The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

While the Australian Securities Exchange (ASX) makes a global name for itself by implementing one of the only real use cases for distributed ledger technology (DLT) in its blockchain-based CHESS replacement project, its CIO Dan Chesterman has detailed a handful of other tech-related initiatives the "large regulated fintech" is also undertaking. Speaking with ZDNet at VMworld in San Francisco last week, Chesterman said his organisation is looking into the application of artificial intelligence (AI) and machine learning (ML), highlighting that in the ASX's context, there are a lot of examples where machines are making quite clever decisions. "The main exploration we've been doing of artificial intelligence in that AI/ML space has been in market announcements ... it's not in production, it's something we're doing as a proof of concept," he said. "And what we've come to the conclusion of is that we certainly see, in that sort of context, there is actually a serious consequence for any error." Market announcements, for example, is one element of the business where Chesterman said AI could both help, but also cause legal dilemmas.

In this July 30, 2019, photo, Paul Hildreth, emergency operations coordinator for the Fulton County School District, works in the emergency operations center at the Fulton County School District Administration Center in Atlanta. Artificial Intelligence is transforming surveillance cameras from passive sentries into active observers that can immediately spot a gunman, alert retailers when someone is shoplifting and help police quickly find suspects. Schools, such as the Fulton County School District, are among the most enthusiastic adopters of the technology. Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras.

It admittedly sounds a little like Big Brother, that a robot can tell significant things about your personality, merely by looking into your eyes. Yet, that is the hiring territory that we are fast approaching – although we may not be sitting across from androids in interviews anytime soon. The use of artificial intelligence in making HR decisions is, while fraught with peril, not without its promising aspects. In an era when it is increasingly difficult for businesses to unearth the best job candidates, we may yet see the day when technology makes it possible to separate good from bad in the blink of an eye. Despite caveats about security and privacy, relying on AI would appear to be a method far superior to digging through a pile of resumes or asking ice-breaking questions like, "What's the last book you read?" Hiring good people – people who are talented, agreeable and work well with their coworkers – goes a long way toward nipping workplace conflicts in the bud.

As a crucial component in intelligent transportation systems, crowd flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of large-scale traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal laws and spatial dependencies) to infer the evolution trend of crowd flow. To address this problem, we propose a unified neural network called Attentive Crowd Flow Machine (ACFM), which can effectively learn the spatial-temporal feature representations of crowd flow with an attention mechanism. In particular, our ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer. Specifically, the first LSTM unit takes normal crowd flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second LSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted crowd flow features. Further, we develop two deep frameworks based on ACFM to predict citywide short-term/long-term crowd flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for crowd flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands and show its superior performance in this traffic prediction task.

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

Domino's Pizza Enterprises built a proof-of-concept using machine learning to personalise vouchers and deals for customers in Australia. The master franchise, which operates in nine countries, is an early tester of an AWS service called Personalize, which was only made publicly available last month. Speaking at the recent AWS Summit in Sydney, Domino's lead data scientist Thomas Atkins said the pizza maker wanted to find a way to scale its ability to personalise deals using a wider array of "variants", such as time of day, pickup or delivery, price, and type of pizza. "Making communication via SMS and all our marketing channels personalised is a real challenge because of scale," Atkins said. "To get from segmented marketing to personalised marketing requires a really deep level of automation. "With the typical level of automation that we have, we can do campaigns with four to six variants relatively easily, but there are a number of manual steps that make this a little bit cumbersome.

The idea of robots taking our jobs is not radically new. But artificial intelligence (AI) is now completely reorganising the global economy. Some estimates of productivity-driven economic growth conclude that AI will contribute approximately $US16 trillion ($23 trillion) to the global economy by 2030. Unfortunately -- compared to the European Union, Japan, United States and United Kingdom -- Australia has been relatively late in turning to address the challenges of AI, and creating the right policies to deal with its many implications (good and bad). For our economy to thrive, what we need now is the right mix of governance, regulation, civil society participation, industry support and business compliance -- as well as the development and deepening of digital literacy throughout Australian communities.

The idea of robots taking our jobs is not radically new. But artificial intelligence (AI) is now completely reorganising the global economy. Some estimates of productivity-driven economic growth conclude that AI will contribute approximately $US16 trillion ($23 trillion) to the global economy by 2030. Unfortunately -- compared to the European Union, Japan, United States and United Kingdom -- Australia has been relatively late in turning to address the challenges of AI, and creating the right policies to deal with its many implications (good and bad). For our economy to thrive, what we need now is the right mix of governance, regulation, civil society participation, industry support and business compliance -- as well as the development and deepening of digital literacy throughout Australian communities.