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Behavior Trees (BTs) are high-level controllers that are useful in a variety of planning tasks and are gaining traction in robotic mission planning. As they gain popularity in safety-critical domains, it is important to formalize their syntax and semantics, as well as verify properties for them. In this paper, we formalize a class of BTs we call Stateful Behavior Trees (SBTs) that have auxiliary variables and operate in an environment that can change over time. SBTs have access to persistent shared memory (often known as a blackboard) that keeps track of these auxiliary variables. We demonstrate that SBTs are equivalent in computational power to Turing Machines when the blackboard can store mathematical (i.e., unbounded) integers. We further identify syntactic assumptions where SBTs have computational power equivalent to finite state automata, specifically where the auxiliary variables are of finitary types. We present a domain specific language (DSL) for writing SBTs and adapt the tool BehaVerify for use with this DSL. This new DSL in BehaVerify supports interfacing with popular BT libraries in Python, and also provides generation of Haskell code and nuXmv models, the latter of which is used for model checking temporal logic specifications for the SBTs. We include examples and scalability results where BehaVerify outperforms another verification tool by a factor of 100., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Behavior Trees (BTs) are high level controllers that have found use in a wide range of robotics tasks. As they grow in popularity and usage, it is crucial to ensure that the appropriate tools and methods are available for ensuring they work as intended. To that end, we created a new methodology by which to create Runtime Monitors for BTs. These monitors can be used by the BT to correct when undesirable behavior is detected and are capable of handling LTL specifications. We demonstrate that in terms of runtime, the generated monitors are on par with monitors generated by existing tools and highlight certain features that make our method more desirable in various situations. We note that our method allows for our monitors to be swapped out with alternate monitors with fairly minimal user effort. Finally, our method ties in with our existing tool, BehaVerify, allowing for the verification of BTs with monitors., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) by enabling decentralized, collaborative learning. However, FL is vulnerable to various security threats, including poisoning attacks, where adversarial clients manipulate the training data or model updates to degrade overall model performance. Recognizing this threat, researchers have focused on developing defense mechanisms to counteract poisoning attacks in FL systems. However, existing robust FL methods predominantly focus on computer vision tasks, leaving a gap in addressing the unique challenges of FL with time series data. In this paper, we present FLORAL, a defense mechanism designed to mitigate poisoning attacks in federated learning for time-series tasks, even in scenarios with heterogeneous client data and a large number of adversarial participants. Unlike traditional model-centric defenses, FLORAL leverages logical reasoning to evaluate client trustworthiness by aligning their predictions with global time-series patterns, rather than relying solely on the similarity of client updates. Our approach extracts logical reasoning properties from clients, then hierarchically infers global properties, and uses these to verify client updates. Through formal logic verification, we assess the robustness of each client contribution, identifying deviations indicative of adversarial behavior. Experimental results on two datasets demonstrate the superior performance of our approach compared to existing baseline methods, highlighting its potential to enhance the robustness of FL to time series applications. Notably, FLORAL reduced the prediction error by 93.27% in the best-case scenario compared to the second-best baseline. Our code is available at https://anonymous.4open.science/r/FLORAL-Robust-FTS., Comment: 12 pages, 4 figures, 6 tables

Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse domains (i.e., domain shift), leading to poor performance on unseen domains. Existing Federated Domain Generalization approaches address this problem but assume each client holds data for an entire domain, limiting their practicality in real-world scenarios with domain-based heterogeneity and client sampling. To overcome this, we introduce FISC, a novel FL domain generalization paradigm that handles more complex domain distributions across clients. FISC enables learning across domains by extracting an interpolative style from local styles and employing contrastive learning. This strategy gives clients multi-domain representations and unbiased convergent targets. Empirical results on multiple datasets, including PACS, Office-Home, and IWildCam, show FISC outperforms state-of-the-art (SOTA) methods. Our method achieves accuracy improvements ranging from 3.64% to 57.22% on unseen domains. Our code is available at https://anonymous.4open.science/r/FISC-AAAI-16107.

In fragile watermarking, a sensitive watermark is embedded in an object in a manner such that the watermark breaks upon tampering. This fragile process can be used to ensure the integrity and source of watermarked objects. While fragile watermarking for model integrity has been studied in classification models, image transformation/generation models have yet to be explored. We introduce a novel, trigger-based fragile model watermarking system for image transformation/generation networks that takes advantage of properties inherent to image outputs. For example, manifesting watermarks as specific visual patterns, styles, or anomalies in the generated content when particular trigger inputs are used. Our approach, distinct from robust watermarking, effectively verifies the model's source and integrity across various datasets and attacks, outperforming baselines by 94%. We conduct additional experiments to analyze the security of this approach, the flexibility of the trigger and resulting watermark, and the sensitivity of the watermarking loss on performance. We also demonstrate the applicability of this approach on two different tasks (1 immediate task and 1 downstream task). This is the first work to consider fragile model watermarking for image transformation/generation networks.

This paper presents the syntax and semantics of a novel type of hybrid automaton (HA) with partial differential equation (PDE) dynamic, partial differential hybrid automata (PDHA). In PDHA, we add a spatial domain $X$ and harness a mathematic conception, partition, to help us formally define the spatial relations. While classically the dynamics of HA are described by ordinary differential equations (ODEs) and differential inclusions, PDHA is capable of describing the behavior of cyber-physical systems (CPS) with continuous dynamics that cannot be modelled using the canonical hybrid systems' framework. For the purposes of analyzing PDHA, we propose another model called the discrete space partial differential hybrid automata (DSPDHA) which handles discrete spatial domains using finite difference methods (FDM) and this simple and intuitive approach reduces the PDHA into HA with ODE systems. We conclude with two illustrative examples in order to exhibit the nature of PDHA and DSPDHA., Comment: 17 pages

Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but are vulnerable to adversarial machine learning attacks. These attacks perturb input data to cause misclassification, bypassing protective systems. Existing defenses often rely on enhancing the training process, thereby increasing the model's robustness to these perturbations, which is quantified using verification. While training improvements are necessary, we propose focusing on the verification process used to evaluate improvements to training. As such, we present a case study that evaluates a novel verification domain that will help to ensure tangible safeguards against adversaries and provide a more reliable means of evaluating the robustness and effectiveness of anti-malware systems. To do so, we describe malware classification and two types of common malware datasets (feature and image datasets), demonstrate the certified robustness accuracy of malware classifiers using the Neural Network Verification (NNV) and Neural Network Enumeration (nnenum) tools, and outline the challenges and future considerations necessary for the improvement and refinement of the verification of malware classification. By evaluating this novel domain as a case study, we hope to increase its visibility, encourage further research and scrutiny, and ultimately enhance the resilience of digital systems against malicious attacks., Comment: In International Conference On Formal Methods in Software Engineering, 2024; (FormaliSE'24)

The Quantitative Reflectance Imaging System (QRIS) is a laboratory-based spectral imaging system constructed to image the sample of asteroid Bennu delivered to Earth by the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft. The system was installed in the OSIRIS-REx cleanroom at NASA's Johnson Space Center to collect data during preliminary examination of the Bennu sample. QRIS uses a 12-bit machine vision camera to measure reflectance over wavelength bands spanning the near ultraviolet to the near infrared. Raw data are processed by a calibration pipeline that generates a series of monochromatic, high-dynamic-range reflectance images, as well as band ratio maps, band depth maps, and 3-channel color images. The purpose of these spectral reflectance data is to help characterize lithologies in the sample and compare them to lithologies observed on Bennu by the OSIRIS-REx spacecraft. This initial assessment of lithological diversity was intended to help select the subsamples that will be used to address mission science questions about the early solar system and the origins of life and to provide important context for the selection of representative subsamples for preservation and distribution to international partners. When QRIS imaged the Bennu sample, unexpected calibration issues arose that had not been evident at imaging rehearsals and negatively impacted the quality of QRIS data. These issues were caused by stray light within the lens and reflections off the glovebox window and interior, and were exacerbated by the sample's extremely low reflectance. QRIS data were useful for confirming conclusions drawn from other data, but reflectance and spectral data from QRIS alone unfortunately have limited utility., Comment: 41 pages, 27 figures

Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.