Voluntary commitment of citizens is an indispensable, supporting pillar of critical infrastructures (CRITIS), such as disaster management, rescue services, healthcare and social services or food supply. However, the long-term sustainability of the voluntary sector, is massively endangered due to the profound changes in demographics, social structures and volunteer motives driven by the individualization and pluralization of society. This paper addresses these pressing challenges by proposing a digital platform designed for goal-driven and competence-based networking and bundling of volunteer engagement across non-profit organizations (NPOs). This platform is intended to be a first step towards synergistically aligning the partly collaborative goals and competencies of volunteers with activity requirements essential for strengthening CRITIS.

The constant flood of alarms triggered by operational technology (OT) used in critical infrastructures (CRITIS) such as energy or traffic systems poses a serious challenge to their safe and efficient operation. Operators must quickly recognize truly critical situations and make life-saving decisions. This task is made more difficult by typical characteristics of CRITIS, including the high heterogeneity of OT systems, their wide geographical distribution, decentralized nature, and continuous, partly unpredictable evolution. As a result, operators often have only an isolated view of individual OT components, without understanding their interdependencies, which prevents effective alarm reduction. This paper tackles the problem of alarm flood reduction based on interdependencies in CRITIS, as studied in our research project iReduce. We present a research roadmap that outlines a two-step approach to identify and represent OT interdependencies. This is supported by a domain-adaptable OT knowledge base for semantic representation and by provenance mechanisms to handle system evolution. We also report on the current implementation status and first evaluation results, focusing on the exploration of OT interdependencies as a key step towards intelligent alarm reduction.

Voluntary engagement is an indispensable cornerstone of Critical Infrastructures (CI) such as civil protection, disaster, crisis and rescue management as well as health and social services. Sustainability of the voluntary sector, however, is massively endangered by profound changes in demography, social structure, and volunteer motives in the sense of individualization and pluralization of society. To address these challenges, this paper contributes a research roadmap for leveraging recent advances in Artificial Intelligence (AI) specifically for the volunteer sector, discussing the proposed research design and methods together with research objectives and promising technological solutions. A brief discussion of a first prototypical realization of LLM-based skill extraction from volunteering opportunities and an outlook on future research complements this contribution.

The immense flood of alerts continuously generated in large-scale control systems of critical infrastructures, such as energy or traffic networks, poses a substantial challenge to their efficient and safe operation. Despite ongoing research efforts, mining alert patterns as a means to cope with alert floods remains particularly difficult, since relationships between alerts are often unknown due to the complexity of systems and limitations in log data. In pursuit of a robust solution to this problem, this paper introduces our alert-driven pattern mining approach, which is based on a hybrid, multi-objective evolutionary algorithm. The approach is unique in that it simultaneously maximizes pattern coverage, enabling reasoning about the full range of underlying relationships, and leverages pattern frequency to identify both rare and frequent patterns. This dual focus supports alert flood reduction in regular as well as exceptional, potentially critical situations. The applicability and effectiveness of the proposed approach are demonstrated using real-world log data from the domain of road traffic management, with a particular focus on the characteristics of the discovered patterns.

Critical infrastructures (CIs) such as energy grids, communication networks, and transportation systems constitute the foundational backbone of modern society’s social and economic functions. Ensuring their efficient and safe operation by providing appropriate techniques to monitor the underlying Operational Technology (aka. OT), commonly referred to as Operational Technology Management (OTM), is therefore naturally of paramount importance. One crucial key challenge, however, lies in the continuous evolving nature of CI-related OT at a rapid pace, introducing massive heterogeneities, interdependencies and interoperability problems. Consequently, these complex challenges, make achieving effective OTM and integrative service quality hard to achieve. The objective of our research project, »DevCon« is therefore to push forward appropriate techniques that facilitate OTM and integrative service quality even amidst of the continuous evolving nature inherent in this kind of systems. In particular, to overcome heterogeneities while explicating interdependencies and ensuring interoperability, we propose conceptualizations of OT objects and service quality, providing the foundations for a pivotal knowledge base. For this, an innovative approach is followed that synergistically combines inductive methods in terms of machine learning and deductive methods in terms of semantic technologies, thereby leveraging their complementary strengths.

Voluntary engagement is an indispensable cornerstone of Critical Infrastructures (CI) such as civil protection, disaster, crisis and rescue management as well as health and social services. Sustainability of the voluntary sector, however, is massively endangered by profound changes in demography, social structure, and volunteer motives in the sense of individualization and pluralization of society. This paper tackles these key challenges by giving an overview on our digital platform for goal-oriented volunteering across and independently of different non-profit organizations. This platform is intended to be a first step towards synergistically aligning the goals and competencies of volunteers with the activity requirements of CI by proposing first ideals on using recommender and dataspace technologies.

Volunteering is a vital pillar of critical infrastructures (CIs) and sustainable development goals (SDGs), fostering, e.g, civil protection or rescue/health/social services. Whether supporting CIs or SDGs, skill-based volunteering is key. Standardized knowledge about skills viable or necessary for certain volunteering opportunities is beneficial in the pre-engagement phase to enable effective skill use as well as in the post-engagement phase to leverage skill gain. It is unclear, however, in how far existing skill classification approaches - currently solely focusing on job postings on the labor market - can handle the nuanced, taskdriven, and prose-like descriptions of predominantly soft skills typical in volunteering opportunities. This paper addresses this gap by presenting a comparison of existing skill classification approaches, initially developed for labor market job postings, in the context of volunteering opportunities. Based on that, we propose using cache- and retrieval-augmented generation techniques for soft skill classification in volunteering, avoiding the high costs of LLM fine-tuning common in current methods. The effectiveness of these lightweight techniques is evaluated both quantitatively and qualitatively using a novel soft skill dataset with expert-labeled volunteering opportunities from a global volunteering platform.

The immense flood of alerts that is constantly produced in large-scale control systems (LSCS) and particularly in road traffic management (RTM), represents a substantial challenge for efficient and safe operation. Although research for reducing alert floods exists since decades, mining of appropriate alert patterns as the ultimate means to cope with alert quantity is especially challenging since relationships between alerts are commonly unknown, due to heterogeneity, size, and evolutionary nature. In search of the holy grail for dealing with alert floods, i.e., exploiting relationships between alerts, this paper contributes an alert-driven pattern mining approach, based on a hybrid, multi-objective evolutionary algorithm. This approach is unique in that first, pattern coverage is maximized, ensuring that each alert occurrence is pinned down within a pattern, thus allowing to reason about all underlying relationships for the whole alert log data. Second, pattern frequency is leveraged, ensuring that both frequent as well as rare patterns are found, thus allowing for alert flood reduction in regular as well as exceptional and possibly critical cases. Based on real-world log data in the area of RTM, the applicability of our approach is demonstrated, complemented by a comparative evaluation.

The UN 2030 Agenda recognizes volunteers as key actors in achieving the Sustainable Development Goals such as quality education, gender equality, environmental conservation and community well-being. At the same time, however, sustainability of the voluntary sector itself is massively endangered by profound changes in demography, social structure, and volunteer motives. This is not least since craving for meaningful volunteering opportunities simultaneously allowing to achieve personal goals is increasingly in the foreground. Thus, adhering to the principle »do good for others and for yourself« we strive to synergistically align volunteers' personal goals with volunteering opportunities proposing a recommender system based on customized cross-encoder models. In this respect, our contribution is threefold: First, we reveal in how far LLMs are able to provide labeled ground truth data for personal goals and volunteering opportunities by comparing existing approaches and propose their adoption to the peculiarities of our domain. Second, we put forward a learning approach for fine-tuned models using transfer learning based on cross-encoder models. Finally, we resolve the feasibility of the different labeling approaches and the resulting models based on appropriate metrics and statistical tests, reflected upon through complementing lessons learned.

Voluntary engagement is an indispensable cornerstone of Critical Infrastructures (CI) such as civil protection, disaster, crisis and rescue management, health and social services, or harvests and food supply. Sustainability of the voluntary sector, however, is massively endangered by profound changes in demography, social structure, and volunteer motives in the sense of individualization and pluralization of society. This article tackles these key challenges by proposing a digital platform for goal-oriented volunteering across and independently of different non-profit organizations. This platform is intended to be a first step in synergistically aligning the goals and competencies of volunteers with the activity requirements to strengthen CI.

Goal setting acts as a mechanism for individuals to attain specific objectives, leading to an increase in task performance, engagement and, ultimately well-being. Currently, there is a variety of so-called goalification apps in different domains, from health to sustainability and productivity, supporting beyond simple tracking different phases of the goalification lifecycle, from goal planning to acting on appropriate tasks and analyzing the outcome. There is already a plethora of research in areas like personal informatics, persuasive systems, and goal setting theory, including several design space conceptualisations and system evaluations. This paper sheds light on these existing research efforts, by contributing a state of the art survey, for the first time from a goalification lifecycle point of view. In particular, both, existing design space conceptualizations and system evaluations are systematically compared to each other. On this basis, lessons learned are put forward, serving as a first step towards an overarching goalification evaluation framework which would allow a proper evaluation of existing goalification apps across several domains.

Critical infrastructures in domains like road traffic management heavily depend on Operational Technology (OT) to ensure safe operation. One faces, however, also tremendous challenges in OT monitoring (OTM), i.e., ensuring the proper functioning of the OT objects themselves, due to their inherent large-scale, heterogeneous, and evolutionary nature. Going beyond the current practice of monitoring single OT object states, the digital twin paradigm could enable a more holistic OTM - research being, however, still in its infancy. Thus, the contribution of this paper is threefold: Firstly, we discuss key challenges from a domain perspective and derive appropriate criteria for a systematic evaluation of data-driven approaches aiming at a digital representation of an OT infrastructure. Secondly, based on these criteria, we identify and discuss promising approaches, ranging from IT networks to Social Networks. Thirdly, based thereupon, we present lessons learned and open issues for further research.

The immense flood of alerts that is constantly produced in large-scale control systems (LSCS) of critical infrastructures, ranging from energy and ICT to traffic, represents a substantial challenge for efficient and safe operation. Although research for reducing alert floods exists since decades, mining of appropriate alert patterns as the ultimate means to cope with alert quantity is especially challenged since relationships between alerts are commonly unknown, due to heterogeneity, size, and evolutionary nature of LSCS. Thus, this paper contributes an alert-driven pattern mining approach based on a hybrid, multi-objective evolutionary algorithm being unique in two directions. First, pattern quality is optimized by maximizing both, pattern size in terms of how many alerts are covered by a single pattern occurrence and pattern confidence taking into consideration how many alert occurrences are covered by repeated pattern occurrences, thus allowing for a two-dimensional flood reduction. Secondly, pattern coverage is maximized, ensuring that each alert occurrence is pinned down within a pattern and at the same time allowing for various patterns to be identified for a single alert, thus facilitating a multi-faceted flood reduction. Based on real-world log data in the area of road traffic management, the applicability of our approach is demonstrated.

Critical infrastructures in areas like road traffic management naturally rely on the broad use of "Operational Technology (OT)" to ensure efficient and safe road traffic monitoring (RTM) through "OT objects" like sensors and actuators whereby monitoring OT itself ("OTM") is evenly crucial. OTM is highly challenging, not least due to massive heterogeneity of OT, immense complexity and size and omnipresence of evolution. As a consequence, knowledge about interdependencies between OT objects in form of semantic relationships is often outdated or simply not available. Thus, in case of incidents, detection of cause and effect in the sense of a situational picture is missing. In order to counteract this fundamental deficiency, we aim to automatically recognize semantic relationships between OT objects to build up an ontological knowledge base as prerequisite for achieving OT situation awareness. The contribution of this paper is to sketch out state-of-research w.r.t. real-world challenges we are facing and based on that to put forward appropriate research questions, leading to the identification and in-depth discussion of potential concepts and technologies appearing to be useful for our work. Overall, this contribution forms the conceptual framework for a proof-of-concept prototype already realized on basis of real-world OT in the area of road traffic management.

The demand for personnel being able to develop Web apps has grown tremendously. Not least to cope with this need, a plethora of “Low-Code Platforms” (LCPs) emerged, empowering “citizen developers” to build up Web apps without programming skills while enhancing productivity by removing repetitive and boring programming tasks. The comprehensive functionality of fullfledged LCPs allowing to specify every nitty gritty detail of a Web app, however, hampers their adoption. This has sparked research projects like our EU Erasmus+-project BeeAPEX, cutting a path through the feature jungle of LCPs and lowering the entry barrier for citizen developers. Based on these findings and by focusing on Oracle APEX as representative example, this paper puts forward (i) a systematic overview of low-code features to develop the main building blocks of Web apps, (ii) shade light on nature and determining characteristics of their development process and (iii) emphasize on reuse potential exploited by LCPs

Large-scale Systems-of-Systems prevalent in the area of critical infrastructures such as Intelligent Transportation Systems are characterized by massive heterogeneities. Hence, cross-system oriented monitoring of the underlying operational technology (OT) is challenging, particular due to the lack of information about functional dependencies within and between systems being an indispensable prerequisite for efficient Operational Technology Monitoring. Since existing approaches to mine functional dependencies from log files, being often the only available source, rarely address the (i) different semantics hidden in the logs, as well as, (ii) challenges prevalent in large-scale SoS, sufficiently, we put forward a novel semantic-driven mining method being able to cope with those challenges aiming to identify functional dependencies between OT objects based on the co-occurrence of events from log files. The applicability of this approach with respect to accuracy, efficiency, and effectivity has been demonstrated on the basis of a systematic evaluation based on artificially generated real-world data.

Multidimensional torus interconnect finds wide application in modern exascale computing. For models design in high-performance computing, grid and cloud computing, and also systems biology, two basic ways of specifying spatial structures with Petri nets are considered – an infinite Petri net specified by a parametric expression (PE) and a reenterable coloured Petri net (CPN). The paper studies a composition of hypertorus grid models in the form of a PE and a reenterable CPN, their mutual transformations, and unfolding into a place/transition net; the parameters are the number of dimensions and the size of grid. A grid is composed via connection of neighbouring cells by dedicated transitions modelling channels. Reenterable model peculiarities are explained on step-by-step simulation examples. The rules of mutual transformations of Petri net spatial specifications are specified. Comparative investigation of two mentioned forms of spatial specifications is implemented, including analysis techniques and tools. CPNs are convenient for the state space analysis. The main advantage of PEs is the ability to obtain linear invariants and other structural constructs of Petri nets, for instance, siphons and traps, in parametric form that allows us to draw conclusions on Petri net properties for any values of parameters.

Large-scale Internet-of-Things (IoT) environments such as Intelligent Transportation Systems (ITS) are characterized by massive heterogeneities. Hence, cross-system monitoring of the underlying IoT-based Operational Technology (OT) is challenging, particularly due to the lack of information about functional dependencies between OT objects. We put forward a novel semantic-driven mining method aiming to identify functional dependencies out of IoT log files. In the current paper, we put this method into practice by (i) applying our approach to the domain of ITS, discussing it's applicability step-by-step on the basis of a real-world example, (ii) putting forward a prototypical implementation giving details about the technologies used, and (iii) providing a systematic evaluation of our method from different perspectives in terms of accuracy, efficiency and effectivity grounded on simulated real-world data.

Proof-of-work agreement protocol, offered by Keller and Bohme, is analysed by coloured Petri nets and refined. Blockchain technology, based on proof-of-work procedure and Nakomoto consensus negotiations, represents fundamentals of many kinds of cryptocurrency widespread recently. The protocol, called A(k), works in continuous time which is simulated using random exponential distribution function of CPN Tools system, obtained values rounded to map them into discrete time of a coloured Petri net. Hierarchical model consists of an environment subnet and a given number of nodes communicating via an unstructured network represented by a single place; the model of node is further structured based on event handlers of the protocol source specification such as initialisation, activation, message delivering, and termination condition check. Based on the simulation results, modifications of the protocol and its parameters are recommended which improve some imperfections of the protocol. [GRAPHICS]

Volunteering is an important cornerstone of our society, from social welfare to disaster relief, supported by a variety of volunteer management systems (VMS). These VMS focus primarily on centralized task management within non-profit organizations (NPOs) but generally do not provide mechanisms that allow volunteers to privately digitize and exploit their engagement assets in terms of digital badges for activities accomplished or competences acquired, in a trustful manner. This lack of sovereignty hampers volunteers in the exploitation of their engagement assets wrt. self-exploration, but also with regard to possible transfers of assets to other NPOs and beyond, e.g., to the education or labor market. We put volunteers in the middle of concern by investigating “how engagement can be digitized and exploited in a lifelong way”, thus adhering to the idea of human-centric personal data management. First we propose a conceptual architecture for a web-based volunteer platform based on a systematic identification of the requirements for trustworthy digitization and exploitation of engagement assets. Second, to address the massive heterogeneity that prevails in different areas of volunteering, a generic and extensible engagement asset model is proposed. Third a reification-based configuration mechanism is proposed so that each NPO can adapt the proposed model to its specific needs. Finally, a prototypical web application is presented which allows to »blockchainify« lifelong volunteer engagement in order to establish trust between all stakeholders.