Line charts are fundamental to dataanalysis and exploration, offering concise visual representations of trends. However, gaining access to the underlying data used to construct these charts is often challenging. In t...
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Many web-based systems such as online retail, information systems or search engines track the interactions users have with them. Tracked data can comprise high-level information like dwelling time, reviewed items, and...
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ISBN:
(纸本)9798400704666
Many web-based systems such as online retail, information systems or search engines track the interactions users have with them. Tracked data can comprise high-level information like dwelling time, reviewed items, and clicked elements, but also fine-grained information in the form of mouse trajectories and keystrokes. While these data are often fed into user- or behavior models in recommender systems, there are few approaches for interactive visualexploration of multi-modal and complex interaction patterns. Yet, the thorough analysis could reveal important insights for the design and evaluation of said models. We propose a suitable visualanalysis approach that allows to validate and correct models in an intuitive and interactive manner. Our tool provides insights into concrete user (inter)actions and also estimates more complex behavioral patterns. Level of detail views in our system outlines the certainty of detected behaviors and serve the explainability. Our approach can help engineers to understand user interactions and improve behavioral models.
Interaction is critical for dataanalysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Int...
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Interaction is critical for dataanalysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.
visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promising zero-shot results in various natu...
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ISBN:
(纸本)9798350353006
visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promising zero-shot results in various natural language processing tasks but is still unexplored in vision emotion understanding. In this work, we focus on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. Initially, we identify key visual clues critical to visual emotion recognition. Subsequently, we introduce a novel GPT-assisted pipeline for generating emotion visual instruction data, effectively addressing the scarcity of annotated instruction data in this domain. Expanding on the groundwork established by InstructBLIP, our proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the powerful capabilities of Large Language Models to enhance performance. Through extensive experiments, our model showcases its proficiency in emotion classification, adeptness in affective reasoning, and competence in comprehending humor. The comparative analysis provides a robust benchmark for Emotion visual Instruction Tuning in the era of LLMs, providing valuable insights and opening avenues for future exploration in this domain. Our code is available at https://***/aimmemotion/EmoVIT.
Big dataanalysis and insight extraction are critical in contemporary large-scale criminal investigations. Analyzing large sets of information related to criminal activities can assist in the identification of correla...
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Artificial Intelligence (AI) tools have recently gained widespread interest for image creation, but tool developers have largely focused on technical capabilities or specialized domain uses, rather than visual artists...
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ISBN:
(纸本)9798400718281
Artificial Intelligence (AI) tools have recently gained widespread interest for image creation, but tool developers have largely focused on technical capabilities or specialized domain uses, rather than visual artists as users. We collected survey data from 89 practising visual artists and conducted follow-up interviews with 30 of them, to better understand their diverse needs and values. Through reflexive thematic analysis, we explored visual artists' attitudes towards collaboration in art creation both with human artists and with AI- and other technology-based support systems. Our results suggest that the focus of popular AI tools on high-quality, finished images does not meet the needs of visual artists. Instead, they wanted reference images, ideation support, and variant exploration. We identified similarities and differences between how visual artists view collaboration with other artists or with machine support, enabling designers of new tools to adopt a more user-centered approach.
In contrast to conventional networks, Software-Defined Networking has revolutionized network control by separating data and control layers, introducing a dynamic and programmable infrastructure. Despite its benefits, ...
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ISBN:
(纸本)9783031770425;9783031770432
In contrast to conventional networks, Software-Defined Networking has revolutionized network control by separating data and control layers, introducing a dynamic and programmable infrastructure. Despite its benefits, SDN faces various security challenges. This study investigates specific attack vectors, such as DDoS/DoS attacks, topology poisoning, and malicious applications, along with machine learning applications that exhibit several benefits in addressing SDN-specific vulnerabilities and attacks. The study emphasizes the need for investigations and contributions directly addressing unique security challenges across SDN planes, interfaces, and attack categories with the help of ML techniques.
This study is devoted to the development and application of dataanalysis and visualization software in the process of oil testing, aiming at improving the understanding of underground reservoirs and the accuracy of r...
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The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows o...
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ISBN:
(纸本)9798350325577
The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows of users, it is critical to first gain insights into the diverse workflows and the limitations of existing genomics tools for supporting them. In this paper, we conducted semi-structured interviews (N=9) to understand the role of visualization in genomics dataanalysis workflows. Our main goals were to identify various genomics workflows, from dataanalysis to visualexploration and presentation, and to observe challenges that genomics analysts encounter in these workflows when using existing tools. Through the interviews, we found several unique characteristics of genomics workflows, such as the use of multiple visualization tools and many repetitive tasks, which can significantly affect the overall performance. Based on our findings, we discuss implications for designing effective visualization authoring tools that tightly support genomics workflows, such as supporting automation and reproducibility.
This Paper introduces a novel Enhanced visual Retrieval and analysis for Charging Efficiency (EVRACE) system, which presents a novel two-stage framework for visual retrieval and advanced analytics for massive charging...
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