Kimberly Garcia
Last Name
Garcia
First name
Kimberly
Email
kimberly.garcia@unisg.ch
25 results
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Item type:Publication, Scrutinizing Systemic Risks in Personalized Recommender Systems Through Sock-Puppet Auditing of VLOPs(ACM, 2026-01-30); ; ; ; Very Large Online Platforms (VLOPs) use personalized recommender systems to optimize their main performance metric: attention-based user engagement. In doing so, these systems might however amplify systemic risks by promoting controversial or polarizing content, thereby exacerbating issues such as misinformation, societal polarization, and the manipulation of civic discourse. To mitigate these risks, regulations such as the European Union's Digital Services Act (DSA) mandate increased data access and transparency, including for the auditing of personalized recommender systems. However, the data access provided by VLOPs remains limited-often restricted to specific user demographics, aggregate statistics, or curated datasets-hindering meaningful oversight. Consequently, new methods are needed to audit recommender systems effectively at the user level. In this paper, based on an analysis of the legal context and technical alternatives for data access, we present SOAP, the System for Observing and Analyzing Posts. SOAP is an open-source framework for auditing recommender systems using sock-puppet accounts. It enables fine-grained user-level analysis beyond the constrained data access typically provided by platforms. We detail SOAP's technical implementation and evaluate its ability to scrutinize systemic risks. Additionally, we tested SOAP in a workshop with over 100 participants and observed a measurable increase in participants' algorithmic literacy. This demonstrates SOAP's potential not only for research and regulatory auditing, but also as an educational framework to foster public awareness of algorithmic influence.Type:journal articleJournal:ACM Transactions on Recommender SystemsDOI:10.1145/3795516 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, FoodCoach: Fully Automated Diet Counseling(2025-02-11); ; Scopus© Citations 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Digital Companion Architecture for Ambient Intelligence(Association for Computing Machinery (ACM), 2024-05-13); Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gaze-enabled activity recognition for augmented reality feedback(2024-03-16); ; ; ; Andrew DuchowskiHead-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material.Type:journal articleJournal:Computers & GraphicsVolume:119Issue:Special Section on Eye Gaze VISAScopus© Citations 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MR Object Identification and Interaction: Fusing Object Situation Information from Heterogeneous Sources(ACM, 2023-09-28); ; The increasing number of objects in ubiquitous computing environments creates a need for effective object detection and identification mechanisms that permit users to intuitively initiate interactions with these objects. While multiple approaches to such object detection-including through visual object detection, fiducial markers, relative localization, or absolute spatial referencing-are available, each of these suffers from drawbacks that limit their applicability. In this paper, we propose ODIF, an architecture that permits the fusion of object situation information from such heterogeneous sources and that remains vertically and horizontally modular to allow extending and upgrading systems that are constructed accordingly. We furthermore present BLEARVIS, a prototype system that builds on the proposed architecture and integrates computer-vision (CV) based object detection with radio-frequency (RF) angle of arrival (AoA) estimation to identify BLE-tagged objects. In our system, the front camera of a Mixed Reality (MR) head-mounted display (HMD) provides a live image stream to a vision-based object detection module, while an antenna array that is mounted on the HMD collects AoA information from ambient devices. In this way, BLEARVIS is able to differentiate between visually identical objects in the same environment and can provide an MR overlay of information (data and controls) that relates to them. We include experimental evaluations of both, the CV-based object detection and the RF-based AoA estimation, and discuss the applicability of the combined RF and CV pipelines in different ubiquitous computing scenarios. This research can form a starting point to spawn the integration of diverse object detection, identification, and interaction approaches that function across the electromagnetic spectrum, and beyond.Type:journal articleJournal:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)Volume:7Issue:3DOI:10.1145/3610879Scopus© Citations 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, SAKE: A Semantic Authoring and Annotation Tool for Knowledge Extraction(2024-09-24); ; Greenhouse Gas (GHG) accounting is traditionally a lengthy and manual process that requires the expertise of experienced environmental scientists; due to the recognition of the climate crisis through upcoming regulations on GHG accounting around the planet, the demand for tools that can support these environmental experts and accelerate their work is growing considerably at the moment. GHG accounting is merely one application of automated support tools that require the preservation of expert knowledge in a machine-readable and machine-understandable format; across fields, this is highly relevant for automating processes that today can only be performed by individuals with specialized training. In this paper, we present SAKE, a Semantic Authoring and Annotation tool for Knowledge Extraction that allows domain experts with no proficiency in semantic technologies annotating domainspecific PDF files, creating a Knowledge Graph with instances of standardized (or new) ontologies. The resulting Knowledge Graph can then be integrated into systems to automate specialized processes. SAKE has been developed together with domain experts in the field of environmental science and is currently used in the scope of a joint project on GHG accounting.Type:conference paperJournal:Posters, Demos, Workshops, and Tutorials of the 20th International Conference on Semantic Systems (SEMANTiCS 2024) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Temporal Scene Understanding using Contextually Unique Identification(2024-10-28); ; ; Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Temporal Scene Understanding using Contextually Unique IdentificationHumans can easily comprehend and explain the dynamics of a scene by observing the evolution of relationships among identified objects over time-while research towards Hybrid Intelligence promotes the integration of human and machine capabilities, this ability is currently beyond the capabilities of automated systems. Toward realizing it in automated scene understanding systems, we present interpretable Object Identification using Contextual Information System (OIC). Given a video, OIC detects, identifies, and tracks objects and their relationships over time to answer questions about the analyzed scene. Moreover, OIC makes predictions and infers the actors' intentions in a scene. To achieve this, our approach generates a scene graph containing classified objects and their semantic relationships. It then computes a Frame Graph by adding Contextually Unique IDentifiers (CUIDs) to each of the detected objects in the scene graph; the CUIDs permit tracking multiple object instances over time, even if the objects are visually identical. The CUIDs are then used to connect objects across a sequence of Frame Graphs, generating a Temporal Graph. This graph is exported as a cue for a pretrained Large Language Model to provide assistance and answer user questions. OIC's modular architecture enables simple comprehension and swapping of its components, making OIC more interpretable and maintainable than end-to-end scene understanding systems. Our quantitative and qualitative evaluation results demonstrate the effectiveness of OIC as a viable next step toward interpretable automated scene understanding systems.Type:conference paperJournal:2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Spectrum of Proactive Functioning in Digital CompanionsThe future of proactive Digital Companions (DCs)—smart agents capable of assisting and protecting their users—lies in their ability to collaborate effectively with users, learn their preferences, and adjust their behavior according to the user’s current state and their environment. To achieve this, DCs must carefully strike a balance between acting autonomously, while keeping users informed to minimize inconveniences, thereby enhancing user acceptance. In this article, we conduct a user survey to enrich the architecture of proactive personal DCs that explores the trade-off between full autonomous functioning and ensuring sufficient user control in various critical and non-critical scenarios. Our findings indicate that most of the participants lean towards having control over the actions of DCs, and will actively collaborate with those systems in everyday situations, in which decisions are not urgent. However, participants would not mind yielding their control to DCs in time-sensitive or urgent scenarios. Furthermore, in our survey, participants highlighted the importance of explaining such actions well. Given the results from our survey, we implemented a system prototype with the enriched architecture of an explainable and unobtrusive proactive DC for a smart home environment. The actions of this DC are not always fully autonomous and follow our survey findings.Type:conference paperScopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Spectrum of Proactive Functioning in Digital CompanionsThe future of proactive Digital Companions (DCs)-smart agents capable of assisting and protecting their users-lies in their ability to collaborate effectively with users, learn their preferences, and adjust their behavior according to the user's current state and their environment. To achieve this, DCs must carefully strike a balance between acting autonomously, while keeping users informed to minimize inconveniences, thereby enhancing user acceptance. In this article, we conduct a user survey to enrich the architecture of proactive personal DCs that explores the trade-off between full autonomous functioning and ensuring sufficient user control in various critical and non-critical scenarios. Our findings indicate that most of the participants lean towards having control over the actions of DCs, and will actively collaborate with those systems in everyday situations, in which decisions are not urgent. However, participants would not mind yielding their control to DCs in time-sensitive or urgent scenarios. Furthermore, in our survey, participants highlighted the importance of explaining such actions well. Given the results from our survey, we implemented a system prototype with the enriched architecture of an explainable and unobtrusive proactive DC for a smart home environment. The actions of this DC are not always fully autonomous and follow our survey findings.Type:conference paperJournal:Proceedings of the International Conference on Mobile and Ubiquitous MultimediaScopus© Citations 2
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