To Look Austrian

While still in the midst of their study abroad experiences, students at Linfield College write reflective essays. Their essays address issues of cultural similarity and difference, compare lifestyles, mores, norms, and habits between their host countries and home, and examine changes in perceptions about their host countries and the United States. In this essay, Sierra Lemon describes her observations during her study abroad program at the Austro-American Institute of Education in Vienna, Austria

Reconfiguring Household Management in Times of Discontinuity as an Open System: The Case of Agro-food Chains

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article is based upon a heterodox approach to economics that rejects the oversimplification made by closed economic models and the mainstream concept of ‘externality.’ This approach re-imagines economics as a holistic evaluation of resources versus human needs, which requires judgement based on understanding of the complexity generated by the dynamic relations between different systems. One re-imagining of the economic model is as a holistic and systemic evaluation of agri-food systems’ sustainability that was performed through the multi-dimensional Governance Assessment Matrix Exercise (GAME). This is based on the five capitals model of sustainability, and the translation of qualitative evaluations into quantitative scores. This is based on the triangulation of big data from a variety of sources. To represent quantitative interactions, this article proposes a provisional translation of GAME’s qualitative evaluation into a quantitative form through the identification of measurement units that can reflect the different capital dimensions. For instance, a post-normal, ecological accounting method, Emergy is proposed to evaluate the natural capital. The revised GAME re-imagines economics not as the ‘dismal science,’ but as one that has potential leverage for positive, adaptive and sustainable ecosystemic analyses and global ‘household’ management. This article proposes an explicit recognition of economics nested within the social spheres of human and social capital which are in turn nested within the ecological capital upon which all life rests and is truly the bottom line. In this article, the authors make reference to an on-line retailer of local food and drink to illustrate the methods for evaluation of the five capitals model

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena

Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-the-art retrieval model, MemN2N, on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial 201

Crowd-sourcing NLG Data: Pictures Elicit Better Data

Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning representations (MRs) paired with Natural Language (NL) utterances. In this work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating different MRs with which to elicit data. We show that pictorial MRs result in better NL data being collected than logic-based MRs: utterances elicited by pictorial MRs are judged as significantly more natural, more informative, and better phrased, with a significant increase in average quality ratings (around 0.5 points on a 6-point scale), compared to using the logical MRs. As the MR becomes more complex, the benefits of pictorial stimuli increase. The collected data will be released as part of this submission.Comment: The 9th International Natural Language Generation conference INLG, 2016. 10 pages, 2 figures, 3 table

The Minimized Face of Internal Communication: An Exploration of How Public Relations Agency Websites Frame Internal Communication and its Connection to Social Media

Internal communication is increasingly vital to organizational success due to the influence of social media, yet it remains understudied within public relations research. Using a qualitative content analysis of 181 websites, this study examines how leading public relations agency websites frame the value of internal communication and its connection to social media. Findings reveal internal communication is largely missing from the frame. When explicitly referenced, it is mostly framed as synonymous with employee communication as a means for management to communicate to employees, though some portrayals are more robust. Websites frame internal communication’s value as enhancing financial outcomes by improving workplace culture, employee engagement, and workers’ willingness to support management’s preferred organization brand or reputation. Social media are disconnected from internal communication and are mostly framed as tools that require additional employee training to use in order to reach external audiences. A handful of agencies urge organizations to include social media and internal stakeholders within the internal communication function. Recommendations are made for future internal communication research and practice

Asymmetrical booster ascent guidance and control system design study. Volume 2: SSFS math models - Ascent

The engineering equations and mathematical models developed for use in the space shuttle functional simulator (SSFS) are presented, and include extensive revisions and additions to earlier documentation. Definitions of coordinate systems used by the SSFS models and coordinate tranformations are given, along with documentation of the flexible body mathematical models. The models were incorporated in the SSFS and are in the checkout stage