Defining Safe Training Datasets for Machine Learning Models Using Ontologies

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain

The Go Clean Cup

Single-serve coffee machines and pods have gained popularity in recent years for ease of use. While these machines can save energy and water in the long run, especially in business settings, the consequences include billions of disposable coffee pods being thrown into landfills around the world each year. Although companies, such as Keurig®, have made reusable pods to fix this problem, the current designs are incompatible with many machines and they use hard plastic, making them difficult to clean. According to a survey conducted by the Go Clean Cup project, 63% of consumers would buy a new reusable single-serve coffee pod of equal price as the others if it was easier to clean. The Go Clean Cup is a reusable single-serve coffee pod that is flexible and eversible for easy cleaning. The pod can be partially or completely everted for coffee grounds to be wiped out without hassle and will be universally compatible with single-serve beverage machines. With a pending utility patent on the design, the Go Clean Cup project is currently focused on developing the best manufacturing method, including molding and laser drilling, to get the product on retail shelves for the lowest cost

Eliminating Security Weaknesses in Requirement Specifications via a Knowledge Graph

Eliciting requirements from customers and writing requirement specifications for any part of a software system is difficult and time-consuming. However, writing security specifications is especially difficult due to many development teams’ lack of security expertise. This causes security to either be an afterthought during implementation, or for security weaknesses to go unnoticed by developers. Utilizing automation to improve this process and validate the security specifications against known security weaknesses could help reduce the number of weaknesses introduced during the requirements phase of software development. The automated process described in this research uses entity linking to search a Software Requirement Specification (SRS) document for keywords associated with publicly known security weaknesses. The keywords are then used to query a knowledge graph of security weaknesses to provide developers with a detailed understanding of the security threats to their system. The developers can use the query results to refine their security specifications and improve the system’s strength early in the software development life cycle. Keywords – Automation, validation, security requirements, knowledge grap