This paper proposes an epistemological transition based on Edgar Morin's complexity paradigm to analyse authenticity in a complex tourism environment, avoiding fragmentation, and integrating relevant actors and relationships. The results show that storytelling is an important element of these tourism experiences, legitimising and unifying the authenticity of the experience and relating objects, social environment and individual experiences. The size of the tour groups and the rigidity of the itinerary were important elements for constructing authenticity. Tourists, service providers and government bodies all directly or indirectly participate as co-creators, making the perception of authenticity a constant negotiation between the elements of the experience and the actors involved in it.
Epistemological relativism in tourism studies has been conceivably paralyzed by the concept of a, or, the "paradigm." In this review article, Platenkamp metaphorically identifies these paradigms with the islands that Odysseus visited (all those centuries ago) during his well-recorded journey to Ithaca. In this context, therefore, Ithaca is changed (by Platenkamp) from being just an idyllic Greek homeland into a contemporary, hybridized world like-in our time-of the multilayered network society in Africa of the capital of Ghana, Kumasi. The basic question for Platenkamp, then, is that of how tourism studies researchers can (or ought?) leave their safe islands (i.e., their paradigms) and organize their own paradigm dialog (after Guba) with others around them on their uncertain and risky voyage to Kumasi. In an attempt to clarify this vital kind of dialog, Platenkamp introduces Said's principles of reception and resistance, but also focuses on the distinction between different modes of "knowledge production" that have been introduced into the social sciences since the 1990s. In this light, to Platenkamp, the uncertainty of this ongoing/unending epistemological quest remains crucial: to him, all (almost all?) believers in a, or any, paradigm within tourism studies are unhealthily "overimmunized" by the tall claims and the perhaps undersuspected strategies of the particular "paradigm" they follow. (Abstract by the Reviews Editor).
MULTIFILE
Although empathy is an essential aspect of co-design, the design community lacks a systematic overview of the key dimensions and elements that foster empathy in design. This paper introduces an empathic formation compass, based on a comparison of existing relevant frameworks. Empathic formation is defined here as the formative process of becoming an empathic design professional who knows which attitude, skills and knowledge are applicable in a co-design process. The empathic formation compass provides designers with a vocabulary that helps them understand what kind of key dimensions and elements influence empathic formation in co-design and how that informs designers’ role and design decisions. In addition, the empathic formation compass aims to support reflection and to evaluate co-design projects beyond the mere reliance on methods. In this way, empathic design can be made into a conscious activity in which designers regulate and include their own feelings and experiences (first-person perspective), and decrease empathic bias. We identify four important intersecting dimensions that empathy is comprised of in design and describe their dynamic relations. The first two opposing dimensions are denoted by empathy and differentiate between cognitive design processes and affective design experiences, and between self-and other orientation. The other two dimensions are defined by design research and differentiate between an expert and a participatory mindset, and research-and design-led techniques. The empathic formation compass strengthens and enriches our earlier work on mixed perspectives with these specific dimensions and describes the factors that foster empathy in design from a more contextual position. We expect the empathic formation compass—combined with the mixed perspectives framework—to enhance future research by bringing about a deeper understanding of designers’ empathic and collaborative design practice.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
