Machine Learning (ML) has gained prominence across various fields, including data management. Rule-based components are substituted by ML-driven counterparts that extract rules from experience. The prevalence of statistical methods is waning as ...

Cybersecurity events severely impact many individuals, infras-tructures, businesses, and institutions. Machine learning has been playing a key role in many aspects of our daily life and in particular as a cyber defense mechanism. However, the nature of ...

Heating, Ventilation, and Air Conditioning (HVAC) systems play a significant role in global energy consumption, and their effective maintenance is vital for reducing operational costs. The integra-tion of Cyber-Physical Systems, including Internet of ...

Multi-cloud computing is a vitally important topic from both busi-ness and technical perspectives since it guarantees resiliency, avail-ability, and security. Due to the vast number of configurations among cloud providers, it is quite challenging to ...

Today’s Internet of Things (IoT) environments are heterogeneous as they are typically comprised of devices equipped with various CPU architectures and software platforms. Therefore, in defending IoT environments against security threats, the capability ...

We introduce the Topology-Aware Multi-GPU Scheduler (TAMG), a novel approach to assigning multiple GPUs in a GPU cluster to deep learning jobs in a job queue. TAMG uses an existing method for job selection, while incorporating the cluster topology into ...

It is well known that optimistically initializing a Q-table in table-based Reinforcement Learning can be a useful technique in reward-sparse environments. In this paper, we propose an approach to ini-tialize a Deep Q-network optimistically. The ...

Data migration refers to the transfer of data from one location to another, for instance, from a local database to a cloud server or from one cloud to another. To minimize business disruption during this process, it is essential to ensure that data ...

In this work, we try to address the two challenging problems in machine learning (ML) which are: (a) the need for large amounts of labeled images for training supervised classifiers and (b) the supervised classification for time series data. We formulate ...

An increasing number of studies use gender information to un-derstand phenomena such as gender bias, inequity in access and participation, or the impact of the Covid pandemic response. Un-fortunately, most datasets do not include self-reported gender in-...

Computer-aided processes can be used to improve the quality of requirements in software projects. These processes, in principle, can be divided into two stages: requirement quality evaluation, essentially a classification task, and requirement correction ...

Microservice applications are increasingly embracing cloud platforms to run their services. These applications can often be impacted by anomalies. Detecting anomalies at runtime is vital to ensure that cloud-native applications meet specified ...

This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineer-ing tasks through sentence pair classification. T he s tudy adapts generic augmentation techniques such as shuffling, back ...