Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which aims to find a transformation matrix to convert feature space to a smaller space by maximising the between-class scatter matrix while minimising the within-class scatter matrix. Although the fast and easy process of finding the transformation matrix has made this method attractive, overemphasizing the large class distances makes the criterion of this method suboptimal. In this case, the close class pairs tend to overlap in the subspace. Despite different weighting methods having been developed to overcome this problem, there is still a room to improve this issue. In this work, we study a weighted trace ratio by maximising the harmonic mean of the multiple objective reciprocals. To further improve the performance, we enforce the l 2,1 -norm to the developed objective function. Additionally, we propose an iterative algorithm to optimise this objective function. The proposed method avoids the domination problem of the largest objective, and guarantees that no objectives will be too small. This method can be more beneficial if the number of classes is large. The extensive experiments on different datasets show the effectiveness of our proposed method when compared with four state-of-the-art methods.

Locally Linear Support Vector Machine (LLSVM) has been actively used in classification tasks due to its capability of classifying nonlinear patterns. However, existing LLSVM suffers from two drawbacks: (1) a particular and appropriate regularization for LLSVM has not yet been addressed; (2) it usually adopts a three-stage learning scheme composed of learning anchor points by clustering, learning local coding coordinates by a predefined coding scheme, and finally learning for training classifiers. We argue that this decoupled approaches oversimplifies the original optimization problem, resulting in a large deviation due to the disparate purpose of each step. To address the first issue, we propose a novel diversified regularization which could capture infrequent patterns and reduce the model size without sacrificing the representation power. Based on this regularization, we develop a joint optimization algorithm among anchor points, local coding coordinates and classifiers to simultaneously minimize the overall classification risk, which is termed as Diversified and Unified Locally Linear Support Vector Machine (DU-LLSVM for short). To the best of our knowledge, DU-LLSVM is the first principled method that directly learns sparse local coding and can be easily generalized to other supervised learning models. Extensive experiments showed that DU-LLSVM consistently surpassed several state-of-the-art methods with a predefined local coding scheme (e.g. LLSVM) or a supervised anchor point learning (e.g. SAPL-LLSVM).

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal-noise ratio and incomplete EEG signals), and difficulties in capturing the inconspicuous relationships between EEG signals and certain brain activities. Most existing works either only consider EEG as chain-like sequences neglecting complex dependencies between adjacent signals or requiring pre-processing such as transforming EEG waves into images. In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements and instructions effectively learning the compositional spatio-temporal representations of raw EEG streams. Extensive experiments on a large scale movement intention EEG dataset (108 subjects,3,145,160 EEG records) have demonstrated that both models achieve high accuracy near 98.3% and outperform a set of baseline methods and most recent deep learning based EEG recognition models, yielding a significant accuracy increase of 18% in the cross-subject validation scenario. The developed models are further evaluated with a real-world BCI and achieve a recognition accuracy of 93% over five instruction intentions. This suggests the proposed models are able to generalize over different kinds of intentions and BCI systems.

We propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints. The assignment problem is a fundamental in both computer science and economics with application in many areas including task and resource allocation. Drawing inspiration from work in multi-criteria decision making and social choice theory we use order weighted averages (OWAs), a parameterized class of mean aggregators, to propose a novel and flexible class of algorithms for the assignment problem. We show an algorithm for finding an SUM-OWA assignment in polynomial time, in contrast to the NP-hardness of finding an egalitarian assignment. We demonstrate through empirical experiments that using SUM-OWA assignments can lead to high quality and more fair assignments.

We consider the problem of selecting a fixed-size committee based on approval ballots. It is desirable to have a committee in which all voters are fairly represented. Aziz et al. (2015a; 2017) proposed an axiom called extended justified representation (EJR), which aims to capture this intuition; subsequently, Sanchez-Fernandez et al. (2017) proposed a weaker variant of this axiom called proportional justified representation (PJR). It was shown that it is coNP-complete to check whether a given committee provides EJR, and it was conjectured that it is hard to find a committee that provides EJR. In contrast, there are polynomial-time computable voting rules that output committees providing PJR, but the complexity of checking whether a given committee provides PJR was an open problem. In this paper, we answer open questions from prior work by showing that EJR and PJR have the same worst-case complexity: we provide two polynomial-time algorithms that output committees providing EJR, yet we show that it is coNP-complete to decide whether a given committee provides PJR. We complement the latter result by fixed-parameter tractability results.

We consider the problem of playing Minesweeper with a limited number of moves: Given a partially revealed board, a number of available clicks k, and a target probability p, can we win with probability p. We win if we do not click on a mine, and, after our sequence of at most k clicks (which reveal information about the neighboring squares) can correctly identify the placement of all mines. We make the assumption, that, at all times, all placements of mines consistent with the currently revealed squares are equiprobable. Our main results are that the problem is PSPACE-complete, and it remains PSPACE-complete when p is a constant, in particular when p = 1. When k = 0 (i.e., we are not allowed to click anywhere), the problem is PP-complete in general, but co-NP-complete when p is a constant, and in particular when p = 1.

A new study released by The Economist Intelligence Unit ran three econometric scenarios to 2030 on five countries -- the United States, the United Kingdom, Australia, Japan--and developing Asia as a whole. In'Risks and rewards: Scenarios around the economic impact of machine learning', commissioned by Google, two scenarios assumed greater human productivity through upskilling and greater investment in technology and access to open source data, while the third assumed insufficient policy support for structural changes in the economy. The results showed that, although the fears of those pessimistic about the impact of machine learning, and artificial intelligence in general, may be overblown, the optimists' claims are not entirely supported, either. The other area of the study, a look at the impact of machine learning on four industries, reaches a similar conclusion. For firms both developing machine learning and those using it, the reports finds that communication between themselves, and with the public and policymakers, needs to improve.

Credit Union Australia (CUA) has turned to Sydney-based artificial intelligence (AI) firm Flamingo AI to pilot its chatbot Rosie in its CUA health insurance business. Under the agreement, Flamingo's cognitive virtual sales assistant, Rosie, will be piloted by CUA Health to provide a service for potential new members visiting its website, assisting the user from the quote stage, through to the point of sale. CUA Health CEO Philip Fraser says: "Consumers are becoming more financially and technologically savvy and there is an ever increasing number that prefer the convenience and accessibility of a digital sales channel." To placate any human anxiety, he adds: "This channel is certainly not a replacement for our human service team – rather it provides us with a huge opportunity to grow our health member base by lifting the number of quote requests that are followed all the way through to taking out a policy." After a period of "learning" the commonly asked questions and answers, Rosie will be deployed on the CUA Health web channel for a three-month pilot, expected to kick off in the next few months.

The idea that machines can make intelligent decisions has been around since the 1950s when the first learning programme was built. At the time, the machine itself was groundbreaking, improving at the game of checkers the more it played. Since then, the idea of such machines has become more and more prevalent, particularly in pop culture. But over the last few years, the concept has moved from the realm of fiction, such as Iron Man's JARVIS (Just A Rather Very Intelligent System), the highly advanced computer system supporting Tony Stark, to our phones and even our living rooms. We're growing more and more comfortable with AI services, such as Apple's Siri and Amazon's Alexa, the latter of which Amazon recently announced would make its way to Australia and New Zealand in 2018.

Businesses will take the leap and embed augmented reality (AR) and machine learning into their business practices in 2018 according to Deloitte's 2018 Technology, Media and Telecommunications (TMT) predictions. With invisible smartphone innovations opening up application opportunities, consumers will be more connected and more distracted by smartphones than ever before. Deloitte Australia TMT leader Kimberly Chang says, "We're at the tipping point of widespread adoption of a number of technologies. "In 2018 we will finally see business challenges being addressed by what has to date been consumer-driven technology." "But it will be a year of trial and error." Deloitte predicts that over a billion smartphone users will create AR content at least once in 2018 and by 2020 AR will generate direct revenues of US$1 billion. Currently around half of AR uses are non-enterprise focused but Australia is on the precipice of mass adoption, with particular potential in industries such as mining, retail, training and marketing. AR also has a role to play in the future of work and job creation in Australia. With the smartphone predicted to be the primary device for AR content creation, Deloitte is predicting smartphone penetration will continue to increase, surpassing 90% by the end of 2023. Machine learning is also set to hit its stride in 2018 with Deloitte predicting the number of implementations and pilot projects using the technology to double from 2017, with two-thirds of large companies having 10 or more implementations and a similar number of pilots. To date, the uptake of machine learning in Australia has been slow, but Deloitte expects to see leaps in experimentation around machine learning applications in 2018, in particular in relation to consumer electronics, autonomous vehicles, finance, insurance, human resources and clinical operational diagnosis in healthcare. Chang continues, "Machine learning is not a new concept, but it is about to revolutionise our daily lives.