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Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
No full textIn recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests
Entrepreneurial Leadership
No full textStart-ups und Unternehmensgründungen ebenso wie Projekt- oder Innovationsmanagement brauchen gleichermaßen neben dem einschlägigen Fachwissen und -erfahrungen Leadership-Skills als Erweiterung klassischer Managementanforderungen. Während diese prozessorientiert konkrete Ziele der strategischen oder operativen Planung umsetzen und optimieren, legt Leadership wie Entrepreneurship den Fokus auf Vision mit entsprechender Motivation zur Gründung oder Veränderung. (Verlagsangaben
HLA-DQA1/DQB1 Genes Typing and Exosomes Characterization for the Assessment of Celiac Disease Risk in a Chilean Population
No full textIntroduction: Celiac Disease (CD) is a multisystemic auto-mmune disorder triggered by gluten in HLA genetically predisposed individuals. HLA-DQ genotyping is useful to assess the individual susceptibility to CD but still not sufficient for early diagnosis. Here, we propose HLA-DQA1 and HLA-DQB1 gene typing and exosomes characterization as new tool for CD prevention and diagnosis.
Methods: A Chilean population (n=30) was investigated for SNPs mutations in HLA Class II alleles associated with CD predisposition, using the GenoChip Food Technology. Exosomes have been isolated from donors’ serum by ultracentrifugation and characterized by Western Blotting (for CD63 and CD9) and transmission electron microscopy. Exosomes were also studied for their interleukin-1ra content.
Results: Among the studied population, 45.84, 37.46, and 16.70% were carrying alleles encoding for MHC-DQ heterodimers associated with extremely high, high, and extremely low risk to develop CD. The exosome size decreased significantly (p<0.05) when derived from extremely low CD risk donors (44.58 ± 7.88 nm). In parallel, isolated Exosomes from donors with high and extremely high CD risk showed higher IL-1ra content. The values increase within the extremely high-risk group (108.8 ± 15.91 and 148.8 ± 12.37 pg/mL), as the CD persons were not following any treatment. However, these values were lower (52.50 ± 3.54 and 48.75 ± 6.52 pg/mL) in exosomes isolated from CD patients after treatment.
Conclusion: A relationship between exosomes’ size and IL-1ra content, and genetic susceptibility for CD has been observed, suggesting their possible use as biomarkers for CD prevention and diagnosis
8. Usable Security und Privacy Workshop
No full textZiel der achten Auflage des wissenschaftlichen Workshops “Usable Security and Privacy” auf der Mensch und Computer 2022 ist es, aktuelle Forschungs- und Praxisbeiträge zu präsentieren und anschließend mit den Teilnehmenden zu diskutieren. Der Workshop soll ein etabliertes Forum fortführen und weiterentwickeln, in dem sich Experten aus verschiedenen Bereichen, z. B. Usability und Security Engineering, transdisziplinär austauschen können
Generating Musical Compositions through a Data-Driven Approach along with Static Implementations of Theoretical Principles
No full textIn the field of automatic music generation, one of the greatest challenges is the consistent generation of pieces continuously perceived positively by the majority of the audience since there is no objective method to determine the quality of a musical composition. However, composing principles, which have been refined for millennia, have shaped the core characteristics of today's music. A hybrid music generation system, mlmusic, that incorporates various static, music-theory-based methods, as well as data-driven, subsystems, is implemented to automatically generate pieces considered acceptable by the average listener. Initially, a MIDI dataset, consisting of over 100 hand-picked pieces of various styles and complexities, is analysed using basic music theory principles, and the abstracted information is fed into explicitly constrained LSTM networks. For chord progressions, each individual network is specifically trained on a given sequence length, while phrases are created by consecutively predicting the notes' offset, pitch and duration. Using these outputs as a composition's foundation, additional musical elements, along with constrained recurrent rhythmic and tonal patterns, are statically generated. Although no survey regarding the pieces' reception could be carried out, the successful generation of numerous compositions of varying complexities suggests that the integration of these fundamentally distinctive approaches might lead to success in other branches
Moving from opposition to taking ownership of open science to make discoveries that matter
No full textGuzzo et al. (Reference Guzzo, Schneider and Nalbantian2022) argue that open science practices may marginalize inductive and abductive research and preclude leveraging big data for scientific research. We share their assessment that the hypothetico-deductive paradigm has limitations (see also Staw, Reference Staw2016) and that big data provide grand opportunities (see also Oswald et al., Reference Oswald, Behrend, Putka and Sinar2020). However, we arrive at very different conclusions. Rather than opposing open science practices that build on a hypothetico-deductive paradigm, we should take initiative to do open science in a way compatible with the very nature of our discipline, namely by incorporating ambiguity and inductive decision-making. In this commentary, we (a) argue that inductive elements are necessary for research in naturalistic field settings across different stages of the research process, (b) discuss some misconceptions of open science practices that hide or discourage inductive elements, and (c) propose that field researchers can take ownership of open science in a way that embraces ambiguity and induction. We use an example research study to illustrate our points
Towards Detection of Malicious Software Packages Through Code Reuse by Malevolent Actors
No full textTrojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a 1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages
