Högskolan i Skövde

Social network analysis techniques have shown a potential for influencing gradu-ates to meet industry needs. In this paper, we propose a social-web driven solutions to bridge formal education and industry needs. The proposed career development frame-work utilizes social network analytics, influence diffusion algorithms and persuasive technology models along three phases: (1) career readiness to measure and visualize the general cognitive dispositions required for a successful career in the 21st Century, (2) career prediction to persuade future graduates into a desired career path by clustering learners whose career prospects are deemed similar, into a community of practice; and (3) career development to drive growth within a social network structure where social network analytics and persuasive techniques are applied to incite the adoption of desired career behaviors. The process starts by discovering behavioral features to create a cognitive profile and diagnose individual deficiencies. Then, we develop a fuzzy clustering algorithm that predicts similar patterns with controlled constraint-violations to construct a social structure for collaborative cognitive attainment. This social framework facilitates the deployment of novel influence diffusion approaches, whereby we propose a reciprocal-weighted similarity function and a triadic clo-sure approach. In doing so, we investigate contemporary social network analytics to maximize influence diffusion across a synthesized social network. The outcome of this social computing approach leads to a persuasive model that supports behavioral changes and developments. The performance results obtained from both analytical and experi-mental evaluations validate our data-driven strategy for persuasive behavioral change.

The need for graduates who are immediately prepared for employment has been widely advocated over the last decade to narrow the notorious gap between industry and higher education. Current instructional methods in formal higher education claim to deliver career-ready graduates, yet industry managers argue their imminent workforce needs are not completely met. From the candidates view, formal academic path is well defined through standard curricula, but their career path and supporting professional competencies are not confidently asserted. In this paper, we adopt a data analytics approach combined with contemporary social computing techniques to measure, instil, and track the development of professional competences of learners in higher education. We propose to augment higher-education systems with a virtual learning environment made-up of three major successive layers: (1) career readiness, to assert general professional dispositions, (2) career prediction to identify and nurture confidence in a targeted domain of employment, and (3) a career development process to raise the skills that are relevant to the predicted profession. We analyze self-declared career readiness data as well as standard individual learner profiles which include career interests and domain-related qualifications. Using these combinations of data sources, we categorize learners into Communities of Practice (CoPs), within which learners thrive collaboratively to build further their career readiness and assert their professional confidence. Towards these perspectives, we use a judicious clustering algorithm that utilizes a fuzzy-logic objective function which addresses issues pertaining to overlapping domains of career interests. Our proposed Fuzzy Pairwise-constraints K-Means (FCKM) algorithm is validated empirically using a two-dimensional synthetic dataset. The experimental results show improved performance of our clustering approach compared to baseline methods.

The metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users’ behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users’ behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends’ behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.

Detecting users and data in the web is an important issue as the web is changing and new information is created every day. In this paper we will discuss six different clustering algorithms that are related to the intelligent web. These algorithms will help us to identify groups of interest in the web, which is very necessary in or- der to perform certain actions on specific group such as targeted advertisement. The algorithms under consideration are: Single-Link algorithm, Average-Link algorithm, Minimum-Spanning-Tree Single-Link algorithm, K-means algorithm, ROCK algorithm and DBSCAN algorithm. These algorithms are categorized into three groups: Hierarchical, Partitional and Density-based algorithms. We will show how each algorithm works and discuss their advantages and disadvantages. We will compare these algorithms to each others and discuss their ability to handle social web data which are of large datasets and high dimensionality. Finally a case study related to using clustering in social networks will be discussed.

Recently, there has been a significant growth in the science of networks, as well as a big boom in social networking sites (SNS), which has arguably had a great impact on multiple aspects of everyday life. Since the beginnings of the World Wide Web, another fast-growing field has been that of recommender systems (RS), which has furthermore had a proven record of immediate financial importance, given that a well-targeted online recommendation often translates into an actual purchase. Although in their beginnings, both SNSs as well as RSs had largely separate paths as well as communities of researchers dealing with them, recently the almost immediate synergies arising from bringing the two together have started to become apparent in a number of real-world systems. However, this is just the beginning; multiple potentially beneficial mutual synergies remain to be explored. In this chapter, after introducing the two fields, we will provide a survey of their existing interaction, as well as a forward-looking view on their potential future.

A network of Internet-based intermediaries that guarantee delivery and payment in e-commerce could help bolster consumer and merchant confidence.

Today’s college students have grown up with technology. These digital natives typically gravitate toward group activities in technology embedded social contexts. However, despite this multidimensional evolution, little has changed in conventional classrooms where they build their education experience. We investigate learning models in a classroom environment which still remains the main driver of education today. We describe a conversational learning model based on group activities which involve multi-party conversations. We implement this model in a technology-enhanced studio-classroom to “visualize” conversations which otherwise would remain abstract to learners. Teachers are empowered with instructional patterns to guide their changing role in this novel classroom environment. Based on standard assessment indicators, we conduct an experimental analysis which results show interesting tradeoffs of learning performance that favor the proposed conversational learning approach compared to those obtained from conventional instruction.

Formal instruction still occurs predominantly in classroom environment, despite the rapid progress in online learning. The authors present digital patterns for classroom instruction to support classroom learning and assist instruction developers. They advocate design patterns and learning activities to encapsulate digital educational resources into pedagogically sound reusable components. This chapter proposes pedagogical patterns to drive content providers to meet learner profiles in regular tertiary education environments. Taking their roots from behavioral learning discipline, these patterns are digitized as part of a separate process in learning production workflows. The authors describe the overall organization of a learning production enterprise and position the pedagogical factory to drive the supply of learning contents in increasingly digitized tertiary education institutions. This chapter reunites digital patterns to support personalization and conversational pedagogies in classroom contexts.

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

There are still disparities in technology-access despite economic pressures and widespread promises to overcome them. The induced digital gap defines the degree of digital citizenship for which, unified policies have yet to be drawn at various educational levels to reduce that gap. The quest for a broad participation to develop digital citizenship competencies needs further investigations into innovative educational approaches, pedagogical methods, and routine practices that foster digital literacy, and narrows the digital divide. This special issue accumulates original theoretical and empirical research contributions across contemporary digital citizenship perspectives. The final selection of the papers explores digital citizenship concepts such as ethics, digital literacy and participation, in various contexts to develop opportunities for a wider engagement in social actions. The international perspectives of contributing authors shed lights on digital citizenship prospects across unique contexts among different nations. 

Concerns for parking are becoming imminent to best support the urban core. These persistent parking problems could be turned into new opportunities, brought by current trends in meeting the globally connected continuum. This paper reveals a work-in- progress to capitalize on private land properties for parking, in order to relieve stress on public agencies, create new sources of revenue, and enlist new entities in the intermediary market. These intermediaries, labelled as Parking Service Providers (or PSPs) play a broker role through advertising parking lots on a shared cloud platform. To streamline these business collaborations and related processes, physical parking lots are augmented with Internet connectivity allowing cloud-provided applications to congregate these lots into a larger inventory. The Internet of Things (IoT) paradigm expands the scope of cloud-based intelligent car parking services in smart cities, with novel applications that better regulate car-parking related traffic. This paper presents a work-in-progress agenda that contributes to new business solutions and state-of-the-art research impacts. We reveal a multi-layered system of PSP-business model through interdisciplinary research blocks where original results are expected to be made at each layer.

Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

Organisations could benefit from the improvements in collaborative learning and increased control gained by the use of open source software. EtherPad enables simultaneous updates on a shared document from remotely located participants. We discuss the adoption life-cycle of this open source tool in the context of a strategic intervention, with a focus on a learning agenda to guide the installation and configuration processes, and to reduce the learning curve involved in the adoption experience.

Smart grid employs ICT infrastructure and network connectivity to optimize efficiency and deliver new functionalities. This evolu- tion is associated with an increased risk for cybersecurity threats that may hamper smart grid operations. Power utility providers need tools for assessing risk of prevailing cyberthreats over ICT infrastructures. The need for frameworks to guide the develop- ment of these tools is essential to define and reveal vulnerability analysis indicators. We propose a data-driven approach for design- ing testbeds to evaluate the vulnerability of cyberphysical systems against cyberthreats. The proposed framework uses data reported from multiple components of cyberphysical system architecture layers, including physical, control, and cyber layers. At the phys- ical layer, we consider component inventory and related physi- cal flows. At the control level, we consider control data, such as SCADA data flows in industrial and critical infrastructure control systems. Finally, at the cyber layer level, we consider existing secu- rity and monitoring data from cyber-incident event management tools, which are increasingly embedded into the control fabrics of cyberphysical systems.

This document reports a technical description of ELVIRA project results obtained as part of Work- package 4.1 entitled “Multi-agent systems for power Grid monitoring”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity. This report outlines intelligent approaches that continuously analyze data flows generated by Supervisory Control And Data Acquisition (SCADA) systems, which monitor contemporary power grid infrastructures. However, cybersecurity threats and security mechanisms cannot be analyzed and tested on actual systems, and thus testbed simulators are necessary to assess vulnerabilities and evaluate the infrastructure resilience against cyberattacks. This report suggests an agent-based model to simulate SCADA- like cyber-components behaviour when facing cyber-infection in order to experiment and test intelligent mitigation mechanisms. 

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.

The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

New technological advances in user mobility and context immersion are enabling novel adaptive and pervasive learning models in ambient environments. These advances allow physical learning spaces with embedded computing capabilities to provide an augmented self-aware learning experience. In this paper, we aim at developing a novel ubiquitous learning model within a pervasive smart campus environment. The goal of our research consists of identifying the steps towards building such an environment and the involved learning processes. We define a model of a smart campus, and advocate learning practices in the light of new paradigms such as context-awareness, ubiquitous learning, pervasive environment, resource virtualization, autonomic computing and adaptive learning. We reveal a comprehensive architecture that defines the various components and their inter-operations in a smart educational environment. The smart campus approach is presented as a composition of ambient learning spaces, which are environments where physical learning resources are augmented with digital and social services. We present a model of these spaces to harness future ubiquitous learning environments. One of the distinguished features of this model is the ability to unleash the instructional value of surrounding physical structures. Another one is the provision of a personalized learning agenda when moving across these ambient learning environments. To achieve these goals, we profile learners and augment physical campus structures to advocate context-aware learning processes. We suggest a social community platform for knowledge sharing which involves peer learners, domain experts as well as campus physical resources. Within this pervasive social scope, learners are continuously immersed in a pedagogically supported experiential learning loop as a persuasive approach to learning. A learning path, which responds to learners’ goals and qualifications, autonomously guides learners in achieving their objectives in the proposed smart campus. We evaluated our ubiquitous learning approach to assert the performance of these building blocks in the proposed smart campus model. The results show interesting tradeoffs and promising insights.

The concept of Smart Cities is an emerging social and technology innovation, attracting large public and private investments at a global scale, arguing for the effective exploitation of digital technologies to drive quality of living and sustainable growth. However, these investments mainly focus in smart technical infrastructure, and they have yet to be systematically complemented with efforts to prepare the human capital of future smart cities in terms of core competences anticipated for exploiting their potential. In this context, this chapter introduces “cyber-physical learning” as a generic overarching model to cultivate Digital Smart Citizenship competence. The proposed approach exploits the potential of Internet of Things technologies to create authentic blended and augmented learning experiences. Proof-of-concept case studies of the proposed cyber-physical learning approach, to develop smart household energy management competences, are presented and discussed as a field of application. Finally, the findings of a survey with university students for eliciting their attitudes to engage with cyber-physical learning environments for enhancing their digital smart citizenship competences are reported.

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.

Ubiquitous learning involves a large-scale service network organized as a social infrastructure. These environments weave together context service dissemination, learner profiling as well as autonomic control of the induced network traffic. The objective of the research presented in this paper is to derive a Quality of Service aware model of ubiquitous learning services based on typical learning schemes. These pedagogical patterns are designed to match various learning situations in terms of learning context, learner profile and network infrastructure. They particularly represent classes of services in ubiquitous learning environments to prioritize traffic so that less important traffic does not consume network bandwidth and slow down or halt the delivery of more important traffic. We analyze formally and empirically the network traffic requirements of a proposed learning service quality controller to support providers of learning services allocating resources in a pervasive learning environment.

Cloud service providers typically compose their services from a number of elementary services, which are developed in- house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user’s mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context (QoC) criteria that combine cloud-service provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

Real-world things are increasingly becoming fully qualified members of the Web. From, pacemakers and medical records to children’s toys and sneakers, things are connected over the Web and publish information that is available for the whole world to see. It is crucial that there is secure access to this Web of Things (WoT) and to the related information published by things on the Web. In this paper, we introduce an architecture that encompasses Web-enabled things in a secure and scalable manner. Our architecture utilizes the features of the well-known role-based access control (RBAC) to specify the access control policies to the WoT, and we use cryptographic keys to enforce such policies. This approach enables prescribers to WoT services to control who can access what things and how access can continue or should terminate, thereby enabling privacy and security of large amount of data that these things are poised to flood the future Web with.

This chapter focuses on the theoretical and technological aspects of designing mobile learning (m-learning) services that deliver context-aware learning resources from various locations and devices. Context-aware learning is an important requirement for next generation intelligent m-learning systems. The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this research work, context reflects timeliness and mobility to nurture pervasive instruction throughout the learning ecosystem. In this context of ubiquity that is supported by a new generation of mobile wireless networks and smart mobile devices, it is clear that the notion of context plays a fundamental role since it influences the computational capabilities of the used technology. In particular, three types of context awareness are being considered in this work —platform-awareness, learner-awareness, and task-awareness. In this research work, these contextual elements are defined at the semantic level in order to facilitate discoverability of context-compliant learning resources, adaptability of content and services to devices of various capabilities, and adaptability of services to task at hand and interaction history. The work presented in this chapter contributes towards this direction, making use of the progress in Semantic Web theory and mobile computing to enable context-aware learning that satisfies learning timeliness and mobility requirements.

The development of systems that can generate automatically instructional material is a challenging goal for the e-learning community. These systems pave the way towards large scale e-learning deployment as they produce instruction on-demand for users requesting to learn about any topic, anywhere and anytime. However, realizing such systems is possible with the availability of vast repositories of web information in different formats that can be searched, reused and integrated into information-rich environments for interactive learning. This paradigm of learning relieves instructors from the tedious authoring task, making them focusing more on the design and quality of instruction. This paper presents a mobile learning system (Mole) that supports the generation of instructional material in M-Learning (Mobile Learning) contexts, by reusing and integrating heterogeneous hypermedia web resources. Mole uses open hypermedia repositories to build a Learning Web and to generate learning objects including various hypermedia resources that are adapted to the user context. Learning is delivered through a nice graphical user interface allowing the user to navigate conveniently while building their own learning path. A test case scenario illustrating Mole is presented along with a system evaluation which shows that in 90% of the cases Mole was able to generate learning objects that are related to the user query.

Mobile services are constantly evolving, thanks to improvements in performance of mobile devices and wireless networks. As a result, there is a need for and efficient supply of discovery processes that will even allow non-technical users and developers to publish, discover and access services in a mobile environment where non-functional properties (context and quality of service information) play an important role in the discovery process in conjunction to functional properties. In this paper, we propose a user-centric mobile services discovery framework that enriches functional descriptions of mobile services with semantic annotations from DBpedia knowledge (the semantically-structured version of Wikipedia) which covers multiple domains and provides lightweight ontologies. In addition, it offers open tools that can be used to simplify the provisioning and discovery of mobile services. The framework allows users to rank services using non-monotonic rules, which define their desired choices based on the context and quality of service information. Experimental results show that our framework provides efficient discovery results of efficient mobile services.

Cyber-Physical Systems (CPSs) are augmenting traditionalCritical Infrastructures (CIs) with data-rich operations. Thisintegration creates complex interdependencies that exposeCIs and their components to new threats. A systematicapproach to threat modeling is necessary to assess CIs’ vulnerabilityto cyber, physical, or social attacks. We suggest anew threat modeling approach to systematically synthesizeknowledge about the safety management of complex CIs andsituational awareness that helps understanding the nature ofa threat and its potential cascading-effects implications.

The assessment of curriculum outcomes is an essential element for continuous academic improvement. However, the collection, aggregation and analysis of assessment data are notoriously complex and time-consuming processes. At the same time, only few developments of supporting electronic processes and tools for continuous academic program assessment and curriculum performance feedback have emerged. In this paper, we introduce a novel course assessment process supported by a Web based interface that articulates and streamlines the assessment data collection, performance evaluation and tracking of remedial recommendations. To close the assessment loop, the Web interface provides also a mechanism to follow up on the implementation of remedial recommendations and analyzes their associated reflective actions during the subsequent course assessment cycle. A guide to map assessment instruments to the course and overall program outcomes is advocated by the proposed tool to propagate the course assessment results towards higher educational objectives (e.g., student outcomes) in a dashboard-like assessment interface. This approach streamlines improvements in education through reflecting the achievement of course outcomes on the achievement of higher educational objectives. In addition, the tool maps the course outcomes to the corresponding course outlines to facilitate the detection of areas where revisions in the instruction and content is needed, and to best respond to recommendations and remedial actions. We provide a methodical approach as well as a Web-based automation of the assessment process, which we evaluate in the context of our regular academic assessment cycles that have eventually led to a successful international accreditation experience. The collected assessment data shows a significant improvement in the achievement rate of the student outcomes after deploying the tool.

This document reports a technical description of ELVIRA project results obtained as part of Work-package 3.1&3.2 entitled “Taxonomy of Critical Infrastructure (Taxonomy of events + Taxonomy of CI component and relationship)”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity.

Dynamic data-driven vulnerability assessments face massive heterogeneous data contained in, and produced by SOCs (Security Operations Centres). Manual vulnerability assessment practices result in inaccurate data and induce complex analytical reasoning. Contemporary security repositories’ diversity, incompleteness and redundancy contribute to such security concerns. These issues are typical characteristics of public and manufacturer vulnerability reports, which exacerbate direct analysis to root out security deficiencies. Recent advances in machine learning techniques promise novel approaches to overcome these notorious diversity and incompleteness issues across massively increasing vulnerability reports corpora. Yet, these techniques themselves exhibit varying degrees of performance as a result of their diverse methods. We propose a cognitive cybersecurity approach that empowers human cognitive capital along two dimensions. We first resolve conflicting vulnerability reports and preprocess embedded security indicators into reliable data sets. Then, we use these data sets as a base for our proposed ensemble meta-classifier methods that fuse machine learning techniques to improve the predictive accuracy over individual machine learning algorithms. The application and implication of this methodology in the context of vulnerability analysis of computer systems are yet to unfold the full extent of its potential. The proposed cognitive security methodology in this paper is shown to improve performances when addressing the above-mentioned incompleteness and diversity issues across cybersecurity alert repositories. The experimental analysis conducted on actual cybersecurity data sources reveals interesting tradeoffs of our proposed selective ensemble methodology, to infer patterns of computer system vulnerabilities.

Current vulnerability scoring mechanisms in complex cyber-physical systems (CPSs) face challenges induced by the proliferation of both component versions and recurring scoring-mechanism versions. Different data-repository sources like National Vulnerability Database (NVD), vendor websites as well as third party security tool analysers (e.g. ICS CERT and VulDB) may provide conflicting severity scores. We propose a machine-learning pipeline mechanism to compute vulnerability severity scores automatically. This method also discovers score correlations from established sources to infer and enhance the severity consistency of reported vulnerabilities. To evaluate our approach, we show through a CPS-based case study how our proposed scoring system automatically synthesises accurate scores for some vulnerability instances, to support remediation decision-making processes. In this case study, we also analyse the characteristics of CPS vulnerability instances. 

Despite their wide proliferation, complex cyber–physical systems (CPSs) are subject to cybersecurity vulnerabilities and potential attacks. Vulnerability assessment for such complex systems are challenging, partly due to the discrepancy among mechanisms used to evaluate their cyber-security weakness levels. Several sources do report these weaknesses like the National Vulnerability Database (NVD), as well as manufacturer websites besides other security scanning advisories such as Cyber Emergency Response Team (CERT) and Shodan databases. However, these multiple sources are found to face inconsistency issues, especially in terms of vulnerability severity scores. We advocate an artificial intelligence based approach to streamline the computation of vulnerability severity magnitudes. This approach decreases the error rate induced by manual calculation processes, that are traditionally used in cybersecurity analysis. Popular repositories such as NVD and SecurityFocus are employed to validate the proposed approach, assisted with a query method to retrieve vulnerability instances. In doing so, we report discovered correlations among reported vulnerability scores to infer consistent magnitude values of vulnerability instances. The method is applied to a case study featuring a CPS application to illustrate the automation of the proposed vulnerability scoring mechanism, used to mitigate cybersecurity weaknesses.

Smart grid employs Information and Communication Technology (ICT) infrastructure and network connectivity to optimize efficiency and deliver new functionalities. This evolution is associated with an increased risk for cybersecurity threats that may hamper smart grid operations. Power utility providers need tools for assessing risk of prevailing cyberthreats over ICT infrastructures. The need for frameworks to guide the development of these tools is essential to define and reveal vulnerability analysis indicators. We propose a data-driven approach for designing testbeds to allow the simulation of cyberattacks in order to evaluate the vulnerability and the impact of cyber threat attacks. The proposed framework uses data reported from multiple smart grid components at different smart grid architecture layers, including physical, control, and cyber layers. The multi-agent based framework proposed in this paper would analyze the conglomeration of these data reports to assert malicious attacks.

Recent advances in data analytics prompt dynamic datadriven vulnerability assessments whereby data contained from vulnerabilityalert repositories as well as from Cyber-physical System (CPS) layer networks and standardised enumerations. Yet, current vulnerability assessment processes are mostly conducted manually. However, the huge volume of scanned data requires substantial information processing and analytical reasoning, which could not be satisfied considering the imprecision of manual vulnerability analysis. In this paper, we propose to employ a cross-linked and correlated database to collect, extract, filter and visualise vulnerability data across multiple existing repositories, whereby CPS vulnerability information is inferred. Based on our locally-updated database, we provide an in-depth case study on gathered CPS vulnerability data, to explore the trends of CPS vulnerability. In doing so, we aim to support a higher level of automation in vulnerability awareness and back risk-analysis exercises in critical infrastructures (CIs) protection.

Smart grid adopts ICT to enhance power-delivery management. However, these advanced technologies also introduce an increasing amount of cyber threats. Cyber threats occur because of vulnerabilities throughout smart-grid layers. Each layer is distinguished by typical data flows. For example, power-data stream flows along the physical layer; command data are pushed to and pulled from sensor-control devices, such as RTUs and PLCs. Vulnerabilities expose these data flows to cyber threat via communication networks, such as local control network, vendor network, corporate network and the wider internet. Thus, these data could be used to analyse vulnerabilities against cyber threats. After data collection, data analysis and modelling techniques would be used for vulnerability assessment.

Vulnerability is defined as ”weakness of an asset or control that can be exploited by a threat” according to ISO/IEC 27000:2009, and it is a vital cyber-security issue to protect cyber-physical systems (CPSs) employed in a range of critical infrastructures (CIs). However, how to quantify both individual and system vulnerability are still not clear. In our proposed poster, we suggest a new procedure to evaluate CPS vulnerability. We reveal a vulnerability-tree model to support the evaluation of CPS-wide vulnerability index, driven by a hierarchy of vulnerability-scenarios resulting synchronously or propagated by tandem vulnerabilities throughout CPS architecture, and that could be exploited by threat agents. Multiple vulnerabilities are linked by boolean operations at each level of the tree. Lower-level vulnerabilities in the tree structure can be exploited by threat agents in order to reach parent vulnerabilities with increasing CPS criticality impacts. At the asset-level, we suggest a novel fuzzy-logic based valuation of vulnerability along standard metrics. Both the procedure and fuzzy-based approach are discussed and illustrated through SCADA-based smart power-grid system as a case study in the poster, with our goal to streamline the process of vulnerability computation at both asset and CPS levels.

Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.

This document reports a technical description of ELVIRA project results obtained as part of Work-package 2.1 entitled “Complex Dependencies Analysis”. In this technical report, we review attempts in recent researches where connections are regarded as influencing factors to  IT systems monitoring critical infrastructure, based on which potential dependencies and resulting disturbances are identified and categorized. Each kind of dependence has been discussed based on our own entity based model. Among those dependencies, logical and functional connections have been analysed with more details on modelling and simulation techniques.

Power grids form the central critical infrastructure in all developed economies. Disruptions of power supply can cause major effects on the economy and the livelihood of citizens. At the same time, power grids are being targeted by sophisticated cyber attacks. To counter these threats, we propose a domain-specific language and a repository to represent power grids and related IT components that control the power grid. We apply our tool to a standard example used in the literature to assess its expressiveness.

Traffic induced by parking-spot seekers is a growing challenge and constitutes a considerable portion of the traffic in city centers. New opportunities to solve this problem are emerging by connected vehicles and infrastructure. For instance, ultrasonic and magnetic sensors are already mounted on the ceiling of many parking lots to detect the availability of a parking spot. These sensors can provide parking spot availability information in real-time. Further, traffic-aware smart sensors which can detect the movement of individual vehicles are also available in many city and highway areas. This report suggests an algorithm for a cloud-based parking service that exploits these streams of data to choose the best parking lot in a given parking area.

The parking seeking problem is subject to a range of criteria that may include user, municipality and parking operator preferences. Users may have some preferences with respect to walking distance to destination. Municipalities prefer to spread the traffic to reduce congestion in the urban core. Parking operators seek to maximize parking lot utilization in order to increase the revenue on real-estate investments. To solve this problem, an optimization algorithm based on multicriteria decision making process is used.

The proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot. The algorithm features three modular sections:

• First, a search process is triggered to identify the expected arrival time periods to all parking lots in the targeted parking area. This process utilizes smart pole data streams reporting congestion rates across the targeted parking area.

• Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state transition matrix, showing the transformation of available spots in a parking lot over short periods of time.

• Finally, this matrix is projected against similar future seasonal periods to predict the actual vacancy of a parking lot at the arrival time.

Despite the growing interest in the Internet-of-Things, many organizations remain reluctant to integrating things into their business processes. Different reasons justify this reluctance including things’ limited capabilities to act upon the cyber-physical surrounding in which they operate. To address this specific limitation, this paper examines thing empowerment with cognitive capabilities that would make them for instance, selective of the next business processes in which they would participate. The selection is based on things’ restrictions like limitedness and goals to achieve like improved reputation. For demonstration purposes, water leaks are used as a case study. A BPEL-based business process driving the fixing of water leaks is implemented involving different cognitive things like moisture sensor.

This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today’s things are confined into a data-supplier role, which deprives them from being the technology of choice for smart applications development. Cognitive computing is about reasoning, learning, explaining, acting, etc. In this paper, cognitive things’ features include functional and non-functional restrictions along with a 3 stage operation cycle that takes into account these restrictions during reasoning, adaptation, and learning. Some implementation details about cognitive things are included in this paper based on a water pipe case-study.

Existing approaches on Web services privacy dominate solutions from a users' perspective, giving little consideration to the preferences of Web service providers. The integration of service providers' preferences into Web services' operations is discussed in this paper. A Web service provider indicates peer Web services that it could interact with as well as the data that they could exchange with. We focus on Privacy and (trust) Partnership preferences based on which, we develop a Specification for Privacy and Partnership Preferences (S3P). This specification suggests a list of exceptional actions to deploy at run-time when these preferences are not met. An integration model of these preferences into Web services design is illustrated throughout a running scenario, and an implementation framework proves the S3P concept. Towards an Approach for Weaving Preferences into Web Services Operation (PDF Download Available). Available from: https://www.researchgate.net/publication/235724905_Towards_an_Approach_for_Weaving_Preferences_into_Web_Services_Operation [accessed Mar 22, 2016].

Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the In- ternet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. A case study is discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework. 

Peak-time traffic woes create considerable amount of stress and environmental pollution resulting in an economic loss. Research innovations in areas such as the Web of Things are able to curtail some of these issues by creating scalable and sustainable environments like parking lots, which provide motorists with access to convenient parking spots. We present a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things’ services within a parking lot. Our system uses service-oriented architecture, allowing motorists to reserve parking spots in advance. In doing so, our proposed system leverages the use of HTTP and Wi-Fi for the Web enablement and interoperability of things within a parking spot and elevates it as a Smart Parking Spot on the Web. Our suggested semantic Web-based structure for representing things makes it possible to query physical things’ states and services depending on their capabilities and other relevant parking-related parameters. Our performance evaluation reveals that a maximum of 40 % time is saved to find parking spots and also 40 % reduction in air pollution is observed.

The Internet of Things (IoT) is an active research area, focusing on connecting real-world things over TCP/IP. This trend has recently triggered the research community to adopt the interoperability of the Web (HTTP) as an application platform for integrating ‘things’ on the Internet. Harnessing physical things into the virtual world using Web standards is also enriching the arena of conventional Web services to unleash data and functions of real-world things as service providers and consumers on the Internet. This evolution of the Web as a highly interoperable application platform for connecting real-world things has raised many research challenges and problems, leading to the fast growing research area called the Web of Things (WoT). Current research on WoT is a catalyst for the realization of IoT, opening up the possibilities of creating ambient spaces (AS), where people and things seamlessly communicate over the Web. In this chapter we discuss the state of the art in WoT research, focusing on the various challenges, and enabling technologies that are driving this research. We discuss architectural frameworks, models and technologies to build applications for future ambient spaces with the WoT. We present case studies that reflect the feasibility and applicability of the WoT technology. We also discuss future trends and research directions within this domain to throw light on existing problems and challenges.

Managing multilingual documents is a time consuming, error prone and expensive task, particularly when dealing with dynamic documents such as web contents. A broad spectrum of organizations such as corporations, NGO’s and Governments are committed to offer such documents in a number of languages where the content is further localized to suit specific cultural settings. In this paper, we propose a business model supported by a web services-enabled framework, which facilitate all aspects related to multilingual web contents management, from negotiating translation-request quotations through production of final localized output as well as its verification, and delivery. This service is based on a collaborative internet-based translation framework, referred to in this paper as Translation Management System (TMS). Our approach uses XLIFF, a Web service standard developed by OASIS, in order to interoperate enterprise translation services and related Web applications. We present and implement a translation business model centered around standardized processes, which we validate through a case study in the context of a Web translation project. We also propose a QoS monitoring model to satisfy the quality-related requirements of a translation job. Finally, we evaluate the usability of our streamlined Web translation services through users’ perception in terms of flexibility, ease of use, and quality of translation. The results revealed interesting performance tradeoffs relative to translation workflows and content-translation accuracy as well as flexibility, and diversity of TMS provided services.

The abundance of services offered by cloud providers raises the need for Quality of Service (QoS) monitoring to enforce Service Level Agreements (SLAs). In this paper, we propose a cloud-monitoring scheme, which offers flexible and dynamically reconfigurable QoS monitoring services to adapt to various cloud based service characteristics. We propose to grant cloud service consumers the option to decide whether to switch to another service provider, if the current provider frequently violates the corresponding SLA contract. For cloud service providers, the monitoring scheme provides dashboard-like indicators to continuously manage the performance of their SaaS platform and visualise the monitoring data. Our experimental evaluation involves a real cloud platform to illustrate the capability of our monitoring scheme in detecting and reporting violations of SLAs for both single and composite SaaS services. A particular emphasis on visualisation options is highlighted when revealing and reconfiguring monitoring data in a user-friendly display.