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Constant checks on their presence and bureaucratic curbs on their movement leave international students feeling like security threats, says Alexander Mack. Equality and Human Rights Commission data show staff complaints take longer to investigate and are less likely to lead to redress.

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The goal of the symposium is to bring together the fields of formal verification, cognitive modeling, and task analysis to study the design and verification of real human-machine systems. Recent papers in each of these communities discuss modeling challenges and the application of basic formal verification in human-machine interaction; however, there is little communication between researchers in these different areas and there are many open questions that require cross-disciplinary collaboration.

The symposium is to bring together experts from many communities in an environment where it is possible to explore key research areas, common solutions, near-term research problems, and advantages in combining the best of the different communities. What model classes, methodologies, and constructs are appropriate for modeling human and machine activities in a way that is amenable to formal verification?

Examples include the following. What levels of abstraction are appropriate for such modeling, and what information is lost in using abstraction? What are the contexts, if any, for which the trade offs between authority between humans, autonomy, and model-based reasoning can be specified?

Normal Subgroup of a Group- Group Theory

What is the impact on design for including explicit meta- reasoning models in the human-machine interaction loop? What types of model-checkers are appropriate, and what other lessons from formal verification apply to human-machine systems?

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What are the ethical considerations of using verified models to allocate responsibility and authority between humans and machines? How can dynamic models evolve in the presence of learning agents, both human and machine, and in the presence of inaccurate mental models. In I Am a Strange Loop, Douglas Hofstadter argues that the key to understanding selves is the "strange loop", a complex feedback network inhabiting our brains and, arguably, constituting our minds.


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Further, humans have both conscious and unconscious minds, attention, emotions, partial self-reflection, a moral sense and many other aspects that are rarely addressed — yet seem critical for the creation of a safe self-sufficient autonomous system. This symposium will focus on the integration of these components into a coherent self-improving self. While autopoiesis and "functional consciousness" are obviously key topics, phenomenal consciousness is, preferably, off-topic.

This AAAI symposium will explore the intersection of robust intelligence RI and trust across multiple contexts among autonomous hybrid systems where hybrids are arbitrary combinations of humans, machines and robots. We seek methods for structuring teams or networks that increase robust intelligence and engender trust among a system of agents. But how can we determine the questions critical to the static and dynamic aspects of behavior and metrics of agent performance?

To better manage RI with AI to promote trust in autonomous agents and teams, our interest is in the theory, mathematics, computational models, and field applications at the intersection of RI and trust, not only in team-multitasking effectiveness or in modeling RI networks, but in the efficiency and trust engendered among interactants. We seek to understand the intersection of RI and trust for humans interacting with systems for example, teams, firms, networks , to use this information with AI to model RI and trust, and to predict outcomes from interactions among hybrids for example, multitasking operations.

Systems that learn, adapt, and apply experience to problems may be better suited to respond to novel environmental challenges. One could argue that such systems are "robust" to the prospect of a dynamic and occasionally unpredictable world. We expect systems that exhibit robustness would afford a greater degree of trust in hybrids that interact with the system. Robustness through learning, adaptation and structure is determined by predicting and modeling the interactions of autonomous hybrid enterprises.

We hypothesize such models improve enterprise intelligence by allowing autonomous entities to continually adapt within normalcy bounds, leading to greater reliability and trust. The focus of this symposium is how robust intelligence impacts trust in the system and how trust in the system impacts robustness. We will explore approaches to RI and trust including for example : intelligent networks, intelligent agents, and intelligent multitasking by hybrids. Jennifer Burke Boeing, jennifer.

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Classed Intersections: Spaces, Selves, Knowledges

Wagner gtri. With a nuanced analysis, it invites us to recognize the complexity of contemporary social intersections, but with clarity and focus. There is a range of excellent new empirical and critical material. Its fresh approach to knowledge, space and action creates a welcome counterpoint to the casual vilification of working class people.

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Munt, University of Sussex, UK 'While class is enjoying a revival in sociological and geographical analysis, the intersectional politics of class remains under-studied and under-theorized. Classed Intersections contains vibrant, insightful essays that interrogate the gender and sexual politics of class in a wide range of empirical and theoretical contexts. Routledge eBooks are available through VitalSource. Most VitalSource eBooks are available in a reflowable EPUB format which allows you to resize text to suit you and enables other accessibility features.


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