I used this example in my talk at useR!2019 in Toulouse, and it is also the basis of a vignette in the package, and a recent blog post by Mitchell O'Hara-Wild. The data set contains domestic tourist visitor nights in Australia, disaggregated by State, Region and Purpose. An example of a feature would be the autocorrelation function at lag 1 -- it is a numerical summary capturing some aspect of the time series. Autocorrelations at other lags are also features, as are the autocorrelations of the first differenced series, or the seasonally differenced series, etc. Another example of a feature is the strength of seasonality of a time series, as measured by \(1-\text{Var}(R_t)/\text{Var}(S_t R_t)\) where \(S_t\) is the seasonal component and \(R_t\) is the remainder component in an STL decomposition.

Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite its importance, it is common for ERM systems to follow myopic and straight-forward decision policies in the real world. Principled approaches to aid decision-making under uncertainty have been explored in this context but have failed to be accepted into real systems. We identify a key issue impeding their adoption - algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. However, this is not a trivial planning problem - a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is to plan under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both the problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.

As the prevalence of deep learning in computer vision, adversarial samples that weaken the neural networks emerge in large numbers, revealing their deep-rooted defects. Most adversarial attacks calculate an imperceptible perturbation in image space to fool the DNNs. In this strategy, the perturbation looks like noise and thus could be mitigated. Attacks in feature space produce semantic perturbation, but they could only deal with low resolution samples. The reason lies in the great number of coupled features to express a high-resolution image. In this paper, we propose Attack by Identifying Effective Features (AIEF), which learns different weights for features to attack. Effective features, those with great weights, influence the victim model much but distort the image little, and thus are more effective for attack. By attacking mostly on them, AIEF produces high resolution adversarial samples with acceptable distortions. We demonstrate the effectiveness of AIEF by attacking on different tasks with different generative models.

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. Firstly, it introduces a taxonomy to analyze the field of machine ethics from an ethical, implementational, and technical perspective. Secondly, an exhaustive selection and description of relevant works is presented. Thirdly, applying the new taxonomy to the selected works, dominant research patterns and lessons for the field are identified, and future directions for research are suggested.

In such systems the dialogue state tracker (DST) is a core component, aimed to maintain a distribution over the dialogue states based on the dialogue history. A dialogue state at any turn t in the dialogue is typically represented as a set of slot-value pairs, such as ( price, moderate) or ( food, italian) in the context of restaurant reservation. The goal of the DST is to determine the user's intent and the user's goal during the dialogue and represent them as such slot-value pairs. The downstream components of a dialogue system (e.g the dialogue manager) that are responsible to choose the next system action, rely on an accurate DST for an effective dialogue strategy. Because of the importance of DST in dialogue systems, their development attracted lots of research both in academia and industry. Typical dialogue systems are modeled for a fixed ontology consisting of a single domain (Mrk ˇ si c et al. 2017; Zhong, Xiong, and Socher 2018; Ren et al. 2018), and the domain ontology schema defines intents, slots and values for each slot of the domain.

--Machine Learning has proven useful in the recent years as a way to achieve failure prediction for industrial systems. However, the high computational resources necessary to run learning algorithms are an obstacle to its widespread application. The sub-field of Distributed Learning offers a solution to this problem by enabling the use of remote resources but at the expense of introducing communication costs in the application that are not always acceptable. In this paper, we propose a distributed learning approach able to optimize the use of computational and communication resources to achieve excellent learning model performances through a centralized architecture. T o achieve this, we present a new centralized distributed learning algorithm that relies on the learning paradigms of Active Learning and Federated Learning to offer a communication-efficient method that offers guarantees of model precision on both the clients and the central server . We evaluate this method on a public benchmark and show that its performances in terms of precision are very close to state-of-the-art performance level of non-distributed learning despite additional constraints. A. General problem In the recent years, the efficiency of Machine Learning for automated processing of large volumes of data has been widely demonstrated. This has been of particular interest for industrial applications that commonly generate large datasets.

As MBW reports, the latest staffing changes have made waves due to the statement that came with the news citing a renewed investment in artificial intelligence. "We are modernizing our company to take advantage of the significant investments we have made in new technology and aligning our operating structure to match the technology-powered businesses we are now in," said iHeartMedia. It's believed that the 57 are mostly radio jocks carry decades of experience working across genres of music including Rock, Urban, Country, Top 40, Hits and more. "During a transition like this it's reasonable to expect that there will be some shifts in jobs – some by location and some by function – but the number is relatively small given our overall employee base of 12,500." "That said, we recognize that the loss of any job is significant; we take that responsibility seriously and have been thoughtful in the process."

Queensland is now home to a second Great Barrier Reef, allowing children and adults alike the ability to interact with the world's largest coral reef system without leaving the city. The Living Reef is the brainchild of game developers and researchers at the Queensland University of Technology's (QUT) The Cube in Brisbane. Large 10-metre-tall screens are educating visitors about the creatures of the reef as well as the environmental issues it faces now and into the future. The team is one of the first in the world to use a system where coral was grown with a method called the space colonisation algorithm to help mimic nature. "We created a system where we could grow coral mathematically using simulation software," Cube studio manager Simon Harrison said.

The chief executive of Google has called for international cooperation on regulating artificial intelligence technology to ensure it is'harnessed for good'. Sundar Pichai said that while regulation by individual governments and existing rules such as GDPR can provide a'strong foundation' for the regulation of AI, a more coordinated international effort is'critical' to making global standards work. The CEO said that history is full of examples of how'technology's virtues aren't guaranteed' and that with technological innovations come side effects. These range from internal combustion engines, which allowed people to travel beyond their own areas but also caused more accidents, to the internet, which helped people connect but also made it easier for misinformation to spread. These lessons teach us'we need to be clear-eyed about what could go wrong' in the development of AI-based technologies, he said.

Australian business leaders are confident that the looming rise of artificial intelligence-powered automation will increase employment, rather than destroy jobs, but are not moving fast enough to retrain staff for changed roles a study from Accenture has found. In a new report entitled "Future Workforce: Reworking The Revolution," Accenture refers to findings from a global study it timed to coincide with the recent World Economic Forum in Davos, which found that there is a notable disconnect between the importance organisations place on AI and automation and how much they are doing to equip staff for the change. The Australian responses to the study showed that 71 per cent of Australian senior executives think that their company will create net job gains in the next three years through AI, and 60 per cent of workers believe it will have a positive impact, rather than taking their job. Accenture's Andrew Woolf says the pace of technology change is accelerating and reskilling staff is part of the solution to the challenge. However, while 53 per cent of business leaders said human-machine collaboration was important to their strategic priorities, only 3 per cent said their organisation planned to significantly increase investment in reskilling workers in the next three years.