2,132 research outputs found

    A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

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    The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio

    The experience of using role-play and simulated practice as an adjunct to paramedic placement learning

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    This study examines the current experiences of paramedic students regarding the perceptions, understanding and utilisation of role-play plus simulation in a paramedic degree programme. This area is underexplored, so it is situated in the context of paramedic practice, training and education landscape in UK, Australia, Canada and the USA, and cognate professions.The skills training in its original format remains, as does the on-the job clinical training (hospital placement and ambulance internship) as these are set regulatory requirements. Role-play and task focused simulation is used as part of syndicate learning for skills development. A mixed methodology, comprising both qualitative and quantitative approaches, including an exploratory sequential design, was used in this research. This was done in order to evaluate the student perceptions of their current placement experience and to explore the perception of combining simulation and role-playing.The study results show that the current educational model of clinical placement is flawed. After a brief exposure to an exemplar event, students preferred the combination of simulation and role-playing over the use of either technique independently. Adoption of this technique firstly requires a set definition of terminology and consistent interpretation within the discipline.A consolidation of the students’ experience is required by enhancing the mentorship supports. Further research is needed to design and develop the combination of role-playing and simulation to enhance student learning in the simulation laboratory. This study promotes positive social change by providing data to the educators and key decision makers of the paramedic programme on students’ perceptions of the benefits of a technique that is able to support instruction and augment the students’ clinical placement experience

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    The Categorical Deviation Effect May Be Underpinned by Attentional Capture: Preliminary Evidence from the Incidental Recognition of Distracters

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    The performance of a visual focal task is appreciably disrupted by an unexpected change (or deviation) in the properties of a task irrelevant auditory background. A vast amount of evidence suggests that a change in the acoustic properties of sound disrupts performance via attentional capture. However, an emerging body of evidence suggests that the disruption of task performance by a change in semantic category within a stream of sounds does not behave the same and is therefore not produced by attentional capture. This preliminary study aimed to further investigate whether the disruption produced by a categorical deviant was underpinned by attentional capture. In a single experiment, participants were presented with an irrelevant sound stream while they memorized a categorized list for free recall. We examined whether free recall performance was disrupted by an unexpected change in category within the sound and later investigated, via a surprise recognition test, whether participants had superior memory for deviant items as compared to items from the same positions in control sequences. Results revealed that the categorical deviation effect manifested in poorer free recall performance. Additionally, post-study, participants demonstrated better recognition memory for deviant items compared to control items. On the assumption that explicit recognition requires attentional encoding of deviant items, our results yield evidence that the categorical deviation effect may indeed be produced via attentional capture

    A Cultural History of Representational Shifts Toward Occidentalism

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    In 1978, Edward Said redefined Orientalism as a Western interpretation of the Middle East best characterized by an inherent cultural hostility. It’s essence, he declared, was the invariable distinction between Western superiority and Eastern inferiority. At the beginning of the twenty-first century, however, cultural critics Darrell Y. Hamamoto (2000), Vijay Prashad (2000), Jane Chi Hyun Park (2010), Jane Naomi Iwamura (2011), and David Weir (2011) have shed light on America’s long fascination with the Far East and more affirmative forms of Orientalism. Building on their work, I map developments of Far Orientalism on American screens at the turn of the century from an evolutionary perspective. In three case studies, I read audiovisual texts in their sociohistorical and media ecological contexts to trace representational shifts from the 1980s to the 2010s. Since the intersection of race and sex is significant for any discussion of Orientalism, I am mostly concerned with narratives featuring interracial romance. The first case study focuses on Michael Cimino’s crime thriller film Year of the Dragon (1985). My analysis is embedded in an examination of contemporary films related to the Vietnam War as well as the emergence of both the Model Minority myth and the redemption narrative. The next chapter is concerned largely with contextualizing Edward Zwick’s epic historical drama film The Last Samurai (2003) within the genre histories of both the American Western and the Japanese Eastern. These efforts culminate in investigations of the ways of how the film relates to the popularization of Buddhism and reworks the White Savior trope. The final case study offers an analysis of Ronald D. Moore’s science fiction television series Battlestar Galactica (2004-09) on the background of the rise of neoliberal economic policy and transhumanist philosophy as well as in relation to assimilation narratives and the genre history of cyberpunk. My research results demonstrate a trend from classic Orientalism to Techno-Orientalism, Spirito-Occidentalism, and outright Occidentalism. In the American imagination, I argue, East Asia has come to represent both the worst expression of modernity and the solution to its dehumanizing side. Neither have stereotypes been shattered nor has the geographical dualism been shed. Older fantasies merely have been complemented by more recent variations. I consider this study to be an extension of Said’s studies of Orientalism as well as a contribution to the fields of American cultural history, Asian American studies and, to a lesser extent, postcolonial studies, gender studies, and media studies

    Evolution of T cell receptor beta loci in salmonids

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    T-cell mediated immunity relies on a vast array of antigen specific T cell receptors (TR). Characterizing the structure of TR loci is essential to study the diversity and composition of T cell responses in vertebrate species. The lack of good-quality genome assemblies, and the difficulty to perform a reliably mapping of multiple highly similar TR sequences, have hindered the study of these loci in non-model organisms. High-quality genome assemblies are now available for the two main genera of Salmonids, Salmo and Oncorhynchus. We present here a full description and annotation of the TRB loci located on chromosomes 19 and 25 of rainbow trout (Oncorhynchus mykiss). To get insight about variations of the structure and composition of TRB locus across salmonids, we compared rainbow trout TRB loci with other salmonid species and confirmed that the basic structure of salmonid TRB locus is a double set of two TRBV-D-J-C loci in opposite orientation on two different chromosomes. Our data shed light on the evolution of TRB loci in Salmonids after their whole genome duplication (WGD). We established a coherent nomenclature of salmonid TRB loci based on comprehensive annotation. Our work provides a fundamental basis for monitoring salmonid T cell responses by TRB repertoire sequencing

    The Developer's Dilemma

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    This book explores this developer’s dilemma or ‘Kuznetsian tension’ between structural transformation and income inequality. Developing countries are seeking economic development—that is, structural transformation—which is inclusive in the sense that it is broad-based and raises the income of all, especially the poor. Thus, inclusive economic growth requires steady, or even falling, income inequality if it is to maximize the growth of incomes at the lower end of the distribution. Yet, this is at odds with Simon Kuznets hypothesis that economic development tends to put upward pressure on income inequality, at least initially and in the absence of countervailing policies. The book asks: what are the types or ‘varieties’ of structural transformation that have been experienced in developing countries? What inequality dynamics are associated with each variety of structural transformation? And what policies have been utilized to manage trade-offs between structural transformation, income inequality, and inclusive growth? The book answers these questions using a comparative case study approach, contrasting nine developing countries while employing a common analytical framework and a set of common datasets across the case studies. The intended intellectual contribution of the book is to provide a comparative analysis of the relationship between structural transformation, income inequality, and inclusive growth; to do so empirically at a regional and national level; and to draw conclusions from the cases on the varieties of structural transformation, their inequality dynamics, and the policies that have been employed to mediate the developer’s dilemma

    Co-designing the inflammatory arthritis self-management (aiM) intervention.

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    Self-management is an integral part of care for people living with inflammatory arthritis. The benefits of self-management interventions for people living with long-term conditions are well established. To date, most of the inflammatory arthritis self-management interventions have targeted only rheumatoid arthritis. Therefore, there is a need for a self-management intervention that reaches beyond just people living with rheumatoid arthritis. The overarching aim of this project was to co-design a self-management intervention for people across the inflammatory arthritis spectrum, based on the needs and preferences of co-designers (i.e. both people living with IA and healthcare professionals), as well as on the scientific literature. This project commenced with a mixed-method systematic review exploring the effectiveness and acceptability of existing inflammatory arthritis self-management interventions. Then, a two-phase, sequential multi-methods approach was employed. The first phase involved five asynchronous co-design workshops, guided by the Intervention Mapping Framework (Bartholomew et al. 2016). The second phase then explored participants' experience in participating in co-design research, including the barriers and facilitators to co-design. The mixed-method systematic review demonstrated that inflammatory arthritis self-management interventions produced a clinically meaningful reduction in fatigue and pain in people living with inflammatory arthritis. There was also some data to suggest that inflammatory arthritis self-management interventions have a beneficial effect on self-efficacy; knowledge; communication; health- related quality of life; and engagement with self-management behaviours. Additionally, the review found that inflammatory arthritis self-management interventions are generally acceptable to people living with inflammatory arthritis and healthcare professionals. Workshop findings provided important insight into the health problems and self-management needs of people living with inflammatory arthritis. The workshops also helped to identify the key content and features of the developed self-management intervention - i.e. the inflAmmatory arthrItis self-Management (aiM) intervention. Participants reported having an overall positive experience participating in the workshops, which provided them with an opportunity to meet others living with IA. The use of asynchronous workshops was felt to contribute to the participants' high attendance rate and the study's low attrition, despite IT-issues that were reported as a barrier to the participants' ability to fully participate in the workshops. This project developed a novel self-management intervention, which aims to improve the health status of people living with inflammatory arthritis through increased engagement with self-management strategies. The aiM intervention is based on the needs and preferences of the co-designers, and is grounded in theory and evidence. The findings have also provided new knowledge regarding the health problems related to people living with inflammatory arthritis, their self-management needs, and mechanisms that facilitate and inhibit co-design processes in an asynchronous remote context. Moving forward, it is recommended that the aiM intervention be tested for its feasibility and acceptability

    Capability-Based Routes for Autonomous Vehicles

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    The pursuit of vehicle automation is an ongoing trend in the automotive industry. Particularly challenging is the goal of introducing driverless autonomous vehicles (AVs) into road traffic. To realize this vision, a targeted development of autonomous driving functions is essential. However, a targeted development process is only possible if the driving functions are tailored as appropriately and completely as possible to the operational design domain (ODD). Regardless of use case, all AVs have one thing in common: driving at least one route from A to B - whether simple or complex. For operational purposes, it is therefore necessary to ensure that the driving requirements (DRs) of the potential routes within the ODD do not exceed the driving capabilities (DCs) of the AVs. Currently, there is no approach that accomplishes the identification of exceeded capabilities. This work presents a method for route-based specification of DRs and DCs for AVs. It addresses the core research question of how to identify routes with DRs that do not exceed the DCs of AVs. An initial analysis reveals the dependencies between route and DRs. Thereby, the scenery defined in the ODD is found to be a fundamental basis for the specification of behavioral requirements as part of the DRs. In combination with the applicable traffic rules, the scenery elements define the behavioral limits for AVs. These limits are specifically extracted and classified as behavioral demands from the scenery using an analysis of these combinations. To enable a route-based specification of DRs, the behavioral demands are modeled as behavior spaces and transformed into a generic map representation - the Behavior-Semantic Scenery Description (BSSD). Based on the BSSD, a method is developed that generates behavioral requirements based on the route-constrained concatenation of behavior spaces. As a result, in addition to the method itself, the associated behavioral requirements are available as a basis for the route-based specification of DRs and DCs. Constraints for the specification are defined by the developed concept for the matching of DRs and DCs. It is shown that the DRs are strongly dependent on the geometry and property of the scenery elements, so that equal behavioral requirements do not necessarily imply equal DRs. These dependencies are used for the specification enabling the definition of matching criteria for a selection of DRs and corresponding DCs. To realize the matching, a capability-based route search is developed and implemented. The route search incorporates all elaborated results of the work enabling the whole approach to be evaluated by applying it to a real road network. The evaluation shows that the identification of feasible routes for AVs based on the scenery is possible and which hurdles based on identified deficits still have to be overcome

    Ny forståelse av gasshydratfenomener og naturlige inhibitorer i råoljesystemer gjennom massespektrometri og maskinlæring

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    Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some crude oils form hydrates that do not agglomerate or deposit, but remain as transportable dispersions. This is commonly believed to be due to naturally occurring components present in the crude oil, however, despite decades of research, their exact structures have not yet been determined. Some studies have suggested that these components are present in the acid fractions of the oils or are related to the asphaltene content of the oils. Crude oils are among the worlds most complex organic mixtures and can contain up to 100 000 different constituents, making them difficult to characterise using traditional mass spectrometers. The high mass accuracy of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) yields a resolution greater than traditional techniques, making FT-ICR MS able to characterise crude oils to a greater extent, and possibly identify hydrate active components. FT-ICR MS spectra usually contain tens of thousands of peaks, and data treatment methods able to find underlying relationships in big data sets are required. Machine learning and multivariate statistics include many methods suitable for big data. A literature review identified a number of promising methods, and the current status for the use of machine learning for analysis of gas hydrates and FT-ICR MS data was analysed. The literature study revealed that although many studies have used machine learning to predict thermodynamic properties of gas hydrates, very little work have been done in analysing gas hydrate related samples measured by FT-ICR MS. In order to aid their identification, a successive accumulation procedure for increasing the concentrations of hydrate active components was developed by SINTEF. Comparison of the mass spectra from spiked and unspiked samples revealed some peaks that increased in intensity over the spiking levels. Several classification methods were used in combination with variable selection, and peaks related to hydrate formation were identified. The corresponding molecular formulas were determined, and the peaks were assumed to be related to asphaltenes, naphthenes and polyethylene glycol. To aid the characterisation of the oils, infrared spectroscopy (both Fourier Transform infrared and near infrared) was combined with FT-ICR MS in a multiblock analysis to predict the density of crude oils. Two different strategies for data fusion were attempted, and sequential fusion of the blocks achieved the highest prediction accuracy both before and after reducing the dimensions of the data sets by variable selection. As crude oils have such complex matrixes, samples are often very different, and many methods are not able to handle high degrees of variations or non-linearities between the samples. Hierarchical cluster-based partial least squares regression (HC-PLSR) clusters the data and builds local models within each cluster. HC-PLSR can thus handle non-linearities between clusters, but as PLSR is a linear model the data is still required to be locally linear. HC-PLSR was therefore expanded into deep learning (HC-CNN and HC-RNN) and SVR (HC-SVR). The deep learning-based models outperformed HC-PLSR for a data set predicting average molecular weights from hydrolysed raw materials. The analysis of the FT-ICR MS spectra revealed that the large amounts of information contained in the data (due to the high resolution) can disturb the predictive models, but the use of variable selection counteracts this effect. Several methods from machine learning and multivariate statistics were proven valuable for prediction of various parameters from FT-ICR MS using both classification and regression methods.Gasshydrater er et av hovedproblemene for Flow assurance i olje- og gassnæringen ettersom at de kan forårsake blokkeringer i oljerørledninger og prosessutstyr som krever at systemet må stenges ned. Tidligere studier har vist at noen råoljer danner hydrater som ikke agglomererer eller avsetter, men som forblir som transporterbare dispersjoner. Dette antas å være på grunn av naturlig forekommende komponenter til stede i råoljen, men til tross for årevis med forskning er deres nøyaktige strukturer enda ikke bestemt i detalj. Noen studier har indikert at disse komponentene kan stamme fra syrefraksjonene i oljen eller være relatert til asfalteninnholdet i oljene. Råoljer er blant verdens mest komplekse organiske blandinger og kan inneholde opptil 100 000 forskjellige bestanddeler, som gjør dem vanskelig å karakterisere ved bruk av tradisjonelle massespektrometre. Den høye masseoppløsningen Fourier-transform ion syklotron resonans massespektrometri (FT-ICR MS) gir en høyere oppløsning enn tradisjonelle teknikker, som gjør FT-ICR MS i stand til å karakterisere råoljer i større grad og muligens identifisere hydrataktive komponenter. FT-ICR MS spektre inneholder vanligvis titusenvis av topper, og det er nødvendig å bruke databehandlingsmetoder i stand til å håndtere store datasett, med muligheter til å finne underliggende forhold for å analysere spektrene. Maskinlæring og multivariat statistikk har mange metoder som er passende for store datasett. En litteratur studie identifiserte flere metoder og den nåværende statusen for bruken av maskinlæring for analyse av gasshydrater og FT-ICR MS data. Litteraturstudien viste at selv om mange studier har brukt maskinlæring til å predikere termodynamiske egenskaper for gasshydrater, har lite arbeid blitt gjort med å analysere gasshydrat relaterte prøver målt med FT-ICR MS. For å bistå identifikasjonen ble en suksessiv akkumuleringsprosedyre for å øke konsentrasjonene av hydrataktive komponenter utviklet av SINTEF. Sammenligninger av massespektrene fra spikede og uspikede prøver viste at noen topper økte sammen med spikingnivåene. Flere klassifikasjonsmetoder ble brukt i kombinasjon med ariabelseleksjon for å identifisere topper relatert til hydratformasjon. Molekylformler ble bestemt og toppene ble antatt å være relatert til asfaltener, naftener og polyetylenglykol. For å bistå karakteriseringen av oljene ble infrarød spektroskopi inkludert med FT-ICR MS i en multiblokk analyse for å predikere tettheten til råoljene. To forskjellige strategier for datafusjonering ble testet og sekvensiell fusjonering av blokkene oppnådde den høyeste prediksjonsnøyaktigheten både før og etter reduksjon av datasettene med bruk av variabelseleksjon. Ettersom råoljer har så kompleks sammensetning, er prøvene ofte veldig forskjellige og mange metoder er ikke egnet for å håndtere store variasjoner eller ikke-lineariteter mellom prøvene. Hierarchical cluster-based partial least squares regression (HCPLSR) grupperer dataene og lager lokale modeller for hver gruppe. HC-PLSR kan dermed håndtere ikke-lineariteter mellom gruppene, men siden PLSR er en lokal modell må dataene fortsatt være lokalt lineære. HC-PLSR ble derfor utvidet til convolutional neural networks (HC-CNN) og recurrent neural networks (HC-RNN) og support vector regression (HC-SVR). Disse dyp læring metodene utkonkurrerte HC-PLSR for et datasett som predikerte gjennomsnittlig molekylvekt fra hydrolyserte råmaterialer. Analysen av FT-ICR MS spektre viste at spektrene inneholder veldig mye informasjon. Disse store mengdene med data kan forstyrre prediksjonsmodeller, men bruken av variabelseleksjon motvirket denne effekten. Flere metoder fra maskinlæring og multivariat statistikk har blitt vist å være nyttige for prediksjon av flere parametere from FT-ICR MS data ved bruk av både klassifisering og regresjon
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