Alpen-Adria-Universität Klagenfurt

Digital twins use actual sensor data to replicate the current state of a plant in a virtual model. They can be used to evaluate the current state, predict future behavior, and thus allow to refine control or optimize operation, enable predictive maintenance as well as detection of anomalies and failures. The model of a digital twin includes models of the components, behaviors and dynamics of a system. With the ability to simulate real scenarios, such models can therefore also be used before a plant is actually implemented, e.g., to predict the actual performance, identify potential issues for the implementation and to develop optimal operation strategy and algorithms. Furthermore, interfaces may be defined, implemented, and tested with such models allowing fast and easy commissioning of the physical implementation. Accurate digital twins therefore also need to include realistic sensor models, considering adverse effects that impact their output signals. The proposed work presents approaches for accurate sensor simulations allowing researchers and industries to assess sensor performance, optimize algorithms, and evaluate system-level integration. We address Frequency Modulated Continuous Wave (FMCW) radar sensors and time-of-flight cameras as examples for far-field sensors and capacitive sensors as an example for near-field sensors. The approaches can be transferred to other sensors, e.g., ultrasound sensors, LiDAR sensors and inductive or magnetic sensors so that a wide range of industrial sensors can be covered. The proposed simulations are benchmarked with different tests, including real-world experiments and compared with the corresponding real sensors.

Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. Unfortunately, the performance of temporal planning engines tends to sharply deteriorate when the number of agents and objects in a domain gets large. A possible remedy is to use macro-actions that are well-studied in the context of classical planning. In temporal planning settings, however, introducing macro-actions is significantly more challenging when the concurrent execution of actions and shared use of resources, provided the compliance to temporal constraints, should not be suppressed entirely. Our work contributes a general concept of sequential temporal macro-actions that guarantees the applicability of obtained plans, i.e., the sequence of original actions encapsulated by a macro-action is always executable. We apply our approach to several temporal planners and domains, stemming from the International Planning Competition and RoboCup Logistics League. Our experiments yield improvements in terms of obtained satisficing plans as well as plan quality for the majority of tested planners and domains.

Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.

Generalized semifield spreads are partitions \(\Gamma =\{U,A_1,\ldots , A_{p^k}\}\) of \(\mathbb {F}_{p^m}\times \mathbb {F}_{p^m}\) obtained from (pre)semifields with a certain additional property, which generalize semifield spreads. In particular, a generalized semifield spread is a bent partition, i.e., every function \(f:\mathbb {F}_{p^m}\times \mathbb {F}_{p^m}\rightarrow \mathbb {F}_p\), which is constant on every set of \(\Gamma \), such that every \(c\in \mathbb {F}_p\) has the same number \(p^{k-1}\) of \(A_i\) in the preimage set, is a bent function. We show that from generalized semifield spreads one obtains not only p-ary and vectorial bent functions, but also bent functions \(f:\mathbb {F}_{p^m}\times \mathbb {F}_{p^m}\rightarrow B\) for any abelian group of order \(p^s\), \(s\le k\). We investigate the effect of (pre)semifield isotopisms on generalized semifield spreads. We observe that isotopisms can destroy the bent partition property, and derive conditions on an isotopism between two (pre)semifields such that the corresponding partitions are equivalent bent partitions. Most notably, we show that with some other class of isotopisms, one can obtain inequivalent bent partitions, hence different classes of bent functions. This is in contrast to the situation for classical semifield spreads. The spreads of two isotopic (pre)semifields are always equivalent. Employing the 2-rank of Boolean functions we confirm that generalizations of the Desarguesian spread bent functions, which we call generalized PS\(_{ap}\) functions, are in general not in the Maiorana-McFarland class. The generalized PS\(_{ap}\) class contains functions which are not Maiorana-McFarland nor partial spread bent functions for any partial spread. Explicitly we determine the 2-rank of some Maiorana-McFarland functions in the generalized PS\(_{ap}\) class in terms of Fibonacci numbers.

After the outbreak of the COVID-19 pandemic, schools had to continuously adapt to new pandemic-related regulations and challenges, including the ad hoc transition to remote learning. According to theories on school improvement and professionalisation, sharing knowledge and experiences with digital learning is helpful when dealing with related issues. However, no existing empirical studies analyse longitudinally how pre-pandemic experiences with sharing knowledge and digital learning impacted perceived professionalisation during the pandemic and how this relationship is mediated both by schools' strategies to improve learning and by schools' collective efficacy. For this study N = 280 school principals from Germany, Austria, and German-speaking Swiss cantons participated in two online questionnaires in 2020 and 2021. Results from the structural equation model reveal that schools' pre-pandemic experiences with knowledge sharing and digital learning are positively indirectly related to the schools' perceived professionalisation in the first and second year of the pandemic. The relationship is mediated by the schools' collective efficacy in dealing with the pandemic and the schools' use of strategies to improve teaching. The results highlight the importance of building school improvement capacity and supporting schools in digital learning to navigate through unexpected emergencies like a pandemic. ARTICLE HISTORY

The chapters presented in this book originate from several years of intensive research work conducted by mostly experienced teams of international authors from various affiliations worldwide. The main criteria, which were relied upon in order to constitute the different parts of this book, were based on the thematic convergence of the topics proposed.

The huge attention currently afforded to renewable energy-based decentralised energy systems, as means for accelerating rural electrification and hence development, has triggered massive consideration and interest given the cost involved in extending existing grids to rural communities of Sub-Saharan Africa (SSA). In most of these communities, access to electricity is essentially restricted to basic domestic utilisation or needs such as: lighting, cooking and storage purposes. Although, small-scale farming consists of the main occupation in rural communities of SSA, it remains less developed and does not take advantage of available renewable energy resources succeptible to promote its expansion and development. However, the potential in renewable energy resources (solar PV, wind, etc.) in SSA countries could be counted as important attribute for the enablement of access to electricity. As a direct consequence of this, sustainable agricultural development may be achieved in order to ensure food security and to prevent urban migration by promoting employment opportunities and poverty alleviation in rural communities of SSA countries as dictated by the United Nations (UN) sustainable development goals (SDGs). Despite the promotion of access to clean electricity being advocated in the literature as the stepping-stone for sustainable development and growth, the gap remains the choice of suitable policy option susceptible to balance access to electricity with sustainable development in rural communities of SSA. In this work, the six-step policy analysis is applied to probe the effectiveness of market-based policies in enhancing access to electricity and agricultural development in rural communities of selected SSA countries. Results show that despite the shortcomings in the implementation of this policy in many SSA countries, this policy approach proves to be favorable to increased share of renewable energies, which translates into increased electrification of the agriculture sector.

Sustainable development is a call that concerns both developing and developed countries and includes ensuring universal access to affordable, reliable, and modern energy services by 2030 (Goal 7). In the energy domain, there cannot be Smart City or Smart Village without a Smart Energy Management System (SEMS) managing a power grid aimed at providing electrical energy. Nowadays, access to electrical energy through optimal and cost-effective management in generation, transmission, distribution, and consumption, is a requirement that demonstrates the necessity and urgency of transforming traditional (or conventional) power grids into Smart Grids (SGs) in various parts of the world. This is pertinent for Africa where electricity supply is often intermittent even in countries with high hydropower potential. It is expected that if Africa is to develop, it should not fail to invest in an efficient electrical system in the next 20 to 30 years.

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence‐initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent‐initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400‐like negativity when the disambiguation was toward patient‐initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents.

Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.

CLIP-based text-to-image retrieval has proven to be very effective at the interactive video retrieval competition Video Browser Showdown 2022, where all three top-scoring teams had implemented a variant of a CLIP model in their system. Since the performance of these three systems was quite close, this post-evaluation was designed to get better insights on the differences of the systems and compare the CLIP-based text-query retrieval engines by introducing slight modifications to the original competition settings. An extended analysis of the overall results and the retrieval performance of all systems' functionalities shows that a strong text retrieval model certainly helps, but has to be coupled with extensive browsing capabilities and other query-modalities to consistently solve known-item-search tasks in a large scale video database.

We consider the problem of sampling elements with some desired property from a large set, without testing the property of interest, but with the (probabilistic) assurance to have at least one match among the random sample. Like in ranked set sampling, we consider an infinite population under study, whose properties of interest are too expensive and/or time-consuming to measure. Unlike RSS, we are void of a ranking mechanism, so our sampling is done entirely blind. We show how it is nonetheless doable to assure, with controllably large likelihood, to either have at least one of the interesting elements in a random sample, or, contrarily, sample with the likewise assurance of not having one of the interesting elements in the sample. Our technique utilizes density bounds for distributions and threshold functions from random graph theory.

We investigated whether different mortality rate formats used to express the same objective probability affected people's emotional reactions, risk perception, and protective behavioral intentions. A sample from the Italian population (N=604) was exposed to six different formats (i.e., Absolute value; Raw ratio; 1 in X; Verbal; Percentage; Probability) to report the mortality rate of COVID-19 in a between-subject design. In line with expectations, the Probability format led to lower emotional reactions compared to all the other formats. Moreover, results from a path analysis revealed that emotional reactions predicted risk perception. The mortality rate formats also had an indirect effect on behavioral intentions to protect oneself, which was mediated by emotional reactions and risk perception. The effect sizes of these indirect effects ranged from small to medium. The direct effect of risk on intentions was found to differ among the two dimensions of risk. Affective Risk led to higher behavioral intentions, while Deliberative Risk had the opposite effect. We discuss these results in line with the ongoing debate regarding the role played by risk scientists during the pandemic and offer practical implications for risk management during health crises like COVID-19.

Adolescents and young adults represent the largest and most active age group of Internet users worldwide. However, contrary to the notion of naturally acquired digital competences, many lack an understanding of technology and the associated risks. Serious games are a promising way to promote self-regulatory digital competences. In the A-DigiKomp project, a three-day online game jam with adolescents was organized to involve the target group in the user-centered development process of game ideas for more digital empowerment. Three prototypes on the topic of data protection and online safety were developed as part of the CGN game jam. Personal experiences and target group-specific knowledge of adolescents were successfully addressed. The potential of game jams as a participatory research method is investigated and implications for future use cases are derived.

Comprehenders across languages tend to interpret role-ambiguous arguments as the subject or the agent of a sentence during parsing. However, the evidence for such a subject/agent preference rests on the comprehension of transitive, active-voice sentences where agents/ subjects canonically precede patients/objects. The evidence is thus potentially confounded by the canonical order of arguments. Transitive sentence stimuli additionally con ate the semantic agent role and the syntactic subject function. We resolve these two confounds in an experiment on the comprehension of intransitive sentences in Basque. When exposed to sentence-initial role-ambiguous arguments, comprehenders preferentially interpreted these as agents and had to revise their interpretation when the verb disambiguated to patient-initial readings. The revision was re ected in an N400 component in ERPs and a decrease in power in the alpha and lower beta bands. This finding suggests that sentence processing is guided by a top-down heuristic to interpret ambiguous arguments as agents, independently of word order and independently of transitivity. ARTICLE HISTORY