How Hard Is Weak-Memory Testing?

Weak-memory models are standard formal specifications of concurrency across hardware, programming languages, and distributed systems. A fundamental computational problem is consistency testing: is the observed execution of a concurrent program in alignment with the specification of the underlying system? The problem has been studied...

Tiny, Always-on, and Fragile: Bias Propagation through Design Choices in On-device Machine Learning Workflows

Billions of distributed, heterogeneous, and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast, and offline inference on personal data. On-device ML is highly context dependent and sensitive to user, usage, hardware, and environment attributes. This sensitivity and the propensity toward bias in ML makes...

Optimal Reads-From Consistency Checking for C11-Style Memory Models

Over the years, several memory models have been proposed to capture the subtle concurrency semantics of C/C++. One of the most fundamental problems associated with a memory model M is consistency checking: given an execution X, is X consistent with M? This problem lies at the heart of numerous applications, including specification testing and...

Design of a Direct numerical Simulation of flow and heat transfer in a T-junction

Several investigations have been undertaken to study the velocity and temperature fields associated with the thermal mixing between fluids, and resulting thermal striping in a T-junction. However, the available experimental databases are not sufficient to describe the involved physics in adequate detail, and, due to experimental limitations,...

Social media for improving metro rail project operations

Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning

When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model...

Domain-Based Fuzzing for Supervised Learning of Anomaly Detection in Cyber-Physical Systems

A novel approach is proposed for constructing models of anomaly detectors using supervised learning from the traces of normal and abnormal operations of an Industrial Control System (ICS). Such detectors are of value in detecting process anomalies in complex critical infrastructure such as power generation and water treatment systems. The traces...

Using datasets from industrial control systems for cyber security research and education

The availability of high-quality benchmark datasets is an important prerequisite for research and education in the cyber security domain. Datasets from realistic systems offer a platform for researchers to develop and test novel models and algorithms. Such datasets also offer students opportunities for active and project-centric learning. In...

An Analytical Criterion for Centrifugal Instability in Non-Axisymmetric Vortices

Non-axisymmetric vortices are ubiquitous in nature; examples include polar vortices in planets, the giant red spot in Jupiter, tornadoes and cyclones on Earth, mesoscale eddies in the ocean. Turbulent flows are furthermore known to be dominated by small- and large-scale vortex structures. Owing to the wide range of applications, knowledge of...