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

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

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

Neural Response Ranking for Social Conversation: A Data-Efficient Approach

The overall objective of 'social' dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good' system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings -- ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201

Natural Language Generation enhances human decision-making with uncertain information

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on human decision-making. We show that the use of Natural Language Generation (NLG) improves decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG lead to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations).Comment: 54th annual meeting of the Association for Computational Linguistics (ACL), Berlin 201

On-line control of grasping actions: object-specific motor facilitation requires sustained visual input

Dorsal stream visual processing is generally considered to underlie visually driven action, but when subjects grasp an object from memory, as visual information is not available, ventral stream characteristics emerge. In this study we use paired-pulse transcranial magnetic stimulation (TMS) to investigate the importance of the current visual input during visuomotor grasp. Previously, the amplitude of the paired-pulse motor evoked potentials (MEPs) in hand muscles before movement onset have been shown to predict the subsequent pattern of muscle activity during grasp. Specific facilitation of paired-pulse MEPs may reflect premotor–motor (PMC–M1) cortex connectivity. Here we investigate the paired-pulse MEPs evoked under memory-cued and visually driven conditions before grasping one of two possible target objects (a handle or a disc). All trials began with a delay period of 1200 ms. Then, a TMS pulse served as the cue to reach, grasp and hold the target object for 0.5 s. Total trial length was 5 s. Both objects were continually visible in both conditions, but the way in which the target object was designated differed between conditions. In the memory-cued condition, the target object was illuminated for the first 200 ms of the trial only. In the visually driven condition, the target object was illuminated throughout the 5 s trial. Thus, the conditions differed in whether or not the object to be grasped was designated at the time of movement initiation. We found that the pattern of paired-pulse MEP facilitation matched the pattern of object-specific muscle activity only for the visually driven condition. The results suggest that PMC–M1 connectivity contributes to action selection only when immediate sensory information specifies which action to make

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