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Discusses the distribution of application functions to process‐oriented field devices and process‐remote components in distributed automation systems. Describes the use of fieldbus systems, function block technology based on standardised function blocks and a suitable infrastructure to enable these function blocks to cooperate during run time.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

Purpose – This chapter examines the process of radicalization, deradicalization, and support for intelligence agencies in a few well-known cases of terrorists who turned into informants.

Methodology/Approach – Five cases studies are utilized to demonstrate the process of engagement in, disengagement from, and revolt against terrorist groups. Existing literature on radicalization and deradicalization is set against the context of these case studies.

Findings – By drawing upon the experiences of terrorists who turned into informants, it is possible to prove theories on radicalization and deradicalization. In particular, the process of cognitive radicalization presumes that extremist beliefs can also be rejected (deradicalization), while the process of behavioral radicalization presumes that terrorists can distance themselves from extremist behaviors (disengagement).

Originality/Value – Scholarship has traditionally focused on “underdogs” of all kinds, with a less keen interest in elites or the actors operating on their behalf. The work of informants has often remained in a dimly lit corner of academic research. This chapter helps illuminate the path undertaken by terrorists who become informants for Western security apparatus.

Purpose – Drawing on Transformative Learning (TL) theory, the authors suggest a new and novel way to approach the study of violent radicalization.

Methodology/Approach – First, their argument is supported by the development of a Transformative Radicalization (TR) framework that borrows and adapts the core tenets of TL theory. Second, they provide a preliminary illustrative exploration of TR using two autobiographical accounts of militant radicalization (Islamist and Anarchist) from the UK and Canada.

Findings – Radicalization is a cognitive and emotional process of change that prepares and motivates an individual to pursue violent behavior. That process of change is incremental; individuals learn and adopt novel political, social, ideological, and/or religious ideals that justify and legitimize indiscriminate violence. The TR framework provides a more nuanced appreciation for the cognitive aspects involved in this process. The authors’ empirical illustrations provide guidance on how subsequent research might use original interview data on individual radicalization processes to develop more in-depth, cross-case comparisons.

Originality/Value – This theory builds a cross-disciplinary understanding of violent radicalization that highlights the way adults learn, alter their meaning perspectives, and change their behavior.

hydrolysis can be described as the reaction between water and another material in which a definite chemical change occurs. Materials which do not react with water can be said to possess hydrolytic stability. Polyurethanes can be hydrolysed by water under certain conditions and the chemical changes involved result in a deterioration in properties of the material. These changes can be followed chemically or physically but under severe conditions the degradation is readily apparent. In these cases the polyurethane can change from being a tough elastic material to a soft plastic substance with little or no strength.

Purpose – This chapter highlights how counter-radicalization, as a manifestation of diffuse securitizing, impacts the work of Muslim civil society organizations (CSOs) in Canada.

Methodology – The author presents how Muslim communities and their civil society representatives experience and adapt to the pressures from counter-radicalization policies. Data for the analysis are drawn from 16 semi-structured, anonymized interviews with managers and board members of prominent Muslim CSOs that are based in urban centers in Canada with high density of Muslim populations.

Findings – Though counter-radicalization policies are advanced under the rubric of community-orientedness and risk governance, security discourse and practice constructs radicalization as a problem within Muslim communities treating them as suspects who are “potentially radical.” Despite this framing, Muslim CSOs are cooperating with state security agencies in counter-radicalization efforts but are doing so cognizant of the immense power the state exerts over them in such “partnerships.” CSOs are raising questions about the selective nature of security practice which views Muslims as dangerous and violent but fails to fully acknowledge their reality as victims of Islamophobic violence. CSOs are using anti-racism, anti-oppression, and rights-based frames to call out the discriminatory treatment of Muslims under national security.

Originality/Value – The author’s study contributes to a community perspective in counterterrorism and counter-radicalization research that is dominated by analyses from “above.” By sharing the experiences of Canadian Muslim CSOs under counter-radicalization, the author illustrates the practice of “diffuse securitizing” and how it limits the work of civil society in liberal democracies.

Project ACORN (Access to Course Readings via Networks) sits within the Electronic Short Loan strand of the Electronic Libraries (eLib) Programme and has been developed by a team at the Pilkington Library at Loughborough University. Having established procedures for making journal articles in short loan collections available electronically, the next stage was to test the portability of these procedures to Leicester University. This paper describes a study undertaken by the Project ACORN team to investigate the portability of the technical model by implementing it at Leicester University, and discusses the main challenges faced (authentication and printing), the solutions proposed by the ACORN team, some possible future solutions, and recommendations for libraries looking to set up an ACORN‐type service.