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The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties.
Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Software scripts for sensor data extraction in Rasberry Pi: user-space and kernel-space comparison
(2024)
This paper compares two popular scripting implementations for hardware prototyping: Python scripts execut from User-Space and C-based Linux-Driver processes executed from Kernel-Space, which can provide information to researchers when considering one or another in their implementations. Conclusions exhibit that deploying software scripts in the kernel space makes it possible to grant a certain quality of sensor information using a Raspberry Pi without the need for advanced real-time operational systems.
We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewisemonotone hash-functions that preserve local order and support fast range-lookups with fewer memory accesses. bloomRF has near-optimal space complexity and constant query complexity. Although, bloomRF is designed for integer domains, it supports floating-points, and can serve as a multi-attribute filter. The evaluation in RocksDB and in a standalone library shows that it is more efficient and outperforms existing point-range-filters by up to 4× across a range of settings and distributions, while keeping the false-positive rate low.
AI-based prediction and recommender systems are widely used in various industry sectors. However, general acceptance of AI-enabled systems is still widely uninvestigated. Therefore, firstly we conducted a survey with 559 respondents. Findings suggested that AI-enabled systems should be fair, transparent, consider personality traits and perform tasks efficiently. Secondly, we developed a system for the Facial Beauty Prediction (FBP) benchmark that automatically evaluates facial attractiveness. As our previous experiments have proven, these results are usually highly correlated with human ratings. Consequently they also reflect human bias in annotations. An upcoming challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. In this work, we introduce AntiDiscriminationNet (ADN), a superior attractiveness prediction network. We propose a new method to generate an unbiased convolutional neural network (CNN) to improve the fairn ess of machine learning in facial dataset. To train unbiased networks we generate synthetic images and weight training data for anti-discrimination assessments towards different ethnicities. Additionally, we introduce an approach with entropy penalty terms to reduce the bias of our CNN. Our research provides insights in how to train and build fair machine learning models for facial image analysis by minimising implicit biases. Our AntiDiscriminationNet finally outperforms all competitors in the FBP benchmark by achieving a Pearson correlation coefficient of PCC = 0.9601.
In diesem Kapitel wird eine Einführung in die sich abzeichnenden Trends bei der Gestaltung der digitalen Transformation gegeben, wobei der Schwerpunkt auf digitalen Produkten, intelligenten Diensten und damit verbundenen Systemen sowie auf Methoden, Modellen und Architekturen liegt. Das primäre Ziel dieses Buches ist es, einige der neuesten Forschungsergebnisse auf diesem Gebiet hervorzuheben. Wir stellen eine Reihe von Kurzbeschreibungen der im Buch enthaltenen Kapitel zur Verfügung.
Die digitale Transformation ist die heute vorherrschende geschäftliche Transformation, die einen starken Einfluss darauf hat, wie digitale Dienstleistungen und Produkte dienstleistungsdominant gestaltet werden. Eine beliebte zugrundeliegende Theorie der Wertschöpfung und des wirtschaftlichen Austauschs, die als dienstleistungsdominante Logik (S-D) bekannt ist, kann mit vielen erfolgreichen digitalen Geschäftsmodellen verbunden werden. Allerdings ist die S-D-Logik an sich abstrakt. Unternehmen können sie nicht ohne Weiteres als Instrument für die Innovation und Gestaltung von Geschäftsmodellen nutzen. Um dies zu ändern, wird eine umfassende Ideenfindungsmethode auf der Grundlage der S-D-Logik vorgeschlagen, die als service-dominantes Design (SDD) bezeichnet wird. SDD zielt darauf ab, Unternehmen beim Übergang zu einer service- und wertorientierten Perspektive zu unterstützen. Die Methode bietet eine vereinfachte Möglichkeit, den Ideenfindungsprozess auf der Grundlage von vier Modellkomponenten zu strukturieren. Jede Komponente besteht aus praktischen Implikationen, Hilfsfragen und Visualisierungstechniken, die aus einer Literaturrecherche, einer Anwendungsfallbewertung der digitalen Mobilität und einer Fokusgruppendiskussion abgeleitet wurden. SDD ist ein erster Schritt zu einem Toolset, das etablierte Unternehmen bei der Service- und Werteorientierung im Rahmen ihrer digitalen Transformation unterstützen kann.
Unternehmen sind derzeit dabei, ihre Strategie, ihre Prozesse und ihre Informationssysteme zu verändern, um ihren Digitalisierungsgrad zu erhöhen. Das Potenzial des Internets und verwandter digitaler Technologien wie Internet der Dinge, Services Computing, Cloud Computing, künstliche Intelligenz, Big Data mit Analysen, mobile Systeme, Kollaborationsnetzwerke und cyber-physikalische Systeme treibt neue Geschäftsmodelle an und ermöglicht sie. Die Digitalisierung führt zu einer tiefgreifenden Umwälzung bestehender Unternehmen, Technologien und Volkswirtschaften und fördert die Architektur digitaler Umgebungen mit vielen eher kleinen und verteilten Strukturen. Dies hat starke Auswirkungen auf neue Wertschöpfungsmöglichkeiten und die Gestaltung digitaler Dienste und Produkte, die durch die Nutzung einer service-dominanten Logik gesteuert werden. Das Hauptergebnis des Buchkapitels erweitert Methoden für integrale digitale Strategien um wertorientierte Modelle für digitale Produkte und Dienstleistungen, die im Rahmen eines multiperspektivischen digitalen Unternehmensarchitektur-Referenzmodells definiert werden.
Die Digitalisierung, der ständige technologische Fortschritt und immer kürzere Produktlebenszyklen stellen Unternehmen derzeit vor große Herausforderungen. Um am Markt erfolgreich zu sein, müssen Geschäftsmodelle häufiger und schneller als früher an veränderte Marktbedingungen angepasst werden. Schnelle Anpassungsfähigkeit, auch Agilität genannt, ist in der heutigen Zeit ein entscheidender Wettbewerbsfaktor. Aufgrund des ständig wachsenden IT-Anteils von Produkten und der Tatsache, dass diese mit Hilfe von IT hergestellt werden, hat die Änderung des Geschäftsmodells große Auswirkungen auf die Unternehmensarchitektur (EA). Die Entwicklung von EAs ist jedoch eine sehr komplexe Aufgabe, da viele Beteiligte mit gegensätzlichen Interessen in den Entscheidungsprozess eingebunden sind. Daher ist ein hohes Maß an Zusammenarbeit erforderlich. Um Unternehmen bei der Entwicklung ihrer EA zu unterstützen, wird in diesem Artikel eine neuartige integrative Methode vorgestellt, die die Interessen der Stakeholder systematisch in die Entscheidungsfindung einbezieht. Durch die Anwendung der Methode wird die Zusammenarbeit zwischen den beteiligten Interessengruppen verbessert, indem Berührungspunkte zwischen ihnen identifiziert werden. Darüber hinaus machen die standardisierten Aktivitäten die Entscheidungsfindung transparenter und vergleichbarer, ohne die Kreativität einzuschränken.
The volume includes papers presented at the International KES Conference on Human Centred Intelligent Systems 2023 (KES HCIS 2023), held in Rome, Italy on June 14–16, 2023. This book highlights new trends and challenges in intelligent systems, which play an important part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital businesses and intelligent systems based on human practices, as well as the study of interaction and the co-adaptation of humans and systems.
Current advances in Artificial Intelligence (AI) combined with other digitalization efforts are changing the role of technology in service ecosystems. Human-centered intelligent systems and services are the target of many current digitalization efforts and part of a massive digital transformation based on digital technologies. Artificial intelligence, in particular, is having a powerful impact on new opportunities for shared value creation and the development of smart service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological experiences from academia and practice on a joint view of digital strategy and architecture of intelligent service ecosystems and explores the impact of digitalization based on real case study results. Digital enterprise architecture models serve as an integral representation of business, information, and technology perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on the novel aspect of closely aligned digital strategy and architecture models for intelligent service ecosystems and highlights the fundamental business mechanism of AI-based value creation, the corresponding digital architecture, and management models. We present key strategy-oriented architecture model perspectives for intelligent systems.
In today’s education, healthcare, and manufacturing sectors, organizations and information societies are discussing new enhancements to corporate structure and process efficiency using digital platforms. These enhancements can be achieved using digital tools. Industry 5.0 and Society 5.0 give several potentials for businesses to enhance the adaptability and efficacy of their industrial processes, paving the door for developing new business models facilitated by digital platforms. Society 5.0 can contribute to a super-intelligent society that includes the healthcare industry. In the past decade, the Internet of Things, Big Data Analytics, Neural Networks, Deep Learning, and Artificial Intelligence (AI) have revolutionized our approach to various job sectors, from manufacturing and finance to consumer products. AI is developing quickly and efficiently. We have heard of the latest artificial intelligence chatbot, ChatGPT. OpenAI created this, which has taken the internet by storm. We tested the effectiveness of a considerable language model referred to as ChatGPT on four critical questions concerning “Society 5.0”, “Healthcare 5.0”, “Industry,” and “Future Education” from the perspectives of Age 5.0.
Enterprises and societies currently face essential challenges, and digital transformation can contribute to their resolution. Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies covering ecosystem partners. The advancement of new business models can be promoted with digital platforms and architectures for Industry 4.0 and Society 5.0. Therefore, products from the sector of healthcare, manufacturing and energy, etc. can increase in value. The adaptive integrated digital architecture framework (AIDAF) for Industry 4.0 and the design thinking approach is expected to promote and implement the digital platforms and digital products for healthcare, manufacturing and energy communities more efficiently. In this paper, we propose various cases of digital transformation where digital platforms and products are designed and evaluated for digital IT, digital manufacturing and digital healthcare with Industry 4.0 and Society 5.0. The vision of AIDAF applications to perform digital transformation in global companies is explained and referenced, extended toward the digitalized ecosystems such as Society 5.0 and Industry 4.0.
This paper presents a description model for smart, connected devices used in a manufacturing context. Similar to the wide spread adoption of smart products for personal and private usage, recent developments lead to a plethora of devices offering a variety of features and capabilities. Manufacturing companies undergoing digital transformation demand guidance with respect to the systematic introduction of smart, connected devices. The introduction of smart connected devices constitutes a strategic decision cost due to the high future committed cost after introduction and maintaining a smart device fleet by a vendor. This paper aims to support the introduction efforts by classifying the devices and thus helping companies identify their specific requirements for smart, connected devices before initiating widespread procurement. By mapping the features of these devices based on various attributes, allows the clustering of smart, connected devices including a requirement list for their implementation on the shopfloor. Four individual commercially available smart connected devices were analyzed using the description model.
Parallel grippers offer multiple applications thanks to their flexibility. Their application field ranges from aerospace and automotive to medicine and communication technologies. However, the application of grippers has the problem of exhibition wear and errors during the execution of their operation. This affects the performance of the gripper. In this context, the remaining useful life (RUL) defines the remaining lifespan until failure for an asset at a particular time of operation occurs. The exact lifespan of an asset is uncertain, thus the RUL model and estimation must be derived from available sources of information. This paper presents a method for the estimation of the RUL for a two-jaw parallel gripper. After the introduction to the topic, an overview of existing literature and RUL methods are presented. Subsequently, the method for estimating the RUL of grippers is explained. Finally, the results are summarized and discussed before the outlook and further challenges are presented.
Context
Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking.
Objective
We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this.
Method
We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a rule and one that was a violation of this rule. Participants answered comprehension questions and rated the perceived difficulty.
Results
For 11 of the 12 rules, we found that violation performed significantly worse than rule for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for violation.
Conclusions
Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.
In recent years, 3D facial reconstructions from single images have garnered significant interest. Most of the approaches are based on 3D Morphable Model (3DMM) fitting to reconstruct the 3D face shape. Concurrently, the adoption of Generative Adversarial Networks (GAN) has been gaining momentum to improve the texture of reconstructed faces. In this paper, we propose a fundamentally different approach to reconstructing the 3D head shape from a single image by harnessing the power of GAN. Our method predicts three maps of normal vectors of the head’s frontal, left, and right poses. We are thus presenting a model-free method that does not require any prior knowledge of the object’s geometry to be reconstructed.
The key advantage of our proposed approach is the substantial improvement in reconstruction quality compared to existing methods, particularly in the case of facial regions that are self-occluded in the input image. Our method is not limited to 3d face reconstruction. It is generic and applicable to multiple kinds of 3D objects. To illustrate the versatility of our method, we demonstrate its efficacy in reconstructing the entire human body.
By delivering a model-free method capable of generating high-quality 3D reconstructions, this paper not only advances the field of 3D facial reconstruction but also provides a foundation for future research and applications spanning multiple object types. The implications of this work have the potential to extend far beyond facial reconstruction, paving the way for innovative solutions and discoveries in various domains.
The aim of this work is the development of artificial intelligence (AI) application to support the recruiting process that elevates the domain of human resource management by advancing its capabilities and effectiveness. This affects recruiting processes and includes solutions for active sourcing, i.e. active recruitment, pre-sorting, evaluating structured video interviews and discovering internal training potential. This work highlights four novel approaches to ethical machine learning. The first is precise machine learning for ethically relevant properties in image recognition, which focuses on accurately detecting and analysing these properties. The second is the detection of bias in training data, allowing for the identification and removal of distortions that could skew results. The third is minimising bias, which involves actively working to reduce bias in machine learning models. Finally, an unsupervised architecture is introduced that can learn fair results even without ground truth data. Together, these approaches represent important steps forward in creating ethical and unbiased machine learning systems.
In den letzten Jahren hat der Trend zur Digitalisierung und Konnektivität die Kundenerwartungen an den B2B-Kundenservice verändert. Vorliegender Artikel arbeitet mit zwei klaren Studienzielen und untersucht zum einen die Rolle von IoT (Internet of Things) und Cybersicherheit als Erfolgsfaktoren für den Business-to-Business (B2B) Kundenservice und zum anderen wie eine sichere Integration zu einem Wettbewerbsvorteil auf dem deutschen Markt beitragen kann. Durch einen qualitativen Ansatz mithilfe von 20 Befragungen wurde untersucht, dass IoT und Cybersicherheit als Erfolgsfaktoren für den deutschen B2B-Kundenservice angesehen werden können. Als Ergebnis liefert diese Studie fünf Kernaussagen (Hypothesen) aus qualitativen Interviews. Neben der Diskussion allgemeiner Erfolgsfaktoren und deren Einfluss, wurde die Rolle von IoT bei der Optimierung des B2B Kundendienstes diskutiert. Zudem werden potenzielle Sicherheitsrisken in Zusammenhang mit den Dienstleistungsmodellen, notwendige Anforderungen an Cybersicherheit sowie Datenerfassung erörtert. Abschließend wurde ein Modell entwickelt, das interne und externe Aspekte aufzeigt, die dazu beitragen, dass IoT und Cybersicherheit als Erfolgsfaktoren in der Aktivitätskette des Kunden in der Pre-Sales‑, Sales- und After-Sales-Phase erlebt werden.
Dieser praxis-nahe und industrie-übergreifende Artikel liefert somit Einblicke basierend auf qualitativen Erkenntnissen für weitere Forschung in der Theorie und befähigt Organisationen das Thema ganzeinheitlich zu betrachten.
Das Motto der diesjahrigen Informatics Inside wird, wie ich finde, in beeindruckender Weise gegenwärtig durch Werkzeuge der generativen KI demonstriert. ChatGPT, Midjourney und Co. ermöglichen uns eine innovative Interaktion mit Information, die uns auffordert unsere bisherigen Vorstellungen von Erkenntnisfähigkeit und Wertschöpfung zu überdenken. Diese Notwendigkeit ist in der Informatik zwar bereits seit den 1930er Jahren bekannt, aber erst die praktische Umsetzung mit modernen Computern macht die formalen Überlegungen hierzu erfahrbar. Daraus resultierende Verunsicherungen, beispielsweise im Hinblick auf Arbeitsplatze, sind gleichermaßen Herausforderung und Chance dieses wichtige Thema einer breiten Öffentlichkeit bekannt zu machen. Hierbei wird einmal mehr deutlich wie tiefgreifend die Informatik in unsere Leben hineinwirkt und welche Verantwortung damit verbunden ist. Vor diesem großen Hintergrund könnte der Hinweis auf Bits und Bytes im Tagungsmotto fast schon wie ein unbedeutendes Detail wirken, was jedoch weit gefehlt wäre. Folgen aus Null und Eins bilden nach wie vor die Bausteine der Informatik und es ist die Aufgabe der angewandten Informatik hieraus nützliche und sinnvolle Anwendungen zu kombinieren.
Die Informatics Inside bietet hierfür einen entsprechenden Rahmen bereits in der akademischen Ausbildung. Unsere Studierenden planen, organisieren und gestalten diese Tagung jedes Jahr eigenstandig. Auch die Themen für die Fachbeiträge wurden von den Studierenden eigenstandig ausgewählt. Aus meiner Sicht bilden die resultierenden Ausarbeitungen in diesem Tagungsband die spannende Vielfalt von Anwendungsthemen des Human Centered Computings sehr gut ab. Dabei zeigt sich ebenfalls deutlich die Bereitschaft unserer Studierenden, die Verantwortung für eine sinnvolle und kreative Gestaltung der digitalen Zukunft zu übernehmen.
Reutlingen, den 15.11.2023 Prof. Dr. rer. medic. Christian Thies