Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safety risks and economic losses, making early prediction of TTX contamination vital to the food industry and competent authorities. Recent studies have pointed to shallow habitats and water temperature as main drivers to TTX contamination in bivalve mollusks. However, the temporal relationships between abiotic factors, biotic factors, and TTX contamination remain unexplored. We have developed an explainable, deep learning-based model to predict TTX contamination in the Dutch Zeeland estuary. Inputs for the model were meteorological and hydrological features; output was the presence or absence of TTX contamination. Results showed that the time of sunrise, time of sunset, global radiation, water temperature, and chloride concentration contributed most to TTX contamination. Thus, the effective number of sun hours, represented by day length and global radiation, was an important driver for tetrodotoxin contamination in bivalve mollusks. To conclude, our explainable deep learning model identified the aforementioned environmental factors (number of sun hours, global radiation, water temperature, and water chloride concentration) to be associated with tetrodotoxin contamination in bivalve mollusks; making our approach a valuable tool to mitigate marine toxin risks for food industry and competent authorities.

In ontology development, there is a gap between domain ontologies which mostly use the web ontology language, OWL, and foundational ontologies written in first-order logic, FOL. To bridge this gap, we present Gavel, a tool that supports the development of heterogeneous 'FOWL' ontologies that extend OWL with FOL annotations, and is able to reason over the combined set of axioms. Since FOL annotations are stored in OWL annotations, FOWL ontologies remain compatible with the existing OWL infrastructure. We show that for the OWL domain ontology OBI, the stronger integration with its FOL top-level ontology BFO via our approach enables us to detect several inconsistencies. Furthermore, existing OWL ontologies can benefit from FOL annotations. We illustrate this with FOWL ontologies containing mereotopological axioms that enable new meaningful inferences. Finally, we show that even for large domain ontologies such as ChEBI, automatic reasoning with FOL annotations can be used to detect previously unnoticed errors in the classification.

Mike Leone, an analyst at Enterprise Strategy Group in Milford, Mass., sees incredible traction for AI and machine learning in enterprise IT. While AI and machine learning are top priorities for companies working toward digital transformation, he said, investment remains modest as a result of the infrastructure costs associated with these new technologies. Both fields rely heavily on different elements of the technology stack, from physical hardware supporting storage, compute and networking to software that handles compliance and other requirements. Yet enterprises still struggle to have all their networking infrastructure in sync, citing security, compliance, and to a lesser extent, big data, as the "weak links" in the chain, according to Leone. A majority of organizations rely on three different tools to develop, test, deploy and manage machine learning models, ESG said.

With the release of Qlik Sense Business on Tuesday, Qlik extended the reach of its cloud-first capabilities. The offering replaces Qlik Sense Cloud Business, which the analytics and business intelligence vendor, based in King of Prussia, Pa., debuted in 2015. In addition, Qlik rolled out Qlik Sense September 2019, the latest update of its central BI product. Qlik Sense Business is a SaaS offering built on third-generation BI capabilities -- augmented intelligence and machine learning. It differs from Qlik Sense Cloud Business by removing limits on the number of users, connecting more seamlessly to Qlik Sense Enterprise and providing expanded AI and machine learning capabilities.

Jon Oltsik, an analyst at Enterprise Strategy Group in Milford, Mass., looked into potential pitfalls associated with enterprise security teams collecting ever-increasing reams of data. ESG research indicates that 38% of organizations collect more than 10 TB of data every month, primarily from firewall logs, network devices, antivirus and user activity logs. "Let's face it, well-intentioned security teams are being buried by data today. They go through heroic efforts and do what they can, but there is an obvious and logical outcome here: As security data volume grows, security professionals will only be able to derive an incremental amount of value," Oltsik said. For organizations swamped with security data, Oltsik recommends making data available through standard APIs or putting data in standard formats such as the Common Information Model used by Splunk.

Cybersecurity machine learning is growing in popularity, according to Jon Oltsik, an analyst with Enterprise Strategy Group Inc. in Milford, Mass. Oltsik attended the recent Black Hat conference, where technology vendors were abuzz with talk of cybersecurity machine learning. ESG research asked 412 respondents about their understanding of artificial intelligence (AI) and cybersecurity machine learning, which revealed that only 30% said they were very knowledgeable on the subject. Only 12% of respondents said their organizations had deployed these systems widely. According to Olstik, the cybersecurity industry sees an opportunity, because only 6% of respondents in surveys said their organizations were not considering AI or machine learning deployments.