Cleft palate is a common birth defect worldwide. Children diagnosed with this abnormality face difficulties during feeding, hearing, and especially speech. Although surgical methods exist to repair cleft palate, subsequent corrective surgeries are often necessary since children are unable to gain full speech capabilities due to velopharyngeal inadequacy. Investigating the velopharyngeal system in normal patients can help speech pathologists, surgeons, and other medical professionals understand the effects of velopharyngeal anatomy on velopharyngeal function and improve patient diagnosis and treatment. Earlier studies visualized the velum using two-and-three dimensional modeling, but these studies did not adequately investigate the variability in velopharyngeal muscle measures nor their impact on normal and abnormal velopharyngeal function. To remedy these shortcomings, this paper investigates the effects of muscles in the velopharyngeal system on closure force with a novel application of the multiple linear regression machine learning technique. Incorporating multiple anatomical features, multiple linear regression was used to predict closure force values and their direction. The results of this study reveal that multiple linear regression was found to be an effective tool for accurately predicting velopharyngeal closure force for any set of anatomical parameters. Furthermore, these results demonstrated that the velum had a major influence on closure force challenging previous claims that the levator veli palatini muscle was responsible for generating closure force.

With the growing rate of cyber attacks, there is a significant need for intrusion detection systems (IDS) in networked environments. As intrusion tactics become more sophisticated and more challenging to detect, this necessitates improved intrusion detection technology to retain user trust and preserve network security. Over the last decade, several detection methodologies have been designed to provide users with reliability, privacy, and information security. This paper reviews three intrusion detection techniques: blockchain technologies, machine learning, and deep learning. This survey overviews various machine learning and deep learning algorithms, summarizes blockchain technology, and discusses different blockchain methods used for intrusion detection and cybersecurity. We provide insight into their applications, drawbacks, and challenges.

This paper presents an experience report documenting the design and development of an agent-based distributed software system called the “Feed Analyzer” a system deployed to Microsoft's Windows Azure Cloud Service. Cloud services are emerging as an increasingly attractive deployment option given that system developers can leverage the power of a scalable infrastructure without the need to purchase or directly manage hardware or IT resources themselves; all at relatively reduced costs. But as this trend towards the cloud continues, reliable software engineering methodologies that readily lend themselves to the development of cloud applications will become increasingly important. The goal of this experience report is not to evaluate the results of the Feed Analyzer project, either quantitatively or qualitatively, from the perspective of a working software system. The goal of the project itself was to evaluate the applicability of agent-based software engineering methodologies to the relatively new realm of cloud services. Consequently, the overarching purpose of this experience report is to contribute to the body of knowledge relative to the process of deploying agent-based systems to cloud platforms.