Motives and profiles of ICO investors

in Journal of Business Research (2021), 125(3), 564-576

Research on initial coin offerings (ICOs) is nascent and assesses ICOs from the perspectives of ventures and regulators. Little is known about the equally important group of investors who provide their ... [more ▼]

Research on initial coin offerings (ICOs) is nascent and assesses ICOs from the perspectives of ventures and regulators. Little is known about the equally important group of investors who provide their capital to ventures in ICOs. Using a primary dataset of 517 ICO investors, we identify and categorize the motivations to invest in ICOs using factor analysis. We find that investors are driven by ideological, technological, and financial motives. Regarding the relative importance of the motives, we find that technological motives are the most important motives to ICO investors, followed by financial and ideological motives. To further profile investors, we conduct a regression analysis to distinguish investors across different motives. For example, we show significant differences across motives with regard to investors' risk perception, sources of information, and demand for strict regulation. The implications of this study for both theory and practice are considerable. [less ▲]

Bankruptcy regulation and self-employment entry: the moderating roles of income share, parenthood, and hybrid entrepreneurship

in Entrepreneurship: Theory and Practice (2021), 45(6), 1522-1549

We investigate how individual factors moderate the impact of bankruptcy exemption levels—that is, the amount of wealth individuals can keep in case of bankruptcy—on entry into self-employment ... [more ▼]

We investigate how individual factors moderate the impact of bankruptcy exemption levels—that is, the amount of wealth individuals can keep in case of bankruptcy—on entry into self-employment. Conceptually, we combine Prospect Theory’s axiom of diminishing sensitivity with insights from research on entrepreneurial failure. We hypothesize that individuals who face higher financial, social, or psychological costs because of bankruptcy will be less sensitive to higher exemption levels than will those who face lower costs across these dimensions. Our empirical results, which are based on a quasi-natural experiment in the United States, support our theoretical predictions. [less ▲]

A comprehensive review of the global development of initial coin offerings (ICOs) and their regulation

in Journal of Business Venturing Insights (2021), 15(6), 00213

Initial coin offerings (ICOs) represent an innovative and new funding mechanism for new technology ventures. In our comprehensive review of the industry’s evolution, we show that despite its short history ... [more ▼]

Initial coin offerings (ICOs) represent an innovative and new funding mechanism for new technology ventures. In our comprehensive review of the industry’s evolution, we show that despite its short history, there have been dramatic changes and shifts in the number of ICOs, the amount of money raised, the geographic distribution of ICOs, and their regulation. This dynamism calls into question current research practices and findings. We propose that scholars sort out and differentiate supply of vs. demand for ICO funding, taking geography and regulation into account with a global perspective. [less ▲]

How does entrepreneurial failure change an entrepreneur’s digital identity? Evidence from Twitter data

in Journal of Business Venturing (2021), 36(1), 106015

We assess whether and how entrepreneurs' digital identities change in response to entrepreneurial failure based on a sample of 760 entrepreneurs who experienced failure. We analyze a longitudinal dataset ... [more ▼]

We assess whether and how entrepreneurs' digital identities change in response to entrepreneurial failure based on a sample of 760 entrepreneurs who experienced failure. We analyze a longitudinal dataset of Twitter messages before, during, and after a business failure with a language-based method of computerized text analysis. The results of our explorative research indicate that the financial, social, and psychological consequences of failure are reflected in entrepreneurs' Tweets and lead to changes in their digital identities. Among others, entrepreneurs' language decreases in emotional tone and indicates increased psychological distress. Simultaneously, we observe higher levels of self-assurance and reflection after failure. We conclude by outlining the potential of using Twitter-generated digital footprints in future entrepreneurship research. [less ▲]

Onion Routing with Replies

in Advances in Cryptology - ASIACRYPT 2021 - 27th International Conference on the Theory and Application of Cryptology and Information Security Singapore, December 6-10, 2021, Proceedings, Part II (2021)

Distributed Prediction of Unsafe Reconfiguration Scenarios of Modular Robotic Programmable Matter

in IEEE Transactions on Robotics (2021), 37(6), 2226-2233

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own ... [more ▼]

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks. © 2004-2012 IEEE. [less ▲]

Incremental learning strategies for credit cards fraud detection.

in nternational Journal of Data Science and Analytics (2021)

very second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection ... [more ▼]

very second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quickly become outdated and require sensitive data storage. The evolving nature of bank fraud enshrines the importance of having up-to-date models, and sensitive data retention makes companies vulnerable to infringements of the European General Data Protection Regulation. For these reasons, evaluating incremental learning strategies is recommended. This paper designs and evaluates incremental learning solutions for real-world fraud detection systems. The aim is to demonstrate the competitiveness of incremental learning over conventional batch approaches and, consequently, improve its accuracy employing ensemble learning, diversity and transfer learning. An experimental analysis is conducted on a full-scale case study including five months of e-commerce transactions and made available by our industry partner, Worldline. [less ▲]

Transfer Learning Strategies for Credit Card Fraud Detection.

in IEEE Access (2021)

Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect ... [more ▼]

Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with short reaction time and high precision. Nevertheless, the heterogeneous nature of the fraud behavior makes it difficult to tailor existing systems to different contexts (e.g. new payment systems, different countries and/or population segments). Given the high cost (research, prototype development, and implementation in production) of designing data-driven FDSs, it is crucial for transactional companies to define procedures able to adapt existing pipelines to new challenges. From an AI/machine learning perspective, this is known as the problem of transfer learning. This paper discusses the design and implementation of transfer learning approaches for e-commerce credit card fraud detection and their assessment in a real setting. The case study, based on a six-month dataset (more than 200 million e-commerce transactions) provided by the industrial partner, relates to the transfer of detection models developed for a European country to another country. In particular, we present and discuss 15 transfer learning techniques (ranging from naive baselines to state-of-the-art and new approaches), making a critical and quantitative comparison in terms of precision for different transfer scenarios. Our contributions are twofold: (i) we show that the accuracy of many transfer methods is strongly dependent on the number of labeled samples in the target domain and (ii) we propose an ensemble solution to this problem based on self-supervised and semi-supervised domain adaptation classifiers. The thorough experimental assessment shows that this solution is both highly accurate and hardly sensitive to the number of labeled samples. [less ▲]

Uplink Power Control in Massive MIMO with Double Scattering Channels

in IEEE Transactions on Wireless Communications (2021)

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works ... [more ▼]

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion. [less ▲]

A Trusted Hybrid Learning Approach to Secure Edge Computing

in IEEE Consumer Electronics Magazine (2021)

Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks ... [more ▼]

Securing edge computing has drawn much attention due to the vital role of edge computing in Fifth Generation (5G) wireless networks. Artificial Intelligence (AI) has been adopted to protect networks against attackers targeting the connected edge devices or the wireless channel. However, the proposed detection mechanisms could generate a high false detection rate, especially against unknown attacks defined as zero-day threats. Thereby, we propose and conceive a new hybrid learning security framework that combines the expertise of security experts and the strength of machine learning to protect the edge computing network from known and unknown attacks, while minimizing the false detection rate. Moreover, to further decrease the number of false detections, a cyber security mechanism based on a Stackelberg game is used by the hybrid learning security engine (activated at each edge server) to assess the detection decisions provided by the neighboring security engines. [less ▲]