Resist, comply or workaround? An examination of different facets of user engagement with information systems

This paper provides a summary of studies of user resistance to Information Technology (IT) and identifies workaround activity as an understudied and distinct, but related, phenomenon. Previous categorizations of resistance have largely failed to address the relationships between the motivations for divergences from procedure and the associated workaround activity. This paper develops a composite model of resistance/workaround derived from two case study sites. We find four key antecedent conditions derived from both positive and negative resistance rationales and identify associations and links to various resultant workaround behaviours and provide supporting Chains of Evidence from two case studies

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Hierarchical agent supervision

Agent supervision is a form of control/customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. To facilitate supervision, it is often of interest to consider hierarchical models where a high level abstracts over low-level behavior details. We study hierarchical agent supervision in the context of the situation calculus and the ConGolog agent programming language, where we have a rich first-order representation of the agent state. We define the constraints that ensure that the controllability of in-dividual actions at the high level in fact captures the controllability of their implementation at the low level. On the basis of this, we show that we can obtain the maximally permissive supervisor by first considering only the high-level model and obtaining a high- level supervisor and then refining its actions locally, thus greatly simplifying the supervisor synthesis task

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Disasters: Issues for State and Federal Government Finances

Extreme events like hurricanes, earthquakes, or terrorist attacks present major challenges for fiscal systems at all levels of government. Analysts concerned with the fiscal and financial impacts of disasters must attempt to assess the likelihood of rare events of large magnitude such as Hurricane Katrina. Extreme value theory, applied here to flood damage data for Louisiana, offers one promising methodology for this purpose. The experience of Katrina and 9/11 also show that large disasters have large intergovernmental impacts. Individual states could, in principle, engage in more extensive ex ante financial and policy preparations for disasters, including disaster avoidance, but the “revealed institutional structure” exposed by recent experience shows that the US federal system shifts much of the economic incidence of local disasters to the rest of society through intergovernmental transfers. This raises policy questions regarding the assignment of responsibility for disaster avoidance in the US federation. In particular, Federal “ownership” of the consequences of disasters may invite or necessitate new forms of Federal “control” of subnational government.

Reprogramming the hand: bridging the craft skills gap in 3D/digital fashion knitwear design

Designer-makers have integrated a wide range of digital media and tools into their practices, many taking ownership of a specific technology or application and learning how to use it for themselves, often drawing on their experiential knowledge of established practices to do so. To date, there has been little discussion on how digital knitting practice has evolved within this context, possibly due to the complexity of the software, limited access to industrial machinery and the fact that it seems divorced from the idea of 'craft'. Despite the machine manufacturers' efforts to make knitting technology and software more user-friendly, the digital interface remains a significant barrier to knitwear designer-makers, generally only accessed via experienced technicians