A5021509994- Formal Methods in Verification
- Advanced Software Engineering Methodologies
- Model-Driven Software Engineering Techniques
- Adversarial Robustness in Machine Learning
- Simulation Techniques and Applications
- Software Reliability and Analysis Research
- Anomaly Detection Techniques and Applications
- Fault Detection and Control Systems
- Explainable Artificial Intelligence (XAI)
- Safety Systems Engineering in Autonomy
- Real-Time Systems Scheduling
- Smart Grid Security and Resilience
- Healthcare Technology and Patient Monitoring
- Software Testing and Debugging Techniques
- Software System Performance and Reliability
- Modeling and Simulation Systems
- Quality and Safety in Healthcare
- IoT and Edge/Fog Computing
- Spreadsheets and End-User Computing
- Machine Learning and Algorithms
- Intelligent Tutoring Systems and Adaptive Learning
- Advanced Neural Network Applications
- Embedded Systems Design Techniques
- Flexible and Reconfigurable Manufacturing Systems
- Non-Invasive Vital Sign Monitoring
Florida Museum of Natural History
2024
University of Florida
2023-2024
University of Pennsylvania
2019-2024
Lee University
2023
Philadelphia University
2021-2022
California University of Pennsylvania
2021
Carnegie Mellon University
2012-2020
Software Engineering Institute
2014
Lomonosov Moscow State University
2010
Abstract The integration of Artificial Intelligence (AI) with the Internet Things (IoT), known as (AIoT), enhances devices’ processing and analysis capabilities disrupts such sectors healthcare, industry, oil. However, AIoT’s complexity scale are challenging for traditional machine learning (ML). Deep offers a solution but has limited testability, verifiability, interpretability. In turn, neuro-symbolic paradigm addresses these challenges by combining robustness symbolic AI flexibility DL,...
Cyber-physical systems (CPS) are heterogeneous, because they tightly couple computation, communication, and control along with physical dynamics, which traditionally considered separately. Without a comprehensive modeling formalism, model-based development of CPS involves using multitude models in variety formalisms that capture various aspects the system design, such as software networking models, protocol design. rigorous unifying framework, integration analysis results for remains ad hoc....
Smart Cyber--Physical Systems (sCPS) are modern CPS systems that engineered to seamlessly integrate a large number of computation and physical components; they need control entities in their environment smart collective way achieve high degree effectiveness efficiency. At the same time, these supposed be safe secure, deal with dynamicity uncertainty, cope external threats, optimize behavior best possible outcome. This "smartness" typically stems from highly cooperative behavior,...
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust safety certification challenges due to inherent uncertainties. The neurosymbolic paradigm replaces stochastic layers with interpretable symbolic AI, enabling determinism. While promising, like multisensor fusion, adaptability, verification remain. This paper introduces NeuroStrata, a framework enhance the testing of autonomous CPS. We outline its key components, present early results,...
We developed model-based adaptation, an approach that leverages models of software and its environment to enable automated adaptation. The goal our is build long-lasting systems can effectively adapt changes in their environment.
Developing cyber-physical systems involves multiple engineering domains, e.g., timing, logical correctness, thermal resilience, and mechanical stress. In today's industrial practice, these domains rely on analyses to obtain verify critical system properties. Domain differences make the abstract away interactions among themselves, potentially invalidating results. Specifically, one challenge is ensure that an analysis never applied a model violates assumptions of analysis. Since such...
Modern cyber-physical systems interact closely with continuous physical processes like kinematic movement. Software component frameworks do not provide an explicit way to represent or reason about these processes. Meanwhile, hybrid program models have been successful in proving critical properties of discrete-continuous systems. These programs deal diverse aspects a system such as controller decisions, communication protocols, and mechanical dynamics, requiring several address the variation....
Decision-making approaches in self-adaptation face a fundamental trade-off between quality and timeliness of adaptation plans. Due to this trade-off, designers often have make an offline compromise finding plans quickly closer-to-optimal that demand longer computation times. Recent work has proposed hybrid planning can resolve dynamically, achieving higher utility than either fast or slow individually. The promise is combine multiple decision-making at run time produce the high within given...
Closed-loop verification of cyberphysical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine whether these guar-antees apply at run time because assumptions may be violated. To predict violations in a verified system, we propose three-step confidence composition (CoCo) framework for monitoring First, represent the sufficient condition propositional logical formula over Second, build calibrated monitors...
Models of actual causality leverage domain knowledge to generate convincing diagnoses events that caused an outcome. It is promising apply these models diagnose and repair run-time property violations in cyber-physical systems (CPS) with learning-enabled components (LEC). However, given the high diversity complexity LECs, it challenging encode (e.g., CPS dynamics) a scalable model could useful suggestions. In this paper, we focus causal diagnosis on input/output behaviors LECs. Specifically,...
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability analysis fail to scale, and purely statistical are constrained by distributional assumptions about sampling process. Instead, we pose a distributionally robust version verification for black-box systems, where our performance hold over large family distributions. This paper proposes novel approach based...
Designing secure cyber-physical systems (CPS) is a particularly difficult task since security vulnerabilities stem not only from traditional cybersecurity concerns, but also physical ones. Many of the standard methods for CPS design make strong and unverified assumptions about trustworthiness devices, such as sensors. When these are violated, subtle inter-domain introduced into system model. In this paper we use formal specification analysis contracts to expose guarantees analyses...
Cyber-physical systems (CPSs) mix software, hardware, and physical aspects with equal importance. Typically, the use of models such during run time has concentrated only on managing controlling cyber (software) aspects. However, to fully realize goals a CPS, too have be treated as first-class models. This approach gives rise three main challenges: (a) identifying integrating software different characteristics semantics; (b) obtaining instances at suitable level abstraction for adaptation;...
Cyber-Physical Systems (CPS) integrate computational and physical components. With the digitisation of society industry progressing integration systems, CPS need to become "smarter" in sense that they can adapt learn handle new unexpected conditions, improve over time. Smarter present a combination challenges existing engineering methods have difficulties addressing: intertwined digital, social spaces, for heterogeneous modelling formalisms, demand context-tied cooperation achieve system...
Recent research in embedded and cyber-physical systems has developed theories tools for integration of heterogeneous components models. These efforts, although important, are insufficient high-quality error-free since inconsistencies between system elements may stem from factors not directly represented models (e.g., analysis expert disagreements). Therefore, we need to broaden our perspective on integration, devise approaches three novel directions integration: modeling methods, data sets,...