Tootfinder

PhD position Geographic Information Visualization and Analysis (GIVA) “GeoNavLearn” with Prof. Sara I. Fabrikant
https://jobs.uzh.ch/job-vacancies/phd-position-geographic-informa…

UZH: PhD position Geographic Information Visualization and Analysis (GIVA) “GeoNavLearn”
With 28,000 students and 9,000 employees, the University of Zurich (UZH) is the biggest university in Switzerland. As a member of the League of European Research Universities, it counts amongst the leading European universities. The UZH offers a culturally diverse and inspiring working environment and promotes international collaboration in research. The Geographic Information Visualization and Analysis (GIVA) group at the University of Zurich, Faculty of Science, invites applications for a 4-y…

Salesforce launches Agentforce 3 with an observability tool called Command Center and MCP support, and says 8,000 customers have signed up to deploy Agentforce (Larry Dignan/Constellation Research)
https://www.constellationr.com/blog-news/i

Salesforce launches Agentforce 3, Command Center for visibility
Salesforce launched Agentforce 3, which features Agentforce Command Center, support for Model Context Protocol (MCP), and updated Atlas architecture to speed up reasoning and performance. The rollout maintains Salesforce's Agentforce cadence, which includes updates every few months as the company learns from enterprise use cases. In addition, Salesforce has added more than 30 partners to AgentExchange including AWS, Box, Cisco, Google Cloud, IBM and payments players such as PayPal and Stripe.

PRISON: Unmasking the Criminal Potential of Large Language Models
Xinyi Wu, Geng Hong, Pei Chen, Yueyue Chen, Xudong Pan, Min Yang
https://arxiv.org/abs/2506.16150

PRISON: Unmasking the Criminal Potential of Large Language Models
As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions. We propose a unified framework PRISON, to quantify LLMs' criminal potential across five dimensions: False Statements, Frame-Up, Psychological Manipulation, Emotional Disguise, and Moral Disengagement. Using structured crime scenarios adapted from classic film…

An Expert Survey on Models and Digital Twins
Jonathan Reif, Daniel Dittler, Milapji Singh Gill, Tam\'as Farkas, Valentin Stegmaier, Felix Gehlhoff, Tobias Kleinert, Michael Weyrich
https://arxiv.org/abs/2506.17313

An Expert Survey on Models and Digital Twins
Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made possible by integrating various Digital Models (DMs) within DTs, which must interoperate to represent different system aspects and fulfill diverse application purposes. However, industry perspectives on the challenges and research needs for integrating these models are rarely obtained. Thus, this study conducts an ex…

Toward Understanding Similarity of Visualization Techniques
Abdulhaq Adetunji Salako, Christian Tominski
https://arxiv.org/abs/2506.17032 https://

Toward Understanding Similarity of Visualization Techniques
The literature describes many visualization techniques for different types of data, tasks, and application contexts, and new techniques are proposed on a regular basis. Visualization surveys try to capture the immense space of techniques and structure it with meaningful categorizations. Yet, it remains difficult to understand the similarity of visualization techniques in general. We approach this open research question from two angles. First, we follow a model-driven approach that is based on d…

Large Language Models as Psychological Simulators: A Methodological Guide
Zhicheng Lin
https://arxiv.org/abs/2506.16702 https://arxiv…

Large Language Models as Psychological Simulators: A Methodological Guide
Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary applications: simulating roles and personas to explore diverse contexts, and serving as computational models to investigate cognitive processes. For simulation, we present methods for developing psychologically grounded personas that move beyond demographic cate…

Refining Ray-Tracing Accuracy and Efficiency in the Context of FRMCS Urban Railway Channel Predictions
Romain Charbonnier, Thierry Tenoux, Yoann Corre
https://arxiv.org/abs/2506.16236

Refining Ray-Tracing Accuracy and Efficiency in the Context of FRMCS Urban Railway Channel Predictions
The upcoming roll-out of the new wireless communication standard for wireless railway services, FRMCS, requires a thorough understanding of the system performance in real-world conditions, since this will strongly influence the deployment costs and the effectiveness of an infrastructure planned for decades. The virtual testing of the equipment and network performance in realistic simulated scenarios is key; its accuracy depends on the reliability of the predicted radio channel properties. In th…

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes, Georgios Kaissis, Emiliano De Cristofaro
https://arxiv.org/abs/2506.16666

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
This paper systematizes research on auditing Differential Privacy (DP) techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target--namely, efficiency, end-to-end-ness, and tightness. Then, we systematize the modes of operation of state-of-the-art DP auditing techniques, including threat models, attacks,…

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…

Sharpening Kubernetes Audit Logs with Context Awareness
Matteo Franzil, Valentino Armani, Luis Augusto Dias Knob, Domenico Siracusa
https://arxiv.org/abs/2506.16328

Sharpening Kubernetes Audit Logs with Context Awareness
Kubernetes has emerged as the de facto orchestrator of microservices, providing scalability and extensibility to a highly dynamic environment. It builds an intricate and deeply connected system that requires extensive monitoring capabilities to be properly managed. To this account, K8s natively offers audit logs, a powerful feature for tracking API interactions in the cluster. Audit logs provide a detailed and chronological record of all activities in the system. Unfortunately, K8s auditing suf…