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.

With rapid population growth and urbanization, emergency services in various cities around the world worry that the current transportation infrastructure is no longer adequate for large-scale evacuations. This paper considers how to mitigate this issue through infrastructure upgrades, such as the additions of lanes to road segments and the raising of bridges and roads. The paper proposes a MIP model for deciding the most effective infrastructure upgrades as well as a Benders decomposition approach where the master problem jointly plans the upgrades and evacuation routes and the subproblem schedules the evacuation itself. Experimental results demonstrate the practicability of the approach on a real case study, filling a significant need for emergencies services.

Several discriminative models with latent variables were also explored to learn better alignment models in a wetlab The Conference of the North American Chapter of the Association experiment domain (Naim et al. 2015). As alignment is for Computational Linguistics: Human Language often the first step in many problems involving language and Technology (NAACL HLT) is a premier conference reporting vision, these approaches and empirical results provide important outstanding research on human language technology.

We consider the link prediction (LP) problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. Many existing methods reduce LP to binary classification. However, the dominance of absent links in real world networks makes misclassification error a poor performance metric. Instead, researchers have argued for using ranking performance measures, like AUC, AP and NDCG, for evaluation. We recast the LP problem as a learning to rank problem and use effective learning to rank techniques directly during training which allows us to deal with the class imbalance problem systematically. As a demonstration of our general approach, we develop an LP method by optimizing the cross-entropy surrogate, originally used in the popular ListNet ranking algorithm. We conduct extensive experiments on publicly available co-authorship, citation and metabolic networks to demonstrate the merits of our method.

This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we propose the outlines of an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which both the example of action and its justification influence the moral intuitions of others. The signals an agent provides to others, and the signals received from others, help each agent determine which others are suitable cooperative partners, and which are likely to defect. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we take steps toward an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which examples of moral judgments and their justifications influence the moral development both of individuals and of the society as a whole. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

Numerous studies have identified differences between left-handed and right-handed people, especially in the fields of psychology and neuroscience. Using a social media setting, this paper presents a data-driven approach to explore whether a person's handedness can be identified given his or her writing, and shows handedness characteristics that can be inferred from language.

Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using models built from a corpus of texts from multiple cultures in order to learn about each represented people group separately. To do this, we employ a topic modeling approach to quantify open-ended writing responses describing personal values and everyday behaviors in two distinct cultures. We show that some topics are more prominent in one culture compared to the other, while other topics are mentioned to similar degrees. Furthermore, our results indicate that culture influences how value-behavior relationships are exhibited. While some relationships exist in both cultural groups, in most cases we see that the observed relations are dependent on the cultural background of the data set under examination.

This paper outlines three projects that lay the foundation for a trans-disciplinary approach to the creation of interactive, multi-sensory devices combining biofeedback, virtual reality, and physical/virtual human-machine interactions. We explore new possibilities for interoperability and enhancing interoception and mindfulness with potential research contributions for novel personal, professional and medical applications.