Reviewerly/exHarmony huggingface dataset:
Bridging the Gap between Security Competencies and Security Threats: Toward a Cyber Security Domain Model

Security incidents are increasing in a wide range of organizational types and sizes worldwide. Although various threat models already exist to classify security threats, they seem to take insufficient account of which organizational assets the threat events are targeting. Therefore, we argue that conducting more job-specific IT security training is necessary to ensure organizational IT security. This requires considering which assets employees use in their daily work and for which threat events employees need to build up IT security competencies. Subsequently, we build a framework-based Cyber Security Domain Model (CSDM) for IT-secure behavior. We follow the Evidence Centered Assessment Design (ECD) to provide a deep- dive analysis of the domain for IT-secure behavior. As the leading result relevant for research and practice, we present our CSDM consisting of 1,087 cyber threat vectors and apply it to five job specifications.
Immersive Virtual Reality in Experiential Learning - A Value Co-creation and Co-destruction Approach

Immersive Virtual Reality (later VR) has its potential in enabling learning experiences. Several studies adopt experiential learning as a key concept to understand the outcomes of VR. This study consists of two parts – the first part conducts a systematic literature review on VR experiential learning and suggests seven main dimensions for the concept identified by the existing literature: engagement, sociability, contextual information, physical sensation, interactivity, cognitions, and presence. The second part adopts a value co-creation and co-destruction approach to empirically test the construction underlying VR experiential learning. The findings indicate 33 value co-creation and 19 value co-destruction constructs contributing to the seven dimensions. The suggested seven value construct dimensions combined with our own empirical findings and the theory of experiential learning, our research results build understanding about the experiential learning in the VR context and further encourages future VR learning research to test and validate these propositions.
Challenges to Open Collaborative Data Engineering

Open data is data that can be used, modified, and passed on, for free, similar to open-source software. Unlike open-source, however, there is little collaboration in open data engineering. We perform a systematic literature review of collaboration systems in open data, specifically for data engineering by users, taking place after data has been made available as open data. The results show that open data users perform a wide range of activities to acquire, understand, process and maintain data for their projects without established best practices or standardized tools for open collaboration. We identify and discuss technical, community, and process challenges to collaboration in data engineering for open data.
Design Principles for Blockchain-based Applications in Green Bond Reporting

Emerging sustainable capital markets are crucial in reaching global climate goals. These markets’ credibility depends on the trustworthiness of data used to report the green impact of projects financed by sustainable financial instruments such as green bonds. To ensure credibility and thereby support these types of markets, the information systems field has the potential to create designs that leverage emerging technologies (in our case, blockchain) for green bond reporting. In this paper, we employ a design science research method to derive a set of design principles. These design principles discuss the most relevant blockchain concepts and reflect the characteristics of today’s green bond process. Through an artificial evaluation and demonstration of the design principles, we report on how to make informed design decisions to develop blockchain applications for green bond reporting. These findings are also significant for practitioners to develop new services or re-design current processes.
Hebbian Continual Representation Learning

Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks. To reduce this performance gap, we investigate the question whether biologically inspired Hebbian learning is useful for tackling continual challenges. In particular, we highlight a realistic and often overlooked unsupervised setting, where the learner has to build representations without any supervision. By combining sparse neural networks with Hebbian learning principle, we build a simple yet effective alternative (HebbCL) to typical neural network models trained via the gradient descent. Due to Hebbian learning, the network have easily interpretable weights, which might be essential in critical application such as security or healthcare. We demonstrate the efficacy of HebbCL in an unsupervised learning setting applied to MNIST and Omniglot datasets. We also adapt the algorithm to the supervised scenario and obtain promising results in the class-incremental learning.
The Impact of Artificial Intelligence on News Production and Communication in the Smart Media Era

Modernization of the media and the advent of artificial intelligence have led to significant changes in the process of news production. AI plays an important role in improving the efficiency and accuracy of reporting. With the help of machine learning algorithms, AI can independently study and analyze information flows, automatically analyze and select the most relevant information. This allows journalists to save time searching and verifying data, while also providing accurate and timely information. With the development of AI, algorithms for automatic content creation were created. This is useful in cases where events such as fires or earthquakes need to be reported quickly and there is no time to wait for manual documentation. However, AI cannot completely replace humans in creating stories because it lacks love and aesthetic taste. AI algorithms can automatically check facts and structured information to create smarter, more readable content. This will improve the quality of the article and increase readers' confidence in the information. The use of artificial intelligence in journalism is an important part of modern journalism. The more efficient and accurate the reporting, the better the information for readers. It is also necessary to adhere to ethical and legal standards when using AI to avoid manipulation and falsification of information. Thus, artificial intelligence has a significant impact on the production and delivery of information, increasing efficiency and accuracy. However, ethical and legal considerations must be taken into account when using AI in communications.
YUMA – An AI Planning Agent for Composing IT Services from Infrastructure-as-Code Specifications

Infrastructure-as-code enables cloud architects to automate IT service delivery by specifying IT services through machine-readable definition files. To allow for a reusability of the infrastructure-as-code specifications, cloud architects specify IT services as compositions of sub-processes. As the AI planning agents for automated IT service composition proposed by prior research fall short in the infrastructure-as-code context, we design a search-based problem-solving agent named YUMA according to a design science research process to fill this research gap. YUMA holds a search tree reflecting the state space and transition model. It includes an algorithm for building the search tree and two algorithms for determining the minimum composition plan. The underlying IT service composition problem is explicated for the infrastructure-as-code context and formulated as a search problem. The results of the demonstration and evaluation show that YUMA fulfills the requirements necessary to solve this problem and digitizes an important task of cloud architects.
5 − 4 ≠ 4 − 3: On the Uneven Gaps between Different Levels of Graded User Satisfaction in Interactive Information Retrieval Evaluation

Similar to other ground truth measures, graded user satisfaction has been frequently employed as a continuous variable in information retrieval evaluation based on the assumption that intervals between adjacent grades are quantitatively equal. To examine the validity of equal-gap assumption and explore dynamic perceptual thresholds triggering grade changes in search evaluation, we investigate the extent to which users are sensitive to changes in search efforts and outcomes across different gaps of graded satisfaction. Experiments on four user study datasets (15,337 queries) indicate that 1) User satisfaction sensitivity, especially to offline evaluation metrics, changes significantly across gaps in satisfaction scale; 2) the size and direction of changes in sensitivity vary across study settings, search types, and intentions, especially within “3-5” scale subrange. This study speaks to the fundamentals of user-centered evaluation and advances the knowledge of heterogeneity in satisfaction sensitivity to search efforts and gains and implicit changes in evaluation thresholds.
Building Digital Trust to Protect Whistleblowers - A blockchain-based Reporting Channel

Organizations today need internal reporting channels to report illegal/unethical misconduct. For this purpose, organizations set up one or more - often digital - internal reporting channels. Persons/Employees who want to report misconduct, so-called whistleblowers, expose themselves to reprisals and therefore need trustworthy reporting channels which ensure ´Digital Trust´. Blockchain, a technology that overcomes the need for trust due to its properties of immutability and integrity of data, could be promising as underlying technology for a digital reporting channel which is recognized as trustworthy. In our research, we explored multiple perspectives relevant to a trustworthy digital reporting system. Applying design science research, we evaluated the current state of the art of (digital) reporting channels and developed a prototypical blockchain-based reporting solution called “Integrity@Inside”. The prototype is being iteratively demonstrated and pre-evaluated.
Comparing Methods for Mitigating Gender Bias in Word Embedding

Word embedding captures the semantic and syntactic meaning of words into dense vectors. It contains biases learning from data that include constructs, cultural stereotypes, and inequalities of the society. Many methods for removing bias in traditional word embedding have been proposed. In this study we use the original GloVe word embedding and perform a comparison among debiasing methods built on top of GloVe in order to determine which methods perform the best removing bias. We have defined half-sibling regression, repulsion attraction neutralization GloVe method and compared it with gender-preserving, gender-neutral GloVe method and other debiased methods. According to our results, no methods outperform in all the analyses and completely remove gender information from gender neutral words. Furthermore, all the debiasing methods perform better than the original GloVe.
A Model-driven Method to Design SoaML Services from BPMN Models: Principles, Proof-of-concept, and Validation

Today's business processes are increasingly complex as they cross organizational boundaries. To execute their business processes, organizations develop software applications called Process-Aware Information System (PAIS). PAIS designers must consider complex scenarios involving multiple partners. Consequently, the architectural design of high quality PAIS is complex and requires vast amounts of knowledge and skills both in software architecture and in the business domain. This paper proposes a model-driven method to design the architecture of PAIS using the service-oriented architecture (SOA) style. The proposed method generates SOA-based design models expressed in SoaML from the specifications of collaborative business processes expressed in BPMN. We developed a prototype tool using the Eclipse Modeling Framework (EMF) ecosystem. We tested the method on a set of processes from the Enterprise Resource Planning literature to assess its effectiveness. Our results show that 80.95\% of the identified services were relevant and corresponded to what architecture specialists expected.
Transferring Well-Performing Collaborative Work Practices with Parameterized Templates and Guidebooks: Empowering Subject Matter Experts for an Adaptation to Slightly Different Contexts

Collaborative work practices (CWPs) package facilitation expertise and have the potential to increase team productivity up to 90%. Collaboration engineers develop CWPs and deploy them to practitioners that execute them. These CWPs, however, are typically customized to conditions of a specific use case. This creates the challenge that changing use case conditions or even small variations across contexts, hinder well-performing CWPs of being applied more often to create a long-term value. Practitioners fail to adapt existing CWPs due to missing collaboration expertise and adaptation guidelines. To address this challenge in collaboration engineering literature, we introduce a) the Subject Matter Expert role; b) the ‘CWP Adaptation Approach’ that formalizes the transfer of CWPs to different contexts with parameterized Templates and Guidebooks. To show a first proof-of-concept, we further inductively generalize from an exemplarily use case with a well-performing CWP in the educational domain.
Context Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcription

The paper presents how organizational practices enable the improvement and maintenance of task performance in a learning human-in-the-loop system exposed to a wide range of context changes. We investigate how the case company tripled the efficiency of medical transcribers by leveraging its machine learning-based automatic speech recognition technology. We find that the focal system operated across stable, drifting, and jumping contexts. Despite changes, it continued to improve or maintained performance thanks to two sets of organizational practices aligning it with the context: extending and refining. This paper makes two key contributions: It shows the importance of considering context changes in the design and operation of learning human-in-the-loop systems. Our empirical findings help with resolving some contradictory outcomes of the recent conceptual work. Secondly, we show that context alignment practices are situated at the sociotechnical system level and, thus, are not just technical solution nor can be detached from social elements.
Image Attribute Estimation for Forensic Image Reconstruction from Fragments

The increasing prevalence of cyber-crime has led to a surge of new forensics tools aimed at collecting digital evidence from a suspect’s computer. A suspect’s hard drive can be the largest source of collected information, but the task of collection can be made significantly more difficult when the contents of a hard drive are deleted or damaged. In these circumstances the information needed to read files normally may be missing, leaving only the raw, often fragmented, data behind. If we were able to reliably reconstruct files from this raw data, then it would be more difficult for suspects to destroy potential evidence. In this paper, we focus on the reconstruction of an image from a set of fragments. This research contributes a novel image reconstruction method which utilizes pre-stitch data extraction on individual data sectors. We show that, when certain attributes are successfully extracted from the data sectors, this method yields a high reconstruction accuracy even when used with a naive stitching algorithm on heavily fragmented image files.
Leading Digital Innovation Units: A Repertory Grid Study about Key Skills for the Digital Age

To combat the trend of failing Digital Transformation endeavors, dedicated leadership skills are needed. However, so far there is little knowledge about the skills for successfully leading Digital Innovation Units (DIUs). We, therefore, interviewed 13 DIU leaders from various industries and elicited their skills with the Repertory Grid method. We identified 54 key skills clustered in seven categories: team development, integration of the DIU into the wider context of the organization, innovation management, personal traits, effective communication, hard skills, and visionary thinking and driving change. Furthermore, we found five influencing factors for the application of key skills: Financial support, uncertainty, certainty in tasks, stability of the product, freedom to operate, and degree of collaboration with the core organization. Finally, we report on three skill enhancers (actions, attitudes, conditions) for practitioners. We complement prior research by improving the understanding of the ideal DIU leader skill profile.
Addressing Organisational, Individual and Technological Aspects and Challenges in Information Security Management: Applying a Framework for a Case Study

This study investigates information security management challenges in a large organisation. The aim of this study is to apply the Technological-Organisational-Individual (TOI) Framework in this organisation to determine to what extent current security management practices are informed by findings of relevant literature and standards on information security incorporated in the framework. The TOI framework is used to map factors influencing security behavior to current practices applied by the organisation and to analyse them. Conclusions suggest that some factors that play a critical role in information security management are not adequately covered. This study also aims to provide recommendations to security managers on how to address these factors to implement security management practices that can improve ISP compliance, and inform literature on any additional security management practices. Further, this study includes insights into how organisations may exploit key strengths in applying information security management to achieve good security behaviour among their employees and take an adaptive approach to changing conditions, such as teleworking.
Blockchain-based Continuous Timestamps Tracking System: Towards Ownership Information Believability

Ownership information of high value assets such as property is often concealed and fragmented, adversely affecting information believability. Following the design science research approach, we conceptualize believability as a data quality dimension that supports ownership traceability. We then investigate how blockchain technology might improve information believability in ownership traceability systems. We represent and address our findings via the development of a blockchain-based continuous timestamps tracking system model, framework and implementation for property ownership. A use case of banking transactional data for property ownership traceability is introduced to illustrate our workflow and system design. The proposed system takes advantage of blockchain technology such as traceability and irreversibility to support information believability in the design, management, and use of information systems.
Market Reaction to Cyber Strategy Disclosure: Word Embedding Derived Approach

In this study, we use a semi-supervised natural language processing (NLP) methodology to assess cybersecurity strategy of firms based on their 10-K filings. Adapted from the Cybersecurity Framework developed by the National Institute of Standards and Technology (NIST), five distinct cybersecurity strategies, namely identification, protection, detection, response, and recovery, are measured annually. We find evidence that cybersecurity identification strategy is positively and significantly associated with firm market value. For those firms experienced a cyberattack in the past, disclosing cybersecurity protection strategy is not positively assessed by the market. This paper makes contribution to the literature on cybersecurity by identifying the cyber strategies disclosed in 10-K reports using textual analysis, which can be used in future cyber studies. We further show empirical evidence of how market reacts to different strategies, which have valuable implications for industry as to how to better manage cyber risk.
Decentralized Service: An Initiation of Blockchain Value Creation into Service Science

How value is created through service has recently undergone massive changes. Centralized service provision with clear distinctions between service offerers and beneficiaries is increasingly superseded by value creation within decentralized networks of distributed actors integrating digital resources equally. One of the drivers of this transformation is blockchain technology. Applying the lens of service-dominant logic and discussing examples of blockchain-based decentralized finance, we shed light on how properties of decentralized technology stimulate value creation in service ecosystems. With this conceptual research, we postulate five propositions of decentralized value creation along the axiomatic foundations of the service-dominant logic. We provide first definitions for decentralized service as well as decentralized service ecosystems. Thereby, we contribute with an extension of the service-dominant logic to the context of decentralized ecosystems. To our knowledge, this research is among the first to add to the growing literature on blockchain value creation from a service science perspective.
‘What Have We Learned from Market Design?’ Blockchain and The Conditions for a Well-Functioning Market

Blockchain technologies, specifically those related to cryptocurrencies and non-fungible tokens (NFTs), have recently garnered significant public interest. Blockchain has been lauded as potentially transformative across various industries, including decentralized finance, supply chain management, healthcare, file storage, and more. However, it has also been the source of much investor angst, evidenced by numerous media headlines reporting instances of fraud and catastrophic asset losses. In this paper, we examine whether the current incarnations of blockchain technologies provide a well-functioning market to its participants. We apply Roth’s market design framework to evaluate whether blockchain marketplaces provide safety, thickness, low congestion, and low repugnance. We find that blockchain applications in their current state do not represent a well-functioning market. We conclude by suggesting additional research avenues and explore whether they can still provide value to organizations even in their suboptimal market state.