On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems.

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior.

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets.

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor.

Author Correction: Athena: Automated Tuning of k-mer based Genomic Error Correction Algorithms using Language Models.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Athena: Automated Tuning of k-mer based Genomic Error Correction Algorithms using Language Models.

The performance of most error-correction (EC) algorithms that operate on genomics reads is dependent on the proper choice of its configuration parameters, such as the value of k in k-mer based techniques. In this work, we target the problem of finding the best values of these configuration parameters to optimize error correction and consequently improve genome assembly. We perform this in an adaptive manner, adapted to different datasets and to EC tools, due to the observation that different configuration parameters are optimal for different datasets, i.

Parental knowledge and practices on infant teething, Taif, Saudi Arabia.

Background: Parents' false beliefs about signs and symptoms associated with teething have been documented in many studies around the world. This study was conducted to assess parental knowledge on infant teething process and to investigate parents' practices used to alleviate teething disturbances.

Methods: A cross-sectional survey was conducted among parents of children of 6 months-5 years old in Taif, Saudi Arabia during April 2013.

Saudi school students' knowledge, attitude and practice toward medicines.

The current study was aimed to assess Saudi school students' knowledge, attitude and practice about medicines. A pretested self-administered questionnaire was used anonymously among 15-20 year-old adolescents attending tertiary schools in Taif City, KSA. A total of 1022 students completed the questionnaires.