Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore,it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.

The Australian government's 2018-19 Budget has earmarked a massive AU$2.4 billion for technology and science over the next 12 years in a bid to support "a stronger and smarter economy". "The government will invest more than AU$2.4 billion in Australia's public technology infrastructure," Treasurer Scott Morrison said in his Budget speech on Tuesday night. "This includes supercomputers, world-class satellite imagery, more accurate GPS across Australia, upgrading the Bureau of Meteorology's technology platform, a national space agency, and leading research in artificial intelligence." The government will invest AU$29.9 million over four years in AI and machine learning, which it said would support business innovation across digital health, digital agriculture, cybersecurity, energy, and mining. "This measure will also support Cooperative Research Centre projects, PhD scholarships, and school-related learning to increase knowledge and develop the skills needed for AI and machine learning."

Ben Rose, General Manager, Direct and Partnerships at nib, joined us via the magic of the Internet to take us through how nib are experimenting with Artificial Intelligence through the use of chatbots and machine learning to automatically answer clients questions. Ben also explains how a seemingly "stuffy" insurance company has an attitude of this century and not the last, and how they have "online" in their DNA that enables agile experimentation. He also advises that companies of all sizes in New Zealand should start working out how AI can help their company - it's not expensive, but it is something to get one's head around.

Many say eyes are a window into the soul and now an emerging body of research suggests that the way in which we move our eyes is affected by our personality. Studies on this topic have found people with similar traits tend to move their eyes in similar ways. For instance, optimists spend less time looking at negative emotional stimuli, such as images of cancer, while curious people tend to take in all areas of a scene. Now, an international team of researchers from institutions in Australia and Germany decided to understand more about the connection between personality and eye movements by developing a machine learning algorithm, a kind of computer code which learns without the need to be specifically programmed, as reported by New Scientist. For this new study, published in the journal Frontiers in Human Neuroscience, the researchers gave 42 students at Flinders University in south Australia special eye-tracking glasses.

The classical view of epistemic logic is that an agent knows all the logical consequences of their knowledge base. This assumption of logical omniscience is often unrealistic and makes reasoning computationally intractable. One approach to avoid logical omniscience is to limit reasoning to a certain belief level, which intuitively measures the reasoning "depth." This paper investigates the computational complexity of reasoning with belief levels. First we show that while reasoning remains tractable if the level is constant, the complexity jumps to PSPACE-complete -- that is, beyond classical reasoning -- when the belief level is part of the input. Then we further refine the picture using parameterized complexity theory to investigate how the belief level and the number of non-logical symbols affect the complexity.

An Australian man has set a new world record for fastest time to solve a Rubik's cube at just 4.22 seconds. Feliks Zemdegs is a 22-year-old'speedcuber' from Australia who participated in the Cube for Cambodia 2018 event on Saturday in Melbourne. He broke the previous world record of 4.59 seconds by solving a 3x3x3 cube in just 4.22 seconds. Feliks Zemdegs set a world record for fastest time to solve a Rubik's cube at just 4.22 seconds The 22-year-old from Australia broke the previous record at the Cube for Cambodia 2018 event on Saturday in Melbourne. A video captured his record-breaking performance as he sat alongside other speedcubers of all ages.

Today, every company is a technology company, with an ever shorter go-to-market cycle. Every professional role that is now touched by technology will soon be collaborating with an Artificial Intelligence (AI) system or – as we prefer – an'Augmented Intelligence' system. It will be the people, not only the technology, that will drive widespread AI adoption, so it's essential to democratise AI capability for the benefit of all roles, from marketing and legal to HR and operations. A new report on New Zealand's AI future highlights the critical importance of developers in particular. Artificial Intelligence: Shaping a Future New Zealand is an in-depth study by the AI Forum exploring the opportunities and impacts of AI in New Zealand.