We characterize the class of committee scoring rules that satisfy the fixed-majority criterion. In some sense, the committee scoring rules in this class are multiwinner analogues of the single-winner Plurality rule, which is uniquely characterized as the only single-winner scoring rule that satisfies the simple majority criterion. We find that, for most of the rules in our new class, the complexity of winner determination is high (i.e., the problem of computing the winners is NP-hard), but we also show some examples of polynomial-time winner determination procedures, exact and approximate.

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.

Autonomous agents operating in partially observable stochastic environments often face the problem of optimizing expected performance while bounding the risk of violating safety constraints. Such problems can be modeled as chance-constrained POMDP's (CC-POMDP's). Our first contribution is a systematic derivation of execution risk in POMDP domains, which improves upon how chance constraints are handled in the constrained POMDP literature. Second, we present RAO*, a heuristic forward search algorithm producing optimal, deterministic, finite-horizon policies for CC-POMDP's. In addition to the utility heuristic, RAO* leverages an admissible execution risk heuristic to quickly detect and prune overly-risky policy branches. Third, we demonstrate the usefulness of RAO* in two challenging domains of practical interest: power supply restoration and autonomous science agents.

Confidence-weighted (CW) learning is a successful online learning paradigm which maintains a Gaussian distribution over classifier weights and adopts a covariancematrix to represent the uncertainties of the weight vectors. However, there are two deficiencies in existing full CW learning paradigms, these being the sensitivity to irrelevant features, and the poor scalability to high dimensional data due to the maintenance of the covariance structure. In this paper, we begin by presenting an online-batch CW learning scheme, and then present a novel paradigm to learn sparse CW classifiers. The proposed paradigm essentially identifies feature groups and naturally builds a block diagonal covariance structure, making it very suitable for CW learning over very high-dimensional data.Extensive experimental results demonstrate the superior performance of the proposed methods over state-of-the-art counterparts on classification and feature selection tasks.

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.

Impressive dinners and high bar tabs could soon be things of the past with a new futuristic way of dating. Lucy Kelly, who used a droid to keep her place in line at an Apple store last year, wants to now use the telepresence robot to fill in the voids of intimacy that are often linked to online dating. Users could sit at home while the robot joins their date miles away at a coffee shop-- adding a more personal element to an inexpensive first encounter. Lucy Kelly, who used a droid to keep her place in line at an Apple store last year, wants to use a telepresence robot to fill in the voids of intimacy that are often linked to online dating.The robot can be controlled from anywhere there is internet, so users can have a conversation with a potential suitor miles away The telepresence robot, created by Double Robotics, has lateral stability control that lets the device move with ease across different surfaces and around obstacles. Its power drive enables it to go up to 80 percent faster than normal driving speed and is controlled by holding down the shift key on the keyboard.

News that a British Airways plane was hit by a drone before landing safely at Heathrow airport has once again highlighted how drones can be a nuisance and, potentially, dangerous. We all know about the military uses of drones (bomb lots of people, surveillance), and how drones can be used for nefarious purposes (theft, voyeurism), but there are actually some pretty cool uses for drones too. Last year the Ocean Alliance partnered with tech heads Yuneec to create "snot bots"; drones with petri dishes attached. For research purposes, the drones are flown over water to catch spray and snot from whales when the animals exhale. There is already a drone journalism lab at the University of Nebraska-Lincoln and the University of Missouri also offers a drone journalism course.

Apple co-founder and PC revolutionary, Steve "the Woz" Wozniak thinks there may be no need to own a car in future, thanks to developments in self-driving vehicles and Uber. Discussing artificial intelligence at the Future Transport Summit 2016 in Sydney, Wozniak said the implications of AI on transportation were bound to be huge, with a subsequent decline in ownership of cars, especially in cities where public transportation was more sophisticated. Wozniak said he anticipated a gradual shift towards private ownership of vehicles as they start to be seen less as a crucial part of our lives and more as means of getting from A to B. "Uber is a good example of [why drivers aren't needed] - if you just order it and a car comes, why does there need a driver? Without a driver we get all the benefits of lower cost and more safety," he said. "How long would it take before all the cars were not owned, or self-driving if they are owned? "Self-driving Ubers are the future, and we can use money we had set aside for a car to pay off student loans or buy new computers." Wozniak suggested that despite the dwindling necessity to own a car, some people will still hope to have one due to our human desire to own things. "I still think people will want to own a beautiful car even if you never see it… Look at the music industry, you used to buy your own CDs and vinyl, and I still have problems giving that up.

The initiative should take delivery drones one step closer to legitimacy, but it'll also underscore the limits of current technology. Australia's vast size makes it unlikely that drones will provide anything approaching coast-to-coast coverage. They just don't have the range to deliver to a village hundreds of miles away from the nearest large town, unfortunately. Even if drone service takes off, rural dwellers will likely have to make do with old-school airmail and delivery trucks.