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Evaluating the programming robustness of large language models (LLMs) is paramount for ensuring their reliability in AI-based software development. However, adversarial attacks exhibit fundamental limitations that compromise fair robustness…
We provide an open source toolbox on https://github.com/ct2641/CAI/releases/tag/1.0 to conduct computer-aided investigation on the fundamental limits of information systems. The toolbox relies on either Gurobi or Cplex as the linear program…
Streaming data analysis is increasingly required in applications, e.g., IoT, cybersecurity, robotics, mechatronics or cyber-physical systems. Despite its relevance, it is still an emerging field with open challenges. SDO is a recent anomaly…
Internet of Things (IoT) is expected to enable a myriad of applications by interconnecting objects - such as sensors and robots - over the Internet. IoT applications range from healthcare to autonomous vehicles and include disaster…
AI-powered software tools are widely used to assist software engineers. However, there is still a need to understand the productivity benefits of such tools for software engineers. In addition to short-term benefits, there is a question of…
Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic…
With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses…
The adoption of DevOps practices in embedded systems and firmware development is emerging as a response to the growing complexity of modern hardware--software co-designed products. Unlike cloud-native applications, embedded systems…
Beowulf clusters are very popular and deployed worldwide in support of scientific computing, because of the high computational power and performance. However, they also pose several challenges, and yet they need to provide high…
The Internet of Things (IOT) sensor network is an effective solution for monitoring environment condition. IOT sensor network generates massive data, and the abilities of massive data storage, processing and query become technical…
We review the rapidly growing literature on auxiliary information-based (AIB) process monitoring methods. Under this approach, there is an assumption that the auxiliary variable, which is correlated with the quality variable of interest,…
Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a…
The Industrial Internet of Things (IIoT) is a transformative paradigm that integrates smart sensors, advanced analytics, and robust connectivity within industrial processes, enabling real-time data-driven decision-making and enhancing…
Most enterprise applications use logging as a mechanism to diagnose anomalies, which could help with reducing system downtime. Anomaly detection using software execution logs has been explored in several prior studies, using both classical…
Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such…
Adopting DevOps practices is nowadays a recurring task in the industry. DevOps is a set of practices intended to reduce the friction between the software development (Dev) and the IT operations (Ops), resulting in higher quality software…
Transitioning cloud-based Hadoop from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, managed Hadoop systems do present a black-box behavior to the…
Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the…
For emerging datacenter (in short, DC) workloads, such as online Internet services or offline data analytics, how to evaluate the upper bound performance and provide apple-to-apple comparisons are fundamental problems. To this end, a…
Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and…