arXiv subjects

Jaime Ruiz

Publications and source records attributed to Jaime Ruiz.

4 recordsLinked to original sources

arXiv2025

Natural Language Interaction for Editing Visual Knowledge Graphs

Knowledge graphs are often visualized using node-link diagrams that reveal relationships and structure. In many applications using graphs, it is desirable to allow users to edit graphs to ensure data accuracy or provides updates. Commonly in graph visualization, users can interact directly with the visual elements by clicking and typing updates to specific items through traditional interaction methods in the graphical user interface. However, it can become tedious to make many updates due to the need to individually select and change numerous items in a graph. Our research investigates natural language input as an alternative method for editing network graphs. We present a user study comparing GUI graph editing with two natural language alternatives to contribute novel empirical data of the trade-offs of the different interaction methods. The findings show natural language methods to be significantly more effective than traditional GUI interaction.

cs.HC↗

arXiv2024

XLR (piXel Loss Rate): a Lightweight Indicator to Measure Video QoE in IP Networks

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstream-based estimation.

eess.IV↗

arXiv2023

A Categorical Representation Language and Computational System for Knowledge-Based Planning

Classical planning representation languages based on first-order logic have preliminarily been used to model and solve robotic task planning problems. Wider adoption of these representation languages, however, is hindered by the limitations present when managing implicit world changes with concise action models. To address this problem, we propose an alternative approach to representing and managing updates to world states during planning. Based on the category-theoretic concepts of $\mathsf{C}$-sets and double-pushout rewriting (DPO), our proposed representation can effectively handle structured knowledge about world states that support domain abstractions at all levels. It formalizes the semantics of predicates according to a user-provided ontology and preserves the semantics when transitioning between world states. This method provides a formal semantics for using knowledge graphs and relational databases to model world states and updates in planning. In this paper, we conceptually compare our category-theoretic representation with the classical planning representation. We show that our proposed representation has advantages over the classical representation in terms of handling implicit preconditions and effects, and provides a more structured framework in which to model and solve planning problems.

cs.AI↗

arXiv2023

Multimodal User Authentication in Smart Environments: Survey of User Attitudes

As users shift from interacting actively with devices with screens to interacting seamlessly with smart environments, novel models of user authentication will be needed to maintain the security and privacy of user data. To understand users' attitudes toward new models of authentication (e.g., voice recognition), we surveyed 117 Amazon Turk workers and 43 computer science students about their authentication preferences, in contexts when others are present and different usability metrics. Our users placed less trust in natural authentication modalities (e.g., body gestures) than traditional modalities (e.g., passwords) due to concerns about accuracy or security. Users were also not as willing to use natural authentication modalities except in the presence of people they trust due to risk of exposure and feelings of awkwardness. We discuss the implications for designing natural multimodal authentication and explore the design space around users' current mental models for the future of secure and usable smart technology.

cs.HC↗