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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)

   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 investigatemore »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.« less
2. Imbalanced classification: A paradigm‐based review

   https://doi.org/10.1002/sam.11538  

   Feng, Yang ; Zhou, Min ; Tong, Xin ( August 2021 , Statistical Analysis and Data Mining: The ASA Data Science Journal)

   A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm‐based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost‐sensitive learning paradigm that minimizes a cost‐adjusted weighted type I and type II errors, and the Neyman–Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state‐of‐the‐art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real dataset on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics,more »and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.

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3. Security, Privacy, and Access Control in Information-Centric Networking: A Survey

   https://doi.org/10.1109/COMST.2017.2749508  

   Tourani, Reza ; Misra, Satyajayant ; Mick, Travis ; Panwar, Gaurav ( January 2018 , IEEE Communications surveys and tutorials)

   Information-centric networking (ICN) replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet and mobile ad hoc networks) with an information-centric paradigm, which prioritizes the delivery of named content, oblivious of the contents' origin. Content and client security, provenance, and identity privacy are intrinsic by design in the ICN paradigm as opposed to the current host centric paradigm where they have been instrumented as an afterthought. However, given its nascency, the ICN paradigm has several open security and privacy concerns. In this paper, we survey the existing literature in security and privacy in ICN and present open questions. More specifically, we explore three broad areas: 1) security threats; 2) privacy risks; and 3) access control enforcement mechanisms. We present the underlying principle of the existing works, discuss the drawbacks of the proposed approaches, and explore potential future research directions. In security, we review attack scenarios, such as denial of service, cache pollution, and content poisoning. In privacy, we discuss user privacy and anonymity, name and signature privacy, and content privacy. ICN's feature of ubiquitous caching introduces a major challenge for access control enforcement that requires special attention. We review existing access control mechanisms including encryption-based, attribute-based, session-based, andmore »proxy re-encryption-based access control schemes. We conclude the survey with lessons learned and scope for future work.« less
4. Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

   https://doi.org/10.1109/FIE56618.2022.9962643  

   Wei-Kocsis, Jin ; Sabounchi, Moein ; Yang, Baijian ; Zhang, Tonglin ( October 2022 , 2022 IEEE Frontiers in Education Conference (FIE))

   This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposedmore »learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.« less
5. Self-relevance effects and label choice: Strong variations in label-matching performance due to non-self-relevant factors

   https://doi.org/10.3758/s13414-017-1307-8  

   Wade, Gregory L. ; Vickery, Timothy J. ( April 2017 , Attention, Perception, & Psychophysics)

   Merely associating one’s self with a stimulus may be enough to enhance performance in a label-matching paradigm (Sui, He, & Humphreys, 2012), implying prioritized processing of self-relevant stimuli. For instance, labeling a square as SELF and a circle as OTHER yields speeded performance when verifying square-SELF compared with circle-OTHER label matches. The precise causes of such effects are unclear. We propose that prioritized processing of label-matches can occur for reasons other than self-relevance. Here, we employ the label-matching paradigm to show similar benefits for non-self-relevant labels (SNAKE, FROG, and GREG) over a frequently employed, non-self-relevant control label (OTHER). These benefits suggest the possibility that self-relevance effects in the label-matching paradigm may be confounded with other properties of labels that lead to relative performance benefits, such as concreteness. The size of self-relevance effects may be overestimated in prior work employing the label-matching paradigm, which calls for greater care in the choice of control labels to determine the true magnitude of self-relevance effects. Our results additionally indicate the possibility of a powerful effect of concreteness (and related properties) on associative memory performance.