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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Anusic, Daniel Hussain, Abid Centre for Business Data Analytics Dept. of Digitalization Copenhagen Business School Denmark Department of Technology Kristiania University College Norway

This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the research project which anticipates to build an automated sentiment analysis system for football clubs to detect and prevent a potential social media crisis at an early stage. The article illustrates a schematic of how state of the art machine learning algorithms, methods and techniques can be put together to provide a basis for automated data collection, classification, sentiment analysis and visual reporting. The article also presents that the time-consuming and costly process of human text labelling can be effectively replaced by lexicon-based sentiment analysis. This paper reports the complete process from data collection to visual reports to discuss challenges and opportunities associated with design and development of such artefacts. © 2023 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Privacy by Design: Proposing a Generic Framework for Privacy Compliant Experiment Repository Tool

Privacy by Design: Proposing a Generic Framework for Privacy...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Abid Hussain Lester Allan Lasrado Dept. of Digitalization Centre for Business Data Analytics Copenhagen Business School Denmark Department of Technology Kristiania University College Norway

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The regulations and guidelines for data privacy have become a significant challenge for both the industry and the research community. Collecting, storing, and processing data while complying with these regulations can be a daunting task. Privacy management during the experimental data collection process comes with additional costs that cannot be ignored. This article proposes a conceptual framework and an architect for a generic experiment data collection tool that handles privacy at runtime. The tool eliminates the need for data storage first and subsequent anonymization. Moreover, it aims to simplify the technical aspects of data privacy processes for researchers and provide user-friendly privacy scales for data anonymization.

关键词： data privacy Privacy Costs Runtime data collection Regulation Software

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Reinforcement learning for wind-farm flow control:Current state and future actions

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Theoretical & Applied Mechanics Letters 2023年第6期13卷 455-464页

作者： Mahdi Abkar Navid Zehtabiyan-Rezaie Alexandros Iosifidis Department of Mechanical and Production Engineering Aarhus UniversityAarhus N8200Denmark Department of Electrical and Computer Engineering Aarhus UniversityAarhus N8200Denmark Centre for Digitalization Big Dataand Data AnalyticsAarhus UniversityAarhus N8200Denmark

Wind-farm flow control stands at the forefront of grand challenges in wind-energy *** central issue is that current algorithms are based on simplified models and,thus,fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere *** learning(RL),as a subset of machine learning,has demonstrated its effectiveness in solving high-dimensional problems in various domains,and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow *** review has two main ***,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL *** examining the latest research in this area,the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL ***,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on *** highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.

关键词： Wind-farm flow control Turbine wakes Power losses Reinforcement learning Machine learning

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SELF-EMPLOYMENT AND MOTHERHOOD: LABOR MARKET OUTCOMES OF SELF-EMPLOYMENT IN EARLY CHILDHOOD YEARS 82

SELF-EMPLOYMENT AND MOTHERHOOD: LABOR MARKET OUTCOMES OF SEL...

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82nd Annual Meeting of the Academy of Management 2022: A Hybrid Experience, AOM 2022

作者： Kuenn, Annemarie Rybczynski, Kate Theunissen, Pomme Maastricht University P.O. Box 616 MaastrichtNL-6200 MD Netherlands Department of Economics University of Waterloo Canada Department of Finance Department of Data Analytics and Digitalization Maastricht University Netherlands

Self-employment is a career choice that may offer the flexibility to take care of children whilst remaining active on the labor market, and can therefore be seen by mothers as an alternative to wage employment during early childhood years. Using data from the German Socio-Economic household panel (SOEP) between 1995 and 2018, we investigate the success of this strategy, by studying wage earnings and the labor force status of mothers seven years after childbirth, once the child reaches school age. Taking account of self-selection into self-employment, we find that the consequences of self-employment experience in the early years after childbirth are no different from being inactive in terms of hourly wage for mothers who return to wage employment, while each additional month of wage employment after childbirth increases this wage by 0.4%. However, each additional month of self-employment experience does increase the odds of being active in the labor market by 22%, compared to inactivity. Analogously, for each additional month of wage employment, the odds of being employed increase by 6%. Self-employment in the early years after child birth thereby seems to keep mothers attached to the labor market to a greater degree than does wage-employment. © 2022, Academy of Management. All rights reserved.

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Constructing Bayesian optimal designs for discrete choice experiments by simulated annealing

arXiv

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arXiv 2024年

作者： Mao, Yicheng Kessels, Roselinde van der Zanden, Tom Department of Data Analytics and Digitalization Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands Department of Economics City Campus University of Antwerp Prinsstraat 13 Antwerp2000 Belgium

Discrete choice experiments (DCEs) investigate the attributes that influence individuals’ choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that utilize existing information about the attributes’ preferences. Given the nonlinear nature of choice models, the construction of an appropriate design requires efficient algorithms. Among these, the coordinate-exchange (CE) algorithm is commonly employed for constructing designs based on the MNL model. However, as a hill-climbing method, the CE algorithm tends to quickly converge to local optima, potentially limiting the quality of the resulting designs. We propose the use of a simulated annealing (SA) algorithm to construct Bayesian optimal designs. This algorithm accepts both superior and inferior solutions, avoiding premature convergence and allowing a more thorough exploration of potential solutions. Consequently, it ultimately obtains higher-quality choice designs compared to the CE algorithm. Our work represents the first application of an SA algorithm in constructing Bayesian optimal designs for DCEs. Through extensive computational experiments, we demonstrate that the SA designs generally outperform the CE designs in terms of statistical efficiency, especially when the prior preference information is highly uncertain. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulated annealing

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DeCrypt: a 3DES inspired optimised cryptographic algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4745-4755页

作者： Chowdhury, Deepraj Dey, Ajoy Garai, Ritam Adhikary, Subhrangshu Dwivedi, Ashutosh Dhar Ghosh, Uttam Alnumay, Waleed S. Department of Electronics and Communication Engineering IIIT Naya Raipur India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Dr. B.C. Roy Engineering College Durgapur India Centre for Business Data Analytics Department of Digitalization Copenhagen Business School Frederiksberg2000 Denmark Meharry Medical College 1005 Dr DB Todd Jr Blvd NashvilleTN37208 United States Riyadh Community College Computer Science Department King Saud University Riyadh Saudi Arabia

Triple data Encryption Standard (also known as 3DES) is a symmetric encryption algorithm. European Traffic Management System popularly uses 3DES for authentication and encryption. However, as per a draft published by NIST in 2018, 3DES is officially being retired and not suggested to use for new applications. Several attacks were imposed on 3DES, and the biggest threat to 3DES is meet in the middle attack. Therefore for long term security, it is essential to enhance the security of such algorithms. This paper proposed a new cipher DeCrypt inspired by 3DES, an improved version of the 3DES algorithm, and secured against meet in the middle attack. As per the experiment performed, DeCrypt cipher is 61 per cent faster than 3DES, providing long-term and better security against sweet-32 attacks than other symmetric algorithms. The proposed algorithm is also faster than 3DES due to reduced encryption and decryption time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection

DBNex: Deep Belief Network and Explainable AI based Financia...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Bhowmik, Abhimanyu Sannigrahi, Madhushree Chowdhury, Deepraj Dwivedi, Ashutosh Dhar Rao Mukkamala, Raghava Amity University Kolkata Department of Artificial Intelligence Kolkata India Iiit Naya Raipur Department of Ece Chhattisgarh India Copenhagen Business School Centre for Business Data Analytics Department of Digitalization Frederiksberg2000 Denmark Kristiania University College Department of Technology Kirkegata 24-26 Oslo0153 Norway

ISBN: (纸本)9781665480451

The majority of financial transactions are now conducted virtually around the world. The widespread use of credit cards and online transactions encourages fraudulent activity. Thus, one of the most demanding real-world challenges is fraud detection. Unbalanced datasets, in which there are a disproportionately high number of non-fraud samples compared to incidents of fraud, are one of the key obstacles to effective fraud detection. A further factor complicating the learning process for cutting-edge machine learning classifiers is how quickly fraud behaviour changes. Thus, in this study, we suggest an efficient fraud detection methodology. We propose a unique nonlinear embedded clustering to resolve imbalances in the dataset, followed by a Deep Belief Network for detecting fraudulent transactions. The proposed model achieved an accuracy of 94% with a 70:30 ratio of training-validation dataset. © 2022 IEEE.

关键词： Deep learning

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An Early Categorization of Prompt Injection Attacks on Large Language Models

arXiv

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arXiv 2024年

作者： Rossi, Sippo Michel, Alisia Marianne Mukkamala, Raghava Rao Thatcher, Jason Bennett Centre for Business Data Analytics Department of Digitalization Copenhagen Business School Howitzvej 60 Frederiksberg Copenhagen2000 Denmark Department of Management Information Systems Fox School of Business Temple University 1801 Liacouras Walk PhiladelphiaPA19122 United States

Large language models and AI chatbots have been at the forefront of democratizing artificial intelligence. However, the releases of ChatGPT and other similar tools have been followed by an increasing number of concerns regarding the difficulty of controlling large language models and their outputs. Currently, we are witnessing a cat-and-mouse game where users attempt to misuse the models with a novel attack called prompt injections while the developers attempt to discover the vulnerabilities and block the attacks simultaneously. In this paper, we provide an overview of this emergent threat and present a categorization of prompt injections, which can guide future research on prompt injections and act as a checklist of vulnerabilities in the development of LLM interfaces. Moreover, based on previous literature and our own empirical research, we discuss the implications of prompt injections to LLM end users, developers, and researchers. © 2024, CC BY.

关键词： Computational linguistics

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Machine-specific energy estimation using the unit process life cycle inventory (UPLCI) model

Machine-specific energy estimation using the unit process li...

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ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020

作者： Krogshave, Johan Thoft Boettjer, Till Ramanujan, Devarajan Mechanical Engineering Section Department of Engineering Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus C8000 Denmark

ISBN: (纸本)9780791883952

This paper discusses a method for machine-specific energy estimation in milling processes using the unit process life cycle inventory (UPLCI) model. To this end, we develop a standard methodology for constructing an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. The adjustment factors are calculated through experimental measurement of energy consumption for a standard test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study which experimentally measured the energy consumption for machining three parts made of Aluminum 6082 on a Chevalier QP2040-L three-axis vertical milling machine. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three parts and had significant estimation errors (314%, 499%, 286%). The largest sources of error in the UPLCI model were from overestimating the idle and basic power consumption of the machine tool. The adjusted UPLCI model significantly reduced the estimation errors for the same tests (27%, 0.3%, 36%). © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Energy utilization

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Hurricane-Induced Power Disruptions: Household Preferences for Improving Infrastructure Resilience

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Economics of Disasters and Climate Change 2024年第2期8卷 235-261页

作者： Asadi, Mehrnoosh Price, James I. Kessels, Roselinde Mozumder, Pallab Department of Economics College of Saint Benedict/Saint John’s University Saint Joseph USA School of Freshwater Sciences University of Wisconsin-Milwaukee Milwaukee USA Department of Data Analytics and Digitalization Maastricht University Maastricht The Netherlands Department of Economics City Campus University of Antwerp Antwerp Belgium Department of Earth & Environment Department of Economics and Institute of Environment Florida International University (FIU) University Park Campus FIU Miami USA

In recent years, the increase in the frequency and intensity of hurricanes has posed a significant threat to coastal infrastructures, particularly the electricity supply system. In response to these challenges, several policies have been proposed to improve the resilience of electricity systems, specifically focusing on expediting the restoration of disrupted utilities. However, implementing these resilience plans comes with considerable costs, which must be balanced against the potential benefits experienced by households. This study examines the willingness of Florida residents to financially support the improvement of the electricity infrastructure resilience in response to hurricanes in Florida. We conduct a Discrete Choice Experiment involving 1138 Floridians to assess their willingness to pay for different scenarios aimed at improving electricity system resilience. Three panel mixed logit models are estimated, accounting for preference heterogeneity. Results indicate that the annual welfare estimates per individual range from $525.51 to $604.70 across the restoration scenarios. The findings offer compelling evidence, indicating strong support for minimizing hurricane-induced power disruptions by implementing the proposed resilience programs in Florida.

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