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1. Predicting A Paradigm Shift: Exploring the Relationship Between Cognitive Style And The Paradigm-Relatedness Of Design Solutions

   Cole, Courtney; Marhefka, Jacqueline; Jablokow, Kathryn; Mohammed, Susan; Ritter, Sarah; Miller, Scarlett (January 2021, Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   null (Ed.)

   Nearly 60 years ago, Thomas Kuhn revolutionized how we think of scientific discovery and innovation when he identified that scientific change can occur in incremental developments that improve upon existing solutions, or it can occur as drastic change in the form of a paradigm shift. In engineering design, both types of scientific change are critical when exploring the solution space. However, most methods of examining design outputs look at whether an idea is creative or not and not the type of creativity that is deployed or if we can predict what types of individuals or teams is more likely to develop a paradigm-shifting idea. Without knowing how to identify who will generate ideas that fit a certain paradigm, we do not know how to build teams that can develop ideas that better explore the solution space. This study provides the first attempt at answering this question through an empirical study with 60 engineering design student teams over the course of a 4- and 8-week design project. Specifically, we sought to identify the role of cognitive style using KAI score, derived from Kirton’s Adaption-Innovation (A-I) theory, on the paradigm-relatedness of ideas generated by individuals and teams. We also sought to investigate the role of crowdsourcing for measuring the paradigm-relatedness of design solutions. The results showed that KAI was positively related to a greater likelihood of an individual’s idea being categorized as paradigm-breaking. In addition, the team KAI diversity was also linked to a greater likelihood of teams’ ideas being categorized as paradigm-challenging. Finally, the results support the use of crowdsourcing for measuring the paradigm-relatedness of design solutions. 

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2. Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

   https://doi.org/10.1002/spe.3233  

   Denninnart, Chavit; Chanikaphon, Thanawat; Amini Salehi, Mohsen (October 2023, Software: Practice and Experience)

   Summary Serverless computing along with Function‐as‐a‐Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single‐purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems—both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient. 

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3. On the Complexity and Typology of Inflectional Morphological Systems

   https://doi.org/10.1162/tacl_a_00271  

   Cotterell, Ryan; Kirov, Christo; Hulden, Mans; Eisner, Jason (November 2019, Transactions of the Association for Computational Linguistics)

   We quantify the linguistic complexity of different languages’ morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language’s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm— how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages. 

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4. The need to implement FAIR principles in biomolecular simulations

   https://doi.org/10.1038/s41592-025-02635-0  

   Amaro, Rommie E; Åqvist, Johan; Bahar, Ivet; Battistini, Federica; Bellaiche, Adam; Beltran, Daniel; Biggin, Philip C; Bonomi, Massimiliano; Bowman, Gregory R; Bryce, Richard A; et al (April 2025, Nature Methods)

   In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm. 

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5. Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

   https://doi.org/10.1109/TE.2023.3337337  

   Wei-Kocsis, Jin; Sabounchi, Moein; Mendis, Gihan J.; Fernando, Praveen; Yang, Baijian; Zhang, Tonglin (December 2023, IEEE Transactions on Education)

   Contribution: A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to motivate the students for exploring AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects and 2) a proactive education paradigm designed with the use of collaborative learning activities based on game-based learning and social constructivism. Background: Increasing evidence shows that AI techniques can be manipulated, evaded, and misled, which can result in new and profound security implications. There is an education and training gap to foster a qualified cyber-workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. Efforts have been made to incorporate a comprehensive curriculum to meet the demand. There still remain essential challenges for effectively educating students on the interaction of AI and cybersecurity. Intended Outcomes: A novel proactive and collaborative learning paradigm is proposed to educate and train a qualified cyber-workforce in this new era where security breaches, privacy violations, and AI have become commonplace. Application Design: The development of this learning paradigm is grounded in the pedagogical approaches of technology-mediated learning and social constructivism. Findings: Although the research work is still ongoing, the prototype learning paradigm has shown encouraging results in promoting the learners’ engagement in applied AI learning. 

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