Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdran, José R Buendía Rubio, Gérôme Bovet, Gregorio Martínez Pérez, Robust Federated Learning for execution time-based device model identification under label-flipping attack, Cluster Computing, Vol. 27 (1), 2024. (Journal Article)
The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriate for scenarios where data privacy and protection are a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated using several aggregation mechanisms as countermeasures. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%. |
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Tobias Schultheiss, Uschi Backes-Gellner, Does updating education curricula accelerate technology adoption in the workplace? Evidence from dual vocational education and training curricula in Switzerland, Journal of Technology Transfer, Vol. 49 (1), 2024. (Journal Article)
In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new technologies faster into firms’ workplaces. We study technology changes and curriculum updates from an early wave of digitalization (i.e., computer-numerically controlled machinery, computer-aided design, and desktop publishing software). We take a text-as-data approach and tap into two novel data sources to measure change in educational content and the use of technology at the workplace: first, vocational education curricula and, second, firms’ job advertisements. To examine the causal effects of adding new technology skills to curricula on the diffusion of these technologies in firms’ workplaces (measured by job advertisements), we use an event study design. Our results show that curriculum updates substantially shorten the time it takes for new technologies to arrive in firms’ workplaces, especially for mainstream firms. |
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Daniel Fasnacht, Christian Straube, Quantum computing as an enabling technology for the next business cycle, HMD Praxis der Wirtschaftsinformatik, Vol. 61 (1), 2024. (Journal Article)
We need more computing capacity for the next growth cycle, and computers with conventional transistor technology are reaching their limits. So new ideas are required. The quantum computer, which overcomes the binary system and is not based on silicon microchips, could be a solution. This technology will continue to develop exponentially and transform science, the economy, and society. Furthermore, the paradigm of quantum communication offers an entirely novel possibility of distributed computing by allowing quantum computers to be networked via quantum channels to intrinsically secure communication. This article explains how quantum computers exploit new phenomena that do not occur in classical physics. Along the four primary application areas identified (optimization, simulation, machine learning, and cryptography), we describe possible applications in various industries. Our critical appraisal presents the technical challenges that still hold the potential for quantum computing to complement traditional computing systems. Accordingly, small and mid-sized companies do not necessarily need to invest in quantum computers but in their use. Quantum as a service can be the first step for visionary leaders to get familiar with it and gain a competitive advantage early on. |
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Alin Marius Andrieş, Alexandra Maria Chiper, Steven Ongena, Nicu Sprincean, External wealth of nations and systemic risk, Journal of Financial Stability, Vol. 70, 2024. (Journal Article)
External imbalances played a pivotal role leading to the global financial crisis and were an important cause of turmoil. While current account (flow) imbalances narrowed in the aftermath of the crisis, the net international investment position (NIIP) (stock) imbalances persisted. This study explores the implications of countries’ net foreign positions on systemic risk. Using a sample of 470 banks located in 49 advanced economies, emerging countries, and developing economies over 2000–2020, we find robust empirical evidence that banks can reduce their systemic risk exposure when the countries in which they are incorporated improve their NIIPs and maintain creditor status vis-à-vis the rest of the world. However, only the equity component of the NIIP is responsible for this outcome, whereas debt flows are not significant. Similarly, we find that the mitigating effect of an external balance sheet on systemic risk is derived from valuation gains rather than from the incremental net acquisition of assets or liabilities represented by the current account. Our findings are particularly relevant for policymakers seeking to improve banks’ resilience to adverse shocks and maintain financial stability. |
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Mahmoud Fatouh, Simone Giansante, Steven Ongena, Leverage ratio, risk-based capital requirements, and risk-taking in the United Kingdom, Financial markets, institutions & instruments, Vol. 33 (1), 2024. (Journal Article)
We assess the impact of the leverage ratio capital requirements on the risk-taking behaviour of banks both theoretically and empirically. Conceptually, introducing binding leverage ratio requirements into a regulatory framework with risk-based capital requirements induces banks to re-optimise, shifting from safer to riskier assets (higher asset risk). Yet, this shift would not be one-for-one due to risk weight differences, meaning the shift would be associated with a lower level of leverage (lower insolvency risk). The interaction of these two changes determines the impact on the aggregate level of risk. Empirically, we use a difference-in-differences setup to compare the behaviour of UK banks subject to the leverage ratio requirements (LR banks) to otherwise similar banks (non-LR banks). Our results show that LR banks did not increase asset risk, and slightly reduced leverage levels, compared to the control group after the introduction of leverage ratio in the UK. As expected, these two changes led to a lower aggregate level of risk. Emperical results indicate that credit default swap spreads on the 5-year subordinated debt of LR banks decreased relative to non-LR banks post leverage ratio introduction, suggesting the market viewed LR banks as less risky, especially during the COVID 19 stress. |
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Fadong Chen, Zhi Zhu, Qiang Shen, Ian Krajbich, Todd Anthony Hare, Intrachoice dynamics shape social decisions, Management Science, Vol. 70 (2), 2024. (Journal Article)
Do people have well-defined social preferences waiting to be applied when making decisions? Or do they have to construct social decisions on the spot? If the latter, how are those decisions influenced by the way in which information is acquired and evaluated? These temporal dynamics are fundamental to understanding how people trade off selfishness and prosociality in organizations and societies. Here, we investigate how the temporal dynamics of the choice process shape social decisions in three studies using response times and mouse tracking. In the first study, participants made binary decisions in mini-dictator games with and without time constraints. Using mouse trajectories and a starting time drift diffusion model, we find that, regardless of time constraints, selfish participants were delayed in processing others’ payoffs, whereas the opposite was true for prosocial participants. The independent mouse trajectory and computational modeling analyses identified consistent measures of the delay between considering one’s own and others’ payoffs (self-onset delay, SOD). This measure correlated with individual differences in prosociality and predicted heterogeneous effects of time constraints on preferences. We confirmed these results in two additional studies, one a purely behavioral study in which participants made decisions by pressing computer keys, and the other a replication of the mouse-tracking study. Together, these results indicate that people preferentially process either self or others’ payoffs early in the choice process. The intrachoice dynamics are crucial in shaping social preferences and might be manipulated via nudge policies (e.g., manipulating the display order or saliency of self and others’ outcomes) for behavior in managerial or other contexts. |
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Antonello Cirulli, Michal Kobak, Urban Ulrych, Portfolio Construction with Hierarchical Momentum, The Journal of Portfolio Management, Vol. 50 (4), 2024. (Journal Article)
This article presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On one hand, investing in high-momentum stocks enhances returns by capturing the momentum premium. On the other hand, hierarchical clustering of a high-dimensional asset universe ensures sparse diversification, stabilizes the portfolio across economic regimes, and mitigates the problem of increased drawdowns typically present in momentum portfolios. Moreover, the proposed portfolio construction approach avoids the covariance matrix inversion. An out-of-sample backtest on a non-survivorship-biased dataset of international stocks shows that, compared to the model-based and model-free benchmarks, hierarchical momentum portfolios achieve improved cumulative and risk-adjusted portfolio returns as well as decreased portfolio drawdowns net of transaction costs. The study further suggests that the unique characteristics of the hierarchical momentum portfolios arise because of both dimensionality reduction via clustering and momentum-based stock selection. |
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Maximilian D Gilger, Lydia Hellrung, Philipp T Neukam, Nils B Kroemer, Stephan Nebe, Shakoor Pooseh, Yacila I Deza-Lougovski, Michael N Smolka, Arbitration between model-free and model-based control is not affected by transient changes in tonic serotonin levels, Journal of Psychopharmacology, Vol. 38 (2), 2024. (Journal Article)
Background: Serotonin has been suggested to modulate decision-making by influencing the arbitration between model-based and model-free control. Disruptions in these control mechanisms are involved in mental disorders such as drug dependence or obsessive-compulsive disorder. While previous reports indicate that lower brain serotonin levels reduce model-based control, it remains unknown whether increases in serotonergic availability might thus increase model-based control. Moreover, the mediating neural mechanisms have not been studied yet. Aim: The first aim of this study was to investigate whether increased/decreased tonic serotonin levels affect the arbitration between model-free and model-based control. Second, we aimed to identify the underlying neural processes. Methods: We employed a sequential two-stage Markov decision-task and measured brain responses during functional magnetic resonance imaging in 98 participants in a randomized, double-blind cross-over within-subject design. To investigate the influence of serotonin on the balance between model-free and model-based control, we used a tryptophan intervention with three intervention levels (loading, balanced, depletion). We hypothesized that model-based behaviour would increase with higher serotonin levels. Results: We found evidence that neither model-free nor model-based control were affected by changes in tonic serotonin levels. Furthermore, our tryptophan intervention did not elicit relevant changes in Blood-Oxygenation-Level Dependent activity. |
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Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdran, Gérôme Bovet, Gregorio Martínez Pérez, Single-board device individual authentication based on hardware performance and autoencoder transformer models, Computers and Security, Vol. 137, 2024. (Journal Article)
The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74±0.13 and an average maximum False Positive Rate (FPR) of 0.06±0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications. |
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Reto Eberle, Alexandra Allgaier, Andreas Buchs, FER-Leitfaden: Nachhaltigkeitsmanagement und -berichterstattung bei KMU, Expert Focus, Vol. 98 (1), 2024. (Journal Article)
Die FER-Fachkommission hat am 5. Dezember 2023 der Veröffentlichung eines Diskussionspapiers über die Nachhaltigkeit in der FER zugestimmt. Dieses Papier enthält einen Leitfaden, der KMU in sieben Schritten darin unterstützt, Nachhaltigkeit in der Organisation zu verankern und transparent darüber zu berichten. Die Öffentlichkeit ist bis am 14. April 2024 zur Kommentierung eingeladen. |
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Beibei Han, Yingmei Wei, Qingyong Wang, Francesco Maria De Collibus, Claudio Tessone, MT²AD: multi-layer temporal transaction anomaly detection in ethereum networks with GNN, Complex & Intelligent Systems, Vol. 10 (1), 2024. (Journal Article)
In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT$^2$AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT$^2$AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT$^2$AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated. |
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Thomas Puschmann, Valentyn Khmarskyi, Green fintech: developing a research agenda, CSR, Sustainability, Ethics & Governance, 2024. (Journal Article)
Digitalization and sustainability have been the core drivers of transformation of the financial industry in recent years. In this context, green fintech plays a major role, which, however, is still an unexplored field in business, information systems and finance research. This paper conducts a systematic literature analysis and develops a research agenda based on a framework, which is derived from clustering 74 academic research papers. The framework consists of the four clusters strategy, organization, technology, and potentials along nine dimensions. The research agenda reveals that green fintech is still a very premature field of research. The analysis shows that areas like customer- and government-related services, insurance-oriented approaches and SDGs which focus on life on land and life below water are still rare and that most of the approaches focus on blockchain technology, while other financial technologies like artificial intelligence are still underrepresented. |
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Julian Kölbel, Markus Leippold, Jordy Rillaerts, Qian Wang, Ask BERT: How Regulatory Disclosure of Transition and Physical Climate Risks affects the CDS Term Structure, Journal of Financial Econometrics, Vol. 22 (1), 2024. (Journal Article)
We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant. |
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Daniel Fasnacht, Virtuelle Konsumwelten –Trends mit Risiken: Gastkommentar, In: Neue Zürcher Zeitung, p. 21, 19 January 2024. (Newspaper Article)
Aus Asien kommt der Trend Social Commerce, der diverse Branchen und disruptive Technologien verbindet und so ein neues Kundenerlebnis schafft. Was bedeutet dieses Phänomen, und sind wir bereit dafür? |
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Giulio Cornelli, Sebastian Klaus Dörr, Leonardo Gambacorta, Ouarda Merrouche, Regulatory Sandboxes and Fintech Funding: Evidence from the UK, Review of Finance, Vol. 28 (1), 2024. (Journal Article)
Over fifty countries have introduced regulatory sandboxes to foster financial innovation. This article conducts the first evaluation of their ability to improve fintechs’ access to capital and attendant real effects. Exploiting the staggered introduction of the UK sandbox, we establish that firms entering the sandbox see an increase of 15% in capital raised post-entry. Their probability of raising capital increases by 50%. Sandbox entry also has a significant positive effect on survival rates and patenting. Investigating the mechanism, we present evidence consistent with lower asymmetric information and regulatory costs. |
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Marco Ceccarelli, Stefano Ramelli, Alexander Wagner, Low carbon mutual funds, Review of Finance, Vol. 28 (1), 2024. (Journal Article)
Climate change poses new challenges for portfolio management. In our not-yet-low carbon world, investors face a trade-off between minimizing their exposure to climate risks and maximizing the benefits of portfolio diversification. This paper investigates how investors and financial intermediaries navigate this trade-off. After the release of Morningstar's novel carbon risk metrics in April 2018, mutual funds labeled as "low carbon" experienced a significant increase in investor demand, especially those with high risk-adjusted returns. Fund managers actively reduced their exposure to firms with high carbon risk scores, especially stocks with returns that correlated more with the funds' portfolios and were thus less useful for diversification. These findings shed light on whether and how climate-related information can re-orient capital flows in a low carbon direction. |
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Daniel Fasnacht, Beyond the hype: sensitive data on the blockchain, CV Publishing AG, cryptovalleyjournal.com, https://cryptovalleyjournal.com/focus/background/beyond-the-hype-sensitive-data-on-the-blockchain/, 2024-01-15. (Scientific Publication In Electronic Form)
While the crypto market has experienced volatility and skepticism, the underlying blockchain technology has continually evolved since the introduction of Bitcoin in 2009. Though Bitcoin has doubled since last year, the focus has shifted to non-fungible tokens (NFTs) and infrastructure protocols like Chainlink and Graph. |
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Yifei Liu, Mathias Gehrig, Nico Messikommer, Marco Cannici, Davide Scaramuzza, Revisiting Token Pruning for Object Detection and Instance Segmentation, In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. (Conference or Workshop Paper published in Proceedings)
Vision Transformers (ViTs) have shown impressive performance in computer vision, but their high computational cost, quadratic in the number of tokens, limits their adoption in computation-constrained applications. However, this large number of tokens may not be necessary, as not all tokens are equally important. In this paper, we investigate token pruning to accelerate inference for object detection and instance segmentation, extending prior works from image classification. Through extensive experiments, we offer four insights for dense tasks: (i) tokens should not be completely pruned and discarded, but rather preserved in the feature maps for later use. (ii) reactivating previously pruned tokens can further enhance model performance. (iii) a dynamic pruning rate based on images is better than a fixed pruning rate. (iv) a lightweight, 2-layer MLP can effectively prune tokens, achieving accuracy comparable with complex gating networks with a simpler design. We evaluate the impact of these design choices on COCO dataset and present a method integrating these insights that outperforms prior art token pruning models, significantly reducing performance drop from ~1.5 mAP to ~0.3 mAP for both boxes and masks. Compared to the dense counterpart that uses all tokens, our method achieves up to 34% faster inference speed for the whole network and 46% for the backbone. |
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Christophe Viguerie, Raffaele Fabio Ciriello, Liudmila Zavolokina, Formative Archetypes in Enterprise Blockchain Governance: Exploring the Dynamics of Participant Dominance and Platform Openness, In: 57th Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences (HICSS), 2024-01-03. (Conference or Workshop Paper published in Proceedings)
It is widely assumed that blockchain should, in principle, lead to decentralization. Yet, in practice, many enterprise blockchains are highly centralized. To explain this conundrum, we conduct a multi-case study of four enterprise blockchains: Walmart DL Freight, Contour, Chronicled MediLedger, and Cardossier. Exploring the dynamics of participant dominance and platform openness during their formative stages, we theorize that these blockchains correspond to the distinct archetypes of Chief, Clan, Custodian, and Consortium, respectively. Importantly, these archetypes shape the subsequent evolution of the governance approach, thus explaining why and how enterprise blockchains with dominant participants and limited openness later exhibit more centralized governance. |
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Dzmitry Katsiuba, Mateusz Dolata, Gerhard Schwabe, Power of Language Automation: The Potential for Closing the Loop in Responding to Online Customer Feedback, In: Hawaii International Conference on System Sciences 2024 (HICSS-57), Hawaii International Conference on System Sciences (HICSS), 2024-01-03. (Conference or Workshop Paper published in Proceedings)
Online customer feedback management is playing an increasingly important role for businesses. Quickly providing guests with good responses to their reviews can be challenging, especially as the number of reviews increases. To address these challenges, this paper explores the response process and the potential for AI augmentation in the formulation and quality assurance of responses. As part of a design science research approach, it proposes an orchestration concept for humans and AI in intelligence co-writing in the hospitality industry and a novel NLP-based solution, which combines the advantages of human and AI in one application. The evaluation of the developed artifact shows that it is currently not possible to close the loop and automate the response process completely. This study describes the necessary components and provides transferable design knowledge. It opens possibilities for practical applications of NLP and further IS research. |
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