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Emerging applications of control, estimation, and machine learning, ranging from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously…

Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focused on developing specific toolchains to support acceleration…

We consider the problem of making apps fault-tolerant through replication, when apps operate at the microsecond scale, as in finance, embedded computing, and microservices apps. These apps need a replication scheme that also operates at the…

Implementing correct distributed systems is an error-prone task. Runtime Verification (RV) offers a lightweight formal method to improve reliability by monitoring system executions against correctness properties. However, applying RV in…

We present RTAMT, an online monitoring library for Signal Temporal Logic (STL) and its interface-aware variant (IA-STL), providing both discrete- and dense-time interpretation of the logic. We also introduce RTAMT4ROS, a tool that…

Formal verification provides the highest assurance of software correctness and security, but its application to large-scale, evolving systems remains a major challenge. While large language models (LLMs) have shown promise in automating…

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source…

A Runtime Verification (RV) framework that supports online, at-speed verification of properties that can change dynamically (during in-field operations) will benefit a large variety of applications. Several state-of-the-art RV frameworks…

Portability, performance, and productivity are three critical dimensions for evaluating a programming model or compiler infrastructure. Several modern programming models for computational science focus on performance and portability. On the…

Modern society is getting accustomed to the Internet of Things (IoT) and Cyber-Physical Systems (CPS) for a variety of applications that involves security-critical user data and information transfers. In the lower end of the spectrum, these…

I describe an approach to compiling common idioms in R code directly to native machine code and illustrate it with several examples. Not only can this yield significant performance gains, but it allows us to use new approaches to computing…

Recently, a two-way RFID authentication protocol based on the AM-SUEO-DBLTKM variable matrix encryption algorithm was proposed for low-cost mobile RFID systems. Its design combines adaptive modulus selection, self-updating matrix ordering,…

Large language model (LLM) inference performance is increasingly bottlenecked by the memory wall. While GPUs continue to scale raw compute throughput, they struggle to deliver scalable performance for memory bandwidth bound workloads. This…

Designing and validating efficient cache-coherent memory subsystems is a critical yet complex task in the development of modern multi-core system-on-chip architectures. Rhea is a unified framework that streamlines the design and…

We notice a way to execute a binary file on Windows and ELF-based systems. It can be used to create software installers and other applications not exceeding 64 kilo bytes.

Multi-Party Computation in the Head (MPCitH) algorithms are appealing candidates in the additional US NIST standardization rounds for Post-Quantum Cryptography (PQC) with respect to key sizes and mathematical hardness assumptions. However,…

Runtime verification (RV) has the potential to enable the safe operation of safety-critical systems that are too complex to formally verify, such as Robot Operating System 2 (ROS2) applications. Writing correct monitors can itself be…

This paper presents a Multilingual Vision Large Language Model, named M-MiniGPT4. Our model exhibits strong vision-language understanding (VLU) capabilities across 11 languages. We utilize a mixture of native multilingual and translated…

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation).…

In this work, we explore the potential of large language models (LLMs) for generating functional test scripts, which necessitates understanding the dynamically evolving code structure of the target software. To achieve this, we propose a…