Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Adoption of Crypto-Assets: Exploring the Role of Trust, Self-Efficacy, and Risk

Over the last years, crypto-assets have gained signiﬁcant interest from private investors, academia, and industry. While the user population and their motivations, perceptions, and behaviors have been studied, non-adopters and factors inﬂuencing their decision have been left unexplored. This work ﬁlls this knowledge gap and sheds light on the effects of trust, perceived self-efﬁcacy, and risk, which have been shown to be the key antecedents to technology acceptance, on the adoption intention of non-users. We propose and empirically test a theoretical model that explains the adoption intention of crypto-assets among those, who decided against using them. The validity of the model is assessed in a structural equation model analysis of 204 non-users. Results revealed that trust is a critical factor affecting adoption intention, with perceived self-efﬁcacy having a mediating effect. Building on the results, practical recommendations are offered that could lower the entry barriers and facilitate the adoption of crypto-assets.
Current Requirements and Implementations in the field of Web Tracking in the pre-age of E-Privacy Regulation

Website operators have an interest in storing and evaluating data about their users. On this basis, websites will be optimized and marketing campaigns carried out. Cookies are a standardized method of data storage in the field of web-tracking and contain a variety of different information about a user. Often personal data is stored in cookies to identify a user. At this point, the legal requirements of the ePrivacy (Cookie) Directive and the General Data Protection Regulation must be observed. The new ePrivacy Regulation has not yet come into force and is expected in 2021. This research aims to develop legal and functional-technical requirements to meet the current challenges. As a general source for the development, a literature review and qualitative expert interviews have been carried out. For the validation, we conducted a study and implemented a web scraper in order to analyze the top 100 e-commerce websites in Germany.
Ai for Tackling Hate speech

Online hate speech constitutes a universal IS phenomenon. While past research has started to define, conceptualize, and delineate hate speech, it has barely attempted to propose how IT – in the form of AI applications – can help tackle hate speech on social media platforms. Here, this research-in-progress paper aims to contribute. To this end, we first integrate definitions and concepts to build a detailed understanding of hate speech. Second, we elaborate on the regulatory context and its geographical boundaries for the development of AI systems that tackle hate speech. Third, in a yet to be completed phase of the project, we build and evaluate an AI system to help tackling hate speech in a predefined setting. We close with a summary of the project, its limitations, and expected contribution.
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing

The time-critical analysis of social media streams is important for humanitarian organizations to plan rapid response during disasters. The crisis informatics research community has developed several techniques and systems to process and classify big crisis-related data posted on social media. However, due to the dispersed nature of the datasets used in the literature, it is not possible to compare the results and measure the progress made towards better models for crisis informatics. In this work, we attempt to bridge this gap by combining various existing crisis-related datasets. We consolidate eight annotated data sources and provide 166.1k and 141.5k tweets for informativeness and humanitarian classification tasks, respectively. The consolidation results in a larger dataset that affords the ability to train more sophisticated models. To that end, we provide binary and multiclass classification results using CNN, FastText, and transformer based models to address informativeness and humanitarian tasks, respectively. We make the dataset and scripts available at https://crisisnlp.qcri. org/crisis_datasets_benchmarks. html.
Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems

In this paper, to maximize the energy efficiency (EE) in the two-hop multi-relay cooperative decoding and forwarding (DF) system for simultaneous wireless information and power transmission (SWIPT), an optimal power allocation algorithm is proposed, in which the relay energy harvesting (EH) adopts a nonlinear model. Under the constraints, including energy causality, the minimum transmission quality of information and the total transmission power at the relays, an optimization problem is constructed to jointly optimize the transmit power and power-splitting (PS) ratios of multiple relays. Although this problem is a nonlinear fractional programming problem, an iterative algorithm is developed to obtain the optimal power allocation. In particular, the joint power allocation at multiple relays is first decoupled into a single relay power allocation, and then single-relay power allocation is performed by the Dinkelbach iteration algorithm, which can be proven that it is a convex programming problem. Its closed form solutions for different polylines of EH models are obtained by using mathematical methods, such as monotonicity, Lagrange multipliers, the KKT condition and the Cardan formula. The simulation results show the superiority of the power allocation algorithm proposed in this paper in terms of EE.
Neural Process Mining: Multi-Headed Predictive Process Analytics in Practice

Ever growing data availability combined with rapid progress in the ﬁeld of analytics has laid the foundation for the emergence of Business Process Management in general and Business Process Analytics in particular. Going beyond descriptive process log analysis, manufacturing companies strive to leverage predictive process analytics to generate process-related predictions. However, current implementations are limited in their practical use as they are not able to combine multiple heterogeneous data sources without loss of information. To overcome this limitation, we propose a deep learning based approach leveraging multi-headed neural networks. We develop and evaluate the proposed approach in the context of a real-world disruption type classiﬁcation showcase. To this end, we cooperate with a medium-sized German manufacturing company and observe promising results in an initial evaluation.
Statistical Tracking Behavior Analysis for the Affine Projection Algorithm Based on Direction Error

Under the condition that the step size is less than one, a statistical tracking behavior analysis for the affine projection algorithm based on direction error is discussed. When the unknown true weight vector is modeled by the stochastic walk model, the mean weight error is derived under the four assumptions based on the deterministic recursive equation. Furthermore, the statistical tracking behavior of the steady state is analyzed for the affine projection algorithm based on direction error. Simulation analysis is shown to suppniort the mathematical results.
Data governance: from master data quality to data monetization

The rapid increase in data generated by humans and machines has made companies aware of data’s strategic potential. With this changing role of data, enterprises have to effectively implement a set of data governance mechanisms to meet strategic goals and increase organizational performance. However, existing research on data governance tends to emphasize operational aspects and is less applicable to the strategic roles that data plays today. Based on a multiple case study involving companies with substantial data governance experience, we identify three data governance archetypes: (1) Improve master data quality, (2) Enable enterprise-wide data management, and (3) Coordinate the network to enable data monetization. Our findings advance the field of IT governance in general and contribute to data governance research by detailing structural, procedural, and relational governance mechanisms for different strategic contexts. For practitioners, our research provides insights into the priorities of data governance initiatives and outlines pathways to manage data as a strategic asset.
LoRaWAN energy optimization with security consideration

Long Range Wide Area Network (LoRaWAN) is an emerging wireless technology that is expected to be widely deployed and implemented in several applications, especially with the promising widespread use of the Internet of Things (IoT) and its potential applications within the Fifth Generation (5G) communication technology. LoRaWAN consists of a number of nodes that monitors and senses the environment to collect specific data, and then sends the collected data to a remote monitoring device for further processing and decision-making. Energy consumption and security assurance are two vital factors needed to be optimized to ensure an efficient and reliable network operation and performance. To achieve that, each of LoRaWAN nodes can be configured by five transmission parameters, which are the spreading factor, carrier frequency, bandwidth, coding rate and transmission power. Choosing the best values of these parameters leads to enhancing the network deployment. In this paper, we shed the light to the security aspect in LoRaWAN network. Then, we introduced an algorithm that depends on the reinforcement learning approach to enable each node in the network to choose the best values of spreading factor and transmission power such that it leads to a lower energy consumption and higher packets’ delivery rate. The results of the simulation experiments of our proposed technique showed a valuable increase in the packet reception rate at the gateway and a significant decrease in the total consumed energy at the end nodes compared with the most related work in literature
How Government Regulations and Consumer Behavior Influence Manufacturers' Product Green Degree Decision-Making: An Agent-Based Model

In recent years, green product issues have received increasing attention. Both government regulations and consumer behaviors have a strong influence on the product green degree decisions of manufacturers’ products. In order to find how government regulations and individual’s green product purchase behavior affect manufacturers’ green degree decisions and the market evolution characteristics, this paper proposes a multiagent model that considers the interaction among government, consumers, and manufacturers. The simulation results show that, firstly, the product green degree decision-making of manufacturers needs the guidance and regulation of the government. Secondly, product price subsidies are the most effective way to affect the manufacturers’ product green degree decisions. In contrast to giving green cost subsidies to manufacturers, the government employs various publicity means to improve the environmental awareness of consumers is also an effective way to enhance the green degree of manufacturers’ products. Thirdly, there is a “Crowding Out Effect” on the other qualities of manufacturers’ products when manufacturers focus on the green degree of their products.
Dynamic Capabilities & Digital Transformation: A quantitative study on how to gain a Competitive Advantage in the Digital Age

Digital transformation is establishing itself as a strategic imperative for firms to stay competitive in a digitally disrupted and constantly changing business world. Prior research indicates the connection between a firm’s responsiveness to external changes - its dynamic capabilities - and its digital maturity, both enhancing its competitive advantage. This study empirically investigates this relationship by comparing 51 German utility firms via an online survey sent to C-level representatives involved in digital transformation activities. The results show a significant positive effect of dynamic capabilities on digital maturity, and of both concepts on competitive advantage, with digital maturity mediating the performance effects of dynamic capabilities. The study contributes to the body of empirical research on digital transformation by emphasizing the importance of dynamic capabilities and digital maturity for firms seeking to increase their competitive advantage.
Research on the Effect of Knowledge Network Embedding on the Dynamic Capabilities of Small and Micro Enterprises

In the complex and dynamic economic environment, the growing pain of small and micro enterprises is long-standing. It is urgent to strengthen the research on the endogenous growth mechanism of small and micro enterprises. Based on the background of the era of knowledge-driven economy, this paper explores the relationship between knowledge network embeddedness and dynamic capabilities of small and micro enterprises with environmental munificence as the regulating variable. We have the structural equation empirical research with the data from 260 questionnaires of small and micro enterprises. The results show that structural embeddedness and relational embeddedness have a positive driving effect on the dynamic capability of small and micro enterprises, and environmental munificence plays a positive regulatory role in the positive impact of knowledge network embedding on the dynamic capability. The research conclusion is helpful for the small and micro enterprise to develop dynamic capacity and for the supportive policy making as well.
Automated Mobility as a Service - Development of a Hierarchical Quality Scale

Despite the broad interest in shared and automated mobility and the need to understand customer expectations to guide further developments, no instrument exists that measures perceived service quality for automated mobility as a service (AMaaS). Using data from 23 exploratory interviews and three empirical surveys with a total of 1431 participants, this study develops and validates a hierarchical, multi-dimensional quality scale. After identifying relevant quality constructs, we purify the initial item set until satisfactory psychometric properties are achieved. Eventually, we identify two primary quality dimensions (outcome quality and trust) and seven significant quality attributes (time efficiency, environmental impact, convenience, flexibility, cybersecurity, privacy, and traffic safety). We confirm the nomological validity to strengthen the scale's predictability further. Our work supports practitioners in developing high-quality customer-oriented mobility offerings and researchers in building stronger theories utilizing thoroughly validated constructs and a measure that predicts customer perceptions reliably.
Dantzig-Wolfe Decomposition for Cost Partitioning

Optimal cost partitioning can produce high quality heuristic estimates even from small abstractions. It can be computed with a linear program (LP) but the size of this LP often makes this impractical. Recent work used Lagrangian decomposition to speed up the computation. Here we use a different decomposition technique called Dantzig-Wolfe decomposition to tackle the problem. This gives new insights into optimal cost partitioning and has several advantages over Lagrangian decomposition: our method detects when a cost partition is optimal; it can deal with general cost functions; and it does not consider abstractions in the linear program that do not contribute to the heuristic value. We also show the advantage of the method empirically and investigate several improvements that are useful for all cost partitioning methods.
A Web-Crawling based Study on Servitization: Analysis of Service Offerings by German Manufacturers of Machinery and Equipment

The machinery and equipment industry is, next to the automotive industry, the sector with the highest turnover in Germany. Here, servitization is a crucial contribution to remain competitive. Nevertheless, the diversity of this industry is not sufficiently taken into account in the scientific community. To overcome this gap, this study utilizes a web-crawling based approach to analyze service offerings of German manufacturers, linking key company figures to investigate potential correlations. This contribution differs from conventional studies using databases that hardly contain any detailed service information of smaller manufacturers. The proposed approach derives a clearer picture of the current service portfolio, considering all company sizes and its degree of servitization, enabling comparisons between micro-, small-, mediumand large-sized manufacturers. In context of digital servitization, the study allows further conclusions about the range of so-called basic services or advanced services and how these are related to key company figures.
Transfer Learning to enable Flexibility of Demand Forecasting: Dropout Profiles to protect from Negative Transfer

One of the largest investments of a company is its implementation of an enterprise system. Sometimes, companies, and thus their enterprise systems, face changes that were not foreseen at the time the system was implemented. One example can be found in the COVID-19 pandemic in which retailers are facing the huge problem that current forecasting models no longer produce accurate forecasts and changes in feature spaces are needed. In this paper transfer learning and a proposed method, called dropout profiles, are used to react faster to changes in feature space. This method is implemented in a case study with changes in the feature space of a demand forecast. It is shown that the proposed method performs significantly better than traditional methods. In practice, this means that neural networks can expect a longer life cycle and companies can react faster to changes in the market.
Interventions to Build Business Process Analyst Competencies- A Kenyan Study

Economic growth in Africa has highlighted the need for organizations to have well-defined business processes. This has given rise to the need for the business process analyst (BPA) role. BPAs are essential to driving business process initiatives and enterprise systems implementations. Kenya aims to be a global hub for business process outsourcing which will be significantly enhanced by a workforce skilled in business process. Given this need, this qualitative study describes the role of process initiatives and the BPA in organizations in Kenya. This study then describes how BPA competencies are being built. We established that inter-group collaboration, on-the-job/experiential learning and vendor certifications were impactful interventions for competency building in BPAs. A practical contribution of this study is a better explanation of BPA competency building and how it can be improved within the Kenyan context.
Self-Supervised Learning of Long-Horizon Manipulation Tasks with Finite-State Task Machines

We consider the problem of a robot learning to manipulate unknown objects while using them to perform a complex task that is composed of several sub-tasks. The robot receives 6D poses of the objects along with their semantic labels, and executes nonprehensile actions on them. The robot does not receive any feedback regarding the task until the end of an episode, where a binary reward indicates success or failure in performing the task. Moreover, certain attributes of objects cannot be always observed, so the robot needs to learn to remember pertinent past actions that it executed. We propose to solve this problem by simultaneously learning a low-level control policy and a high-level ﬁnite-state task machine that keeps track of the progress made by the robot in solving the various sub-tasks and guides the low-level policy. Several experiments in simulation clearly show that the proposed approach is efﬁcient at solving complex robotic tasks without any supervision.
Digital transformation of a traditional business model: A case study from the maritime industry

Research on the digital transformation of business models has shown how digital technologies change the creation, capture, promotion, and delivery of value. However, fewer studies empirically investigate how these elements change over time, especially in established companies. This paper reports longitudinal findings from an empirical case study of digital transformation in the maritime industry. The case company uses digital technology to change its traditional business of scheduling and conducting maritime inspection. The results show how changes in product strategy, revenue logic, distribution model, and service and implementation model introduce organizational tensions and how the company seeks to overcome them. We contribute by discussing how the transformation occurs in all business model elements simultaneously and highlighting an increased role of customer behavior management for value co-creation.
Understanding the Invitation Acceptance in Agent-initiated Social E-commerce

Recent years have witnessed the phenomenal success of a new form of social e-commerce platforms, which transforms users into agents by motivating them with monetary rewards to promote products and invite new agents through their social network. Despite their rapid growth, there is still inadequate evidence on how such agent invitation works. This research examines what factors affect the agent invitation process. We first conduct a qualitative user study, where we identify four potential mechanisms related to the agent invitation: social conformity, social enrichment, refusal avoidance, and benefit-cost trade-off. Leveraging the empirical data collected from one of the largest social e-commerce platforms in China - Beidian, we operationalize a set of behavioral indicators of these mechanisms and further develop machine learning models to predict users' reactions to invitations. We found that the identified four mechanisms contribute to the high success rate of agent invitations differently. We conclude by discussing the implications of our findings and their potential benefits to real-world applications.
Yellow pigments based on lead, tin, and antimony: Ancient recipes, synthesis, characterization, and hue choice in artworks

The aim of this study is to investigate the yellow pigments based on lead, tin, and antimony produced according to the ancient treatises, recipe books, and literary sources. The pigments considered in this study are lead/tin yellow type I (Pb2SnO4), lead/tin yellow type II (PbSn1−xSixO3), lead antimonate or Naples yellow (Pb2Sb2O7), and lead/tin/antimony yellow (Pb2SnSbO6,5). Color was measured by X-Rite reflectance spectrophotometer to investigate the chromatic characteristics of the produced pigments in relation to the recipes. The pigments were chemically characterized by scanning electron microscopy with energy-dispersive spectroscopy, and micro-Raman analysis to investigate the influence of reagents, temperature, and melting modalities in the compositions of the produced powders. A part of the study was devoted to the study on the use of these yellow pigments in the art history especially concerning the modus pingendi of Italian artists between 14th and 18th century. The results of this study highlighted the possibility to obtain pigments with different colors and compositions according to the reagents, to the temperature, to the melting modality, in particular if single or double firing was used. © 2016 Wiley Periodicals, Inc. Col Res Appl, 41, 226–231, 2016