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Industrial control systems (ICS) are increasingly targeted by sophisticated attacks on sensors and actuators, necessitating advanced frameworks that enable proactive mitigation. This paper introduces HyTwin, a formal framework that models both adversarial actions and corresponding mitigation strategies through digital twin-based interventions. HyTwin leverages differential dynamic logic (dL) to represent the temporal evolution of attacks and quantify the mitigation horizon, a critical parameter enabling precise reasoning about when and how to deploy fail-safe mechanisms during ongoing attacks. Our approach integrates temporal semantics with attack models to dynamically engage fail-safe controls. This work provides a rigorous framework for designing proactive countermeasures that preserve system safety, ensuring robustness in adversarial scenarios. The proposed framework establishes a foundation for advancing ICS security through verifiable temporal reasoning and contributes to bridging gaps between theoretical modeling and real-world industrial applications. 

Free and open source social platform software has dramatically lowered the barrier to entry for anyone to set up and administer their own social network. This new population of social network administrators thus assume data management responsibilities for sociotechnical systems. Administrators have the power to customize this software, including data collection and data retention, potentially leading to radically different privacy policies. To better understand the characteristics — e.g., the variability, prohibitions, and permissions — of privacy policies on these new social networking platforms, we have conducted a case study of Mastodon. We performed a text analysis of 351 privacy policies and a survey of 104 Mastodon administrators. While most administrators used the default policy that ships with the Mastodon software, we observed that approximately ten percent of our sample tailored their privacy policies to their instances and that some administrators conflated codes of conduct with privacy policies. Our findings suggest the existing market-based individualistic frameworks for thinking about privacy policies do not adequately address this emerging community. 

Modern smart buildings and environments rely on sensory infrastructure to capture and process information about their inhabitants. However, it remains challenging to ensure that this infrastructure complies with privacy norms, preferences, and regulations; individuals occupying smart environments are often occupied with their tasks, lack awareness of the surrounding sensing mechanisms, and are non-technical experts. This problem is only exacerbated by the increasing number of sensors being deployed in these environments, as well as services seeking to use their sensory data. As a result, individuals face an unmanageable number of privacy decisions, preventing them from effectively behaving as their own “privacy firewall” for filtering and managing the multitude of personal information flows. These decisions often require qualitative reasoning over privacy regulations, understanding privacy-sensitive contexts, and applying various privacy transformations when necessary We propose the use of Large Language Models (LLMs), which have demonstrated qualitative reasoning over social/legal norms, sensory data, and program synthesis, all of which are necessary for privacy firewalls. We present PrivacyOracle, a prototype system for configuring privacy firewalls on behalf of users using LLMs, enabling automated privacy decisions in smart built environments. Our evaluation shows that PrivacyOracle achieves up to 

A natural history note describing unusual coloration of a very old female Aspidoscelis gularis that we observed while doing fieldwork in South Texas. 

This is a natural history note that we published based on an observation of a caudal bifurcation in Aspidoscelis gularis that we witnessed during fieldwork in South Texas. 

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle1 to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios2.