The article presents a cluster-based technique to detect and locate eavesdropping events in optical line systems. This method uses optical performance monitoring (OPM) data collected at the receiver to detect…
Continue readingThe study presents DefectHunter, a novel model for detecting software vulnerabilities. Traditional vulnerability detection methods are time-consuming, labor-intensive, and often underperform when dealing with complex datasets. The authors propose an…
Continue readingThis paper investigates the security strength of anonymous routing protocols in Network-on-Chip (NoC) architectures, a key component in multicore System-on-Chip (SoC) designs. The authors demonstrate that the existing anonymous routing…
Continue readingThe research project led by Amit Mathapati at Cornell University in 2009 aims to create an advanced mathematical model, specifically an Artificial Neural Network (ANN), to improve the early detection…
Continue readingThe study focuses on the use of machine learning techniques to detect persistent inflammatory biomarkers in post-COVID-19 patients. The research involved 290 patients, using data collected from hospitals in Iraq….
Continue readingThis research paper provides insights into how children (aged 7-13) value and design AI interfaces, specifically teachable machines. The study involved a co-design session where children and adults participated in…
Continue readingThe paper discusses the importance of deep learning methods in text classification due to the rapid growth of online information. Text classification which automatically sorts documents into predefined categories based…
Continue readingThis study investigates the feasibility of programming Internet of Things (IoT) kitchen devices using semantic parsing of cooking recipes. The researchers developed a unique annotated corpus to transform cooking instructions…
Continue readingThis article presents a study on semantic parsing of cooking recipes to make them understandable for IoT kitchen devices. The authors developed a unique annotated corpus aimed at transforming recipe…
Continue readingThe article presents a novel approach to Unsupervised Domain Adaptation (UDA) called Invariant Consistency Learning (ICON). Traditional UDA methods often suffer from spurious correlations between domain-invariant and domain-specific features, which…
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