Alpen-Adria-Universität Klagenfurt

It is shown that the generalized Rothaus construction of p -ary bent functions can be extended to a construction of a vectorial bent function with non-weakly regular components, for which in general the duals are not a bent function, i.e., they belong to the class of non-dual bent functions. This complements results on other two constructions of non-weakly regular bent functions, the generalized Maiorana-McFarland construction and the semi-direct sum, for which vectorial versions are presented and the properties of their duals are investigated in the literature. The distribution of the values of the Walsh transform of vectorial bent functions (with non-weakly regular components) is then analysed in detail. Among others, a condition on the values of the Walsh transform of a vectorial bent function from F ⁿ _p to F ^m _p is presented, which implies that m ≤ ⌈ n /2⌉. This refines a classical result by Nyberg, which states that for an ( n , m ) bent function, n even, with only regular components, m can be at most n /2. Some results on the weight distribution of codes obtained from vectorial bent functions with non-weakly regular components complement the article.

Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic information about the objects of interest from raw sensory data. While deep learning-based (DL) methods excel at inferring semantic object information from images, such as class and relative 6 degree of freedom (6-DoF) pose, they are computationally demanding and thus often not suitable for payload constrained mobile robots. In this letter we present a real-time capable unmanned aerial vehicle (UAV) system for object-relative, closed-loop navigation with a minimal sensor configuration consisting of an inertial measurement unit (IMU) and RGB camera. Utilizing a DL-based object pose estimator, solely trained on synthetic data and optimized for companion board deployment, the object-relative pose measurements are fused with the IMU data to perform object-relative localization. We conduct multiple real-world experiments to validate the performance of our system for the challenging use case of power pole inspection. An example closed-loop flight is presented in the supplementary video.

The Lebanese wireless device explosion incident has drawn widespread attention, involving devices such as pagers, walkie-talkies, and other common devices [1]. This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment, signaling the onset of a new wave of “supply chain warfare” [2]. Even worse, with the rapid proliferation of Internet of Things (IoT) devices and smart hardware, the fragility of global supply chains would become increasingly fatal and significant, since almost all devices of daily usage could be maliciously programmed and triggered as weapons of massive destruction. Given this, we need new thinking and new approaches for improving supply chain security [3]. With its decentralized, tamper-proof, and highly traceable characteristics, blockchain technology is considered an effective solution to address these security threats [4], [5]. How to secure the entire lifecycle of smart devices, from production and transportation to usage, through blockchain-enabled safety management and protection, has become a pressing issue that requires immediate attention.

Background For individuals living alone, having a diverse personal network is considered crucial for mitigating the risk of social isolation and enhancing well‐being. Although a reciprocal dynamic between network diversity and well‐being is likely, longitudinal evidence supporting reciprocal effects is limited. This study investigates dynamic transactions between network diversity and well‐being (life satisfaction, loneliness, and depressiveness) in a community‐based sample of middle‐aged adults from Germany. It also explores moderations by the duration of living alone. Method Data were drawn from the three‐wave RIKSCHA (Risks and Chances of Living Alone) project, which includes N = 389 middle‐aged adults living alone. Results Cross‐lagged panel models revealed high rank‐order stabilities and correlated changes in network diversity and well‐being. Random‐intercept cross‐lagged panel models and dynamic panel models indicated that unobserved traits accounted for these high stabilities. Correlated changes disappeared when accounting for the trait‐like stability of variables. Across all models, no evidence of reciprocal associations between network diversity and well‐being was found. All results remained consistent regardless of the duration of living alone. Conclusions The study discusses trait factors accounting for the high stabilities observed in network diversity and well‐being among middle‐aged adults living alone. Future research should further explore the traits impacting successful adaptation to living alone within the context of personal networks.

Editorial by the guest editors (Anna Argirò, Anna Brook and Katja Čičigoj) introducing the Visceral Bodies Special Issue of Studies in the Maternal.

Children with chronic illnesses often miss school, leading to negative outcomes like diminished health-related quality of life (HRQoL) and sense of belonging. Telepresence robots are suggested to keep these children connected to peers and education, yet little research has explored their impact. This study assessed effects of a telepresence system on HRQoL and sense of belonging in 29 patients with chronic illnesses aged 6 to 18 years, who were absent from school. Using a one-group pre-posttest design, participants completed questionnaires before and 6 months after receiving the robot. It was expected that HRQoL and sense of belonging would remain stable due to the robot. Wilcoxon tests indicated no decline in HRQoL (Z = −.958, 95% CI [–3.1, 8.3]) or sense of belonging (Z = −1.409, 95% CI [–0.3, 0.8]). Spearman correlations revealed a significant correlation between age and changes in school (rs = 0.621, 95% CI [0.200, 0.848]) and friends’ subscales (rs = 0.579, 95% CI [–0.136, 829]), suggesting adolescents benefit particularly from the robot. Consistent with prior research, this study shows no change in psychosocial factors, indicating a stabilizing effect of telepresence robots and contributing to sustainable psychosocial care for pediatric patients.

Research on psychological richness in China and in adolescents is limited. We validated the 17‐item Psychologically Rich Life Questionnaire in a sample of 1794 Chinese high school students. Internal consistency was adequate, and a two‐factor structure was found.

Zusammenfassung Viele Mathematiklehramtsstudierende berichten im ersten Studienjahr von Motivationsproblemen, die wahrscheinlich mit ungünstigen Studienprozessen einhergehen. Um diesen Zusammenhang aufzudecken, orientieren wir uns an bestehenden Differenzierungen von berufs- und fachbezogener Motivation im Rahmen der Expectancy-Value-Cost Theorie. Konkret untersuchen wir, ob berufs- und fachbezogene Wertüberzeugungen von Lehramtsstudierenden mathematikspezifisch operationalisiert werden können und welche Bedeutung im Studienprozess diese für Lehramtsstudiengänge haben. Aufbauend auf Vorarbeiten und bestehenden Studien zu verschiedenen Objekten der Motivation wurde dazu ein Fragebogen zu Wertüberzeugungen entwickelt. In einer Studie mit 270 Mathematikstudierenden des Sekundarstufen II-, I‑ und Primarstufen-Lehramtes konnte eine gute Passung zwischen dem theoretischen Modell der berufs- und fachbezogenen Wertüberzeugungen und den empirischen Daten festgestellt werden. Vor allem intrinsische Wertüberzeugungen in Bezug auf das Fach zeigen sich als relevant für Studienzufriedenheit, Abbruchneigung und Partizipation (in Form von Beweisnutzung und Abschreibeverhalten). Theoretische und praktische Implikationen zur Bedeutung spezifischer Motivationslagen im Lehramtsstudium Mathematik sowie gezielte Interventionsmöglichkeiten werden diskutiert.

Across the western United States and elsewhere, the frequency and intensity of wildland fires are projected to increase, posing a challenge to natural‐resource management. While collaborative, multi‐benefit partnerships can provide opportunities to overcome barriers to effective management, in many cases these collaborations have been slow to form. To investigate this issue, we surveyed natural‐resource professionals in California to identify barriers to effective management and potential solutions in the form of collaborations. Analyzing our data using social network analysis, results show that limited financial and human capital were the most salient barriers in our sample, while lack of public support and data were less common concerns. Collaboration networks were dominated by land‐management and other public agencies, as they were the most frequently mentioned as collaborators. Tribes, universities, private‐sector organizations, and watershed‐service providers mentioned other actors as collaborators more often than they were mentioned as collaborators, while the opposite was true for land‐management and other public agencies. Universities, land managers, watershed‐service providers, and private‐sector organizations displayed a strong desire to establish new collaborations, while other public agencies and tribes displayed less of a desire to do so. Overall, this study highlights insufficient financial and human capital as barriers confronting natural‐resource management in California. It also shows that organizations with the capacity and capability to help mitigate these shortcomings, such as the private sector and universities, are among those most positive toward additional collaborations. Our findings suggest that initiatives to expand regional collaborative partnerships could increase the effectiveness of natural‐resource management in California.

Already one year after the publication of Philosophical Investigations , the discussion about a private language had gathered pace. Since then, the debate has moved in various directions: Discussions about Wittgenstein’s method of doing philosophy; about how to read him; about variations of ‘private’ language users ; about private experiences, (private) ostensive definitions, behaviourism, the ‘inner’ and ‘outer’ etc. I have tried to cover those points, which I think crucial for the understanding of a ‘private’ language: the rule-fixing problem, the confusion of giving and using a sample, private charts, knowledge, memory, and justification. I have thereby made extensive use of remarks by Wittgenstein and Rush Rhees, particularly Wittgenstein’s manuscripts, the Whewell Court lectures 1938-41, and unpublished material by Rhees. The reason for this is that I could not have put it in any better words, and that for me these remarks speak for themselves. I wish that others will make a ‘similar’ experience.

Emotion and activity recognition can play an important role in supporting people, especially in the AAL environment. Modeling is one of the important factors that can be used to introduce such support. Metamodel conceptualization provides the basis for designing a modeling framework. Metamodel development is an iterative activity that is commonly evolving over time. This development may need to extend, re-use or integrate a metamodel with others to meet specific requirements. On the one hand, manual integration of a metamodel is time-consuming, tedious and requires special knowledge. On the other hand, the automatic process is challenging, due to the nature and complexity of meta-concepts. Therefore, a method to support metamodel integration is highly desired. In this work, we first consider briefly the fundamentals of modeling from the perspective of cognitive psychology. We then propose an interactive approach to dynamically adapt metamodel creation by allowing the modeler to configure and reuse existing metamodels and generate a woven form. Finally, this approach allows the developer to use such a model in the machine learning activities. We evaluated the integration process against two benchmarks and compared the results. The performance of the proposed approach showed that it enables the generation of a consistent and correct weaved model. It was able to reach about 87% accuracy in retrieving meta-elements.

The automotive sector is swiftly advancing, focusing on driving experiences that prioritize safety and integrate with human emotions and well-being. This chapter explores the transformative potential of combining large language models (LLMs) with context-aware emotion and fatigue recognition techniques in Advanced Driver Assistance Systems (ADAS). The primary objective is to enhance driving experiences and overall well-being through real-time emotional and fatigue assessments. Grounded in Active and Assisted Living (AAL) principles, this chapter emphasizes the practical implementation of bio-signal-based emotion and fatigue estimation techniques within the ADAS framework. Utilizing formal knowledge representation techniques, such as ontologies, demonstrates how contextual modeling can facilitate optimal support services in dynamic driving contexts. Central to this approach is the integration of LLMs with emotion and fatigue recognition methodologies. The chapter details using non-intrusive bio-signal sensors to analyze facial expressions through video, EEG measurements, and voice analysis. This synergy between language comprehension and bio-signal insights enables real-time emotional assessments and fatigue estimations, empowering safer and more responsive driving experiences. LLMs act as the cognitive bridge, enhancing human driver assistance with context-aware emotion and fatigue recognition. The chapter reveals the potential of language models to decode emotional cues and detect fatigue levels, which are crucial for shaping proactive and adaptive driver assistance strategies. Integrating LLMs within ADAS showcases their ability to anticipate driver needs, provide timely alerts, and improve decision-making processes. Importantly, this chapter offers a roadmap for integrating LLMs with context-aware emotion and fatigue recognition into ADAS. Leveraging the capabilities of LLMs presents a scalable method for embedding bio-signal-based emotion and fatigue estimation techniques into ADAS, highlighting the AAL principles while demonstrating the transformative potential of LLMs. This work envisions an advanced iteration of ADAS, fostering a safer, more intuitive, and supportive driving experience.

The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.

Open Access: https://doi.org/10.1080/02604027.2024.2417042 >>>>>>>>>>>>>> If we want our youth to be in a position to work toward a better future, we need to help them envision this future in the first place. This conceptual article argues that the literary-based English language classroom is uniquely positioned to do so. However, an important prerequisite is to adopt a pedagogy that is grounded in hope rather than despair. This article makes the case for hopeful approaches in the English language classroom by exploring how solarpunk as a genre and movement may be used to facilitate and implement this language pedagogy of hope. This discussion is embedded in more general notions of pedagogy of hope, which are being applied to the language education context. Solarpunk and its pedagogical potential is then explored in the dimensions of literary, cultural, and communicative language learning. Two examples serve to illustrate how the use of the genre might be approached both with fictional and nonfictional texts.

Answer set programming is a popular declarative paradigm with countless applications for modeling and solving combinatorial problems. We can view a program as a knowledge database compactly representing conditions for solutions. Often we are interested in reasoning about solutions of filtering answer sets. At the heart of these questions is brave and cautious reasoning. For browsing answer sets, we combine both as restricting atoms of answer sets is only meaningful for atoms called facets that belong to some (brave) but not to all answer sets (cautious). Surprisingly, the precise computational complexity of facet problems remained widely open so far. In this paper, we study the complexity of answer set facets. We establish tight results for reasoning with facets, deciding upper and lower bounds as well as the exact number of facets, and comparing facets. Facet reasoning seems to be a natural problem formalism, residing in complexity families Σᴾ, Πᴾ, Dᴾ, and Θᴾ, up to the third level. Moreover, our study considers quantitative importance questions on facets and generalizing from facets to conjunctions, disjunctions, and arbitrary queries. We complete our results by an experimental evaluation.