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Reminiscences on Influential Papers
No full textThis issue's contributors cover the impact of paying attention to the low-level implementation details, a paradigm shift in the way we approach stream processing, and the value of combining theoretical analysis with experimental evaluation. Furthermore, one of our contributors, rather than picking one paper, highlights the importance of putting the time to practice reading, reviewing, and learning from papers, not only from one's own field of interest but also from other fields. VLDB, similar to some systems conferences, launched a Shadow Program Committee for this purpose following the VLDB 2026 (Vol 19) submission cycles. We wish to continue this effort in the future VLDB cycles. Enjoy reading!While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested
The role of interface design on prompt-mediated creativity in Generative AI
Get PDFGenerative AI for the creation of images is becoming a staple in the toolkit of digital artists and visual designers. The interaction with these systems is mediated by prompting, a process in which users write a short text to describe the desired image’s content and style. The study of prompts offers an unprecedented opportunity to gain insight into the process of human creativity. Yet, our understanding of how people use them remains limited. We analyze more than 145,000 prompts from the logs of two Generative AI platforms (Stable Diffusion and Pick-a-Pic) to shed light on how people explore new concepts over time, and how their exploration might be influenced by different design choices in human-computer interfaces to Generative AI. We find that users exhibit a tendency towards exploration of new topics over exploitation of concepts visited previously. However, a comparative analysis of the two platforms, which differ both in scope and functionalities, reveals some stark differences. Features diverting user focus from prompting and providing instead shortcuts for quickly generating image variants are associated with a considerable reduction in both exploration of novel concepts and detail in the submitted prompts. These results carry direct implications for the design of human interfaces to Generative AI and raise new questions regarding how the process of prompting should be aided in ways that best support creativity
Counting Small Induced Subgraphs: Scorpions Are Easy but Not Trivial
No full textIn the parameterized problem #IndSub(Φ) for fixed graph properties Φ, given as input a graph G and an integer k, the task is to compute the number of induced k-vertex subgraphs satisfying Φ. Dörfler et al. [Algorithmica 2022] and Roth et al. [SICOMP 2024] conjectured that #IndSub(Φ) is #W[1]-hard for all non-meager properties Φ, i.e., properties that are nontrivial for infinitely many k. This conjecture has been confirmed for several restricted types of properties, including all hereditary properties [STOC 2022] and all edge-monotone properties [STOC 2024].We refute this conjecture by showing that induced k-vertex graphs that are scorpions can be counted in time O(n⁴) for all k. Scorpions were introduced more than 50 years ago in the context of the evasiveness conjecture. A simple variant of this construction results in graph properties that achieve arbitrary intermediate complexity assuming ETH.Moreover, we formulate an updated conjecture on the complexity of #IndSub(Φ) that correctly captures the complexity status of scorpions and related constructions
Hannah Devinney. Gender and Representation: Investigations of Bias in Natural Language Processing
No full textSurtr: Transparent Verification with Simple yet Strong Coercion Mitigation
Get PDFTransparent verification allows voters to directly identify their vote in cleartext in the final tally result. Both Selene and Hyperion offer this simple and intuitive verification method, and at the same time allow for coercion to be mitigated under the assumption that tally servers can privately notify voters of the keying material needed for verification. Subsequently, a voter can generate fake keying material to deceive a coercer. In this paper, we propose Surtr, a new scheme that enables transparent verification without requiring a private notification channel. This approach strengthens coercion mitigation, since a coercer can monitor the notification channel, and simplifies the process by eliminating the need for voters to generate fake keying material for the coercer
Tracing Human-AI Relations: A Participatory Approach to GenAI Integration in Creative Public Service Work
No full textThis paper examines a participatory process for integrating Generative AI (GenAI) into the creative work of a public service organization. Through this process, we gain insights into the complexities of human-AI relations, the evolving nature of creative work, and the conditions necessary for meaningful AI integration in service organizations. The paper makes two key contributions: it documents a case of participatory GenAI integration in a public service setting, and it explores implications for creative practice and the evolving role of human-AI collaboration. Findings highlight the importance of reflection, value-driven AI integration, and the need for facilitated spaces for critical reflection. The study contributes to ongoing discussions on how service organizations and designers can engage with AI in ways that align with professional identity, ethics, and creative agency
Radioactive Eye Information:Guarding Eye-Image Datasets through Radioactive Watermarking for Unauthorized-Use Detection
No full textThis paper explores radioactive watermarking as a technique for embedding invisible information in eye-tracking data, ensuring that any model trained on the modified samples retains an identifiable mark. Large-scale datasets have enabled robust deep learning models for appearance-based gaze estimation, but no reliable methods currently exist to detect unauthorized use of datasets. To address this, we evaluate radioactive watermarking, which embeds a watermark into eye data using pre-trained convolutional neural networks commonly used in gaze estimation models. We assess watermark robustness through gaze classification experiments, testing multiple neural architectures in different embedding and detection setups. Results demonstrate that training with watermarked data can be detected with high confidence, depending on the proportion of watermarked samples and the training setup. Detection is reliable with at least 10% watermarked data, while exceeding 15% degrades performance without significantly improving detection. Watermarks that retain high image quality preserve network performance and enable consistent detection
BikeNodePlanner: A data-driven decision support tool for bicycle node network planning
No full textA bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: A fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling
Uncovering large inconsistencies between machine learning derived gridded settlement datasets
No full textHigh-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes