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- ItemLow-power emerging memristive designs towards secure hardware systems for applications in internet of things(Amsterdam : Elsevier, 2021) Du, Nan; Schmidt, Heidemarie; Polian, IliaEmerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.
- ItemAnthropomorphized artificial intelligence, attachment, and consumer behavior(Dordrecht [u.a.] : Springer, 2021) Hermann, ErikThe increasing humanization and emotional intelligence of AI applications have the potential to induce consumers’ attachment to AI and to transform human-to-AI interactions into human-to-human-like interactions. In turn, consumer behavior as well as consumers’ individual and social lives can be affected in various ways. Following this reasoning, I illustrate the implications and research opportunities related to consumers’ (potential) attachment to humanized AI applications along the stages of the consumption process.
- ItemArtificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems(Basel : MDPI, 2021) Asfahan, Hafiz M.; Sajjad, Uzair; Sultan, Muhammad; Hussain, Imtiyaz; Hamid, Khalid; Ali, Mubasher; Wang, Chi-Chuan; Shamshiri, Redmond R.; Khan, Muhammad UsmanThe present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Tdbout, wout, and Eairout). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.
- ItemLeveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective(Dordrecht : Springer, 2021) Hermann, ErikArtificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.
- ItemEthical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability(Dordrecht : Springer Netherlands, 2021) Hermann, Erik; Hermann, Gunter; Tremblay, Jean-ChristopheArtificial intelligence can be a game changer to address the global challenge of humanity-threatening climate change by fostering sustainable development. Since chemical research and development lay the foundation for innovative products and solutions, this study presents a novel chemical research and development process backed with artificial intelligence and guiding ethical principles to account for both process- and outcome-related sustainability. Particularly in ethically salient contexts, ethical principles have to accompany research and development powered by artificial intelligence to promote social and environmental good and sustainability (beneficence) while preventing any harm (non-maleficence) for all stakeholders (i.e., companies, individuals, society at large) affected.