of the presented content. The information density of text can be enhanced by visualizing data in typographic attributes, such as font weight, letter spacing, or oblique angle. To increase information density the furthest, without the visualization losing performance or effectiveness, the perceivable granularity of the typographic attributes needs to be known. In an empirical experiment, the number of distinguishable levels in typographic attributes and the effects of changing the associated font size or facilitating multivariate encoding are assessed. Findings facilitate designing information-dense typographic visualizations without decreasing their performance or effectiveness.Item Interaction Tasks for Explainable Recommender Systems(The Eurographics Association, 2023) Al-Hazwani, Ibrahim; Alahmadi, Turki; Wardatzky, Kathrin; Inel, Oana; El-Assady, Mennatallah; Bernard, Jürgen; Gillmann, Christina; Krone, Michael; Lenti, SimoneIn the modern web experience, users interact with various types of recommender systems. In this literature study, we investigate the role of interaction in explainable recommender systems using 27 relevant papers from recommender systems, humancomputer interaction, and visualization fields. We structure interaction approaches into 1) the task, 2) the interaction intent, 3) the interaction technique, and 4) the interaction effect on explainable recommender systems. We present a preliminary interaction taxonomy for designers and developers to improve the interaction design of explainable recommender systems. Findings based on exploiting the descriptive power of the taxonomy emphasize the importance of interaction in creating effective and user-friendly explainable recommender systems.Item Learning and Teaching in Co-Adaptive Guidance for Mixed-Initiative Visual Analytics(The Eurographics Association, 2020) Sperrle, Fabian; Jeitler, Astrik; Bernard, Jürgen; Keim, Daniel A.; El-Assady, Mennatallah; Turkay, Cagatay and Vrotsou, KaterinaGuidance processes in visual analytics applications often lack adaptivity. In this position paper, we contribute the concept of co-adaptive guidance, building on the principles of initiation and adaptation. We argue that both the user and the system adapt their data-, task- and user/system-models over time. Based on these principles, we propose reasoning about the guidance design space through introducing the concepts of learning and teaching that complement the existing dimension of implicit and explicit guidance, thus, deriving the four guidance dynamics user-teaching, system-teaching, user-learning, and system-learning. Finally, we classify current guidance approaches according to the dynamics, demonstrating their applicability to co-adaptive guidance.Item Learning Contextualized User Preferences for Co-Adaptive Guidance in Mixed-Initiative Topic Model Refinement(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sperrle, Fabian; Schäfer, Hanna; Keim, Daniel; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonMixed-initiative visual analytics systems support collaborative human-machine decision-making processes. However, many multiobjective optimization tasks, such as topic model refinement, are highly subjective and context-dependent. Hence, systems need to adapt their optimization suggestions throughout the interactive refinement process to provide efficient guidance. To tackle this challenge, we present a technique for learning context-dependent user preferences and demonstrate its applicability to topic model refinement. We deploy agents with distinct associated optimization strategies that compete for the user's acceptance of their suggestions. To decide when to provide guidance, each agent maintains an intelligible, rule-based classifier over context vectorizations that captures the development of quality metrics between distinct analysis states. By observing implicit and explicit user feedback, agents learn in which contexts to provide their specific guidance operation. An agent in topic model refinement might, for example, learn to react to declining model coherence by suggesting to split a topic. Our results confirm that the rules learned by agents capture contextual user preferences. Further, we show that the learned rules are transferable between similar datasets, avoiding common cold-start problems and enabling a continuous refinement of agents across corpora.Item LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sevastjanova, Rita; Kalouli, Aikaterini-Lida; Beck, Christin; Hauptmann, Hanna; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, TobiasLanguage models, such as BERT, construct multiple, contextualized embeddings for each word occurrence in a corpus. Understanding how the contextualization propagates through the model's layers is crucial for deciding which layers to use for a specific analysis task. Currently, most embedding spaces are explained by probing classifiers; however, some findings remain inconclusive. In this paper, we present LMFingerprints, a novel scoring-based technique for the explanation of contextualized word embeddings. We introduce two categories of scoring functions, which measure (1) the degree of contextualization, i.e., the layerwise changes in the embedding vectors, and (2) the type of contextualization, i.e., the captured context information. We integrate these scores into an interactive explanation workspace. By combining visual and verbal elements, we provide an overview of contextualization in six popular transformer-based language models. We evaluate hypotheses from the domain of computational linguistics, and our results not only confirm findings from related work but also reveal new aspects about the information captured in the embedding spaces. For instance, we show that while numbers are poorly contextualized, stopwords have an unexpected high contextualization in the models' upper layers, where their neighborhoods shift from similar functionality tokens to tokens that contribute to the meaning of the surrounding sentences.Item A Survey of Human-Centered Evaluations in Human-Centered Machine Learning(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sperrle, Fabian; El-Assady, Mennatallah; Guo, Grace; Borgo, Rita; Chau, Duen Horng; Endert, Alex; Keim, Daniel; Smit, Noeska and Vrotsou, Katerina and Wang, BeiVisual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. The result is a lack of structure and comparability between evaluations. In this survey, we provide a comprehensive overview of evaluations in the field of human-centered machine learning. We particularly focus on human-related factors that influence trust, interpretability, and explainability. We analyze the evaluations presented in papers from top conferences and journals in information visualization and human-computer interaction to provide a systematic review of their setup and findings. From this survey, we distill design dimensions for structured evaluations, identify evaluation gaps, and derive future research opportunities.Item A Typology of Guidance Tasks in Mixed-Initiative Visual Analytics Environments(The Eurographics Association and John Wiley & Sons Ltd., 2022) Pérez-Messina, Ignacio; Ceneda, Davide; El-Assady, Mennatallah; Miksch, Silvia; Sperrle, Fabian; Borgo, Rita; Marai, G. Elisabeta; Schreck, TobiasGuidance has been proposed as a conceptual framework to understand how mixed-initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed-initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed-initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.Item v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions(The Eurographics Association and John Wiley & Sons Ltd., 2020) Blumenschein, Michael; Debbeler, Luka J.; Lages, Nadine C.; Renner, Britta; Keim, Daniel A.; El-Assady, Mennatallah; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, TatianaComparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disciplines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties, comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualizations have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a few tasks, unless they are combined. In this paper, we present the v-plot designer; a technique for authoring custom hybrid charts, combining mirrored bar charts, difference encodings, and violin-style plots. v-plots are customizable and enable the simultaneous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expert survey that compares and evaluates 20 common visualization techniques to derive guidelines for the task-driven selection of appropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v-plots to individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the userguiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic experts.Item VISITOR: Visual Interactive State Sequence Exploration for Reinforcement Learning(The Eurographics Association and John Wiley & Sons Ltd., 2023) Metz, Yannick; Bykovets, Eugene; Joos, Lucas; Keim, Daniel; El-Assady, Mennatallah; Bujack, Roxana; Archambault, Daniel; Schreck, TobiasUnderstanding the behavior of deep reinforcement learning agents is a crucial requirement throughout their development. Existing work has addressed the identification of observable behavioral patterns in state sequences or analysis of isolated internal representations; however, the overall decision-making of deep-learning RL agents remains opaque. To tackle this, we present VISITOR, a visual analytics system enabling the analysis of entire state sequences, the diagnosis of singular predictions, and the comparison between agents. A sequence embedding view enables the multiscale analysis of state sequences, utilizing custom embedding techniques for a stable spatialization of the observations and internal states. We provide multiple layers: (1) a state space embedding, highlighting different groups of states inside the state-action sequences, (2) a trajectory view, emphasizing decision points, (3) a network activation mapping, visualizing the relationship between observations and network activations, (4) a transition embedding, enabling the analysis of state-to-state transitions. The embedding view is accompanied by an interactive reward view that captures the temporal development of metrics, which can be linked directly to states in the embedding. Lastly, a model list allows for the quick comparison of models across multiple metrics. Annotations can be exported to communicate results to different audiences. Our two-stage evaluation with eight experts confirms the effectiveness in identifying states of interest, comparing the quality of policies, and reasoning about the internal decision-making processes.Item Visual Analytics of Conversational Dynamics(The Eurographics Association, 2019) Seebacher, Daniel; Fischer, Maximilian T.; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Landesberger, Tatiana von and Turkay, CagatayLarge-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational dynamics, episodes with low or high communication activity as well as breaks in communication need to be detected to enable the identification of temporal interaction patterns. Traditional episode detection approaches are highly dependent on the choice of parameters, such as window-size or binning-resolution. In this paper, we present a novel technique for the identification of relevant episodes in bi-directional interaction sequences from abstract communication networks. We model communication as a continuous density function, allowing for a more robust segmentation into individual episodes and estimation of communication volume. Additionally, we define a tailored feature set to characterize conversational dynamics and enable a user-steered classification of communication behavior. We apply our technique to a real-world corpus of email data from a large European research institution. The results show that our technique allows users to effectively define, identify, and analyze relevant communication episodes.Item Why am I reading this? Explaining Personalized News Recommender Systems(The Eurographics Association, 2023) Arnórsson, Sverrir; Abeillon, Florian; Al-Hazwani, Ibrahim; Bernard, Jürgen; Hauptmann, Hanna; El-Assady, Mennatallah; Angelini, Marco; El-Assady, MennatallahSocial media and online platforms significantly impact what millions of people get exposed to daily, mainly through recommended content. Hence, recommendation processes have to benefit individuals and society. With this in mind, we present the visual workspace NewsRecXplain, with the goals of (1) explaining and raising awareness about recommender systems, (2) enabling individuals to control and customize news recommendations, and (3) empowering users to contextualize their news recommendations to escape from their filter bubbles. This visual workspace achieves these goals by allowing users to configure their own individualized recommender system, whose news recommendations can then be explained within the workspace by way of embeddings and statistics on content diversity.