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1. Mechanical Impedance

   French, Mark ( January 2019 , American lutherie)

   We all know what “impedance” is, right? It’s that stuff... the force that... well, what is it really? Turns out it’s something like pushing a kid on a swing at the wrong time. And it has a lot to do with the resonances of acoustic instruments, which has a lot to do with how they sound. Anyway, Professor Mark gives us the scoop. 

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2. Is language-of-thought the best game in the town we live?

   https://doi.org/10.1017/S0140525X23001814  

   Lupyan, Gary ( January 2023 , Behavioral and Brain Sciences)

   There are towns in which language-of-thought (LoT) is the best game. But do we live in one? I go through three properties that characterize the LoT hypothesis: Discrete constituents, role-filler independence, and logical operators, and argue that in each case predictions from the LoT hypothesis are a poor fit to actual human cognition. As a hypothesis of what human cognition ought to be like, LoT departs from empirical reality.

    

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3. Analyzing the Feasibility and Generalizability of Fingerprinting Internet of Things Devices

   https://doi.org/10.2478/popets-2022-0057  

   Ahmed, Dilawer ; Das, Anupam ; Zaffar, Fareed ( March 2022 , Proceedings on Privacy Enhancing Technologies)

   Abstract In recent years, we have seen rapid growth in the use and adoption of Internet of Things (IoT) devices. However, some loT devices are sensitive in nature, and simply knowing what devices a user owns can have security and privacy implications. Researchers have, therefore, looked at fingerprinting loT devices and their activities from encrypted network traffic. In this paper, we analyze the feasibility of fingerprinting IoT devices and evaluate the robustness of such fingerprinting approach across multiple independent datasets — collected under different settings. We show that not only is it possible to effectively fingerprint 188 loT devices (with over 97% accuracy), but also to do so even with multiple instances of the same make-and-model device. We also analyze the extent to which temporal, spatial and data-collection-methodology differences impact fingerprinting accuracy. Our analysis sheds light on features that are more robust against varying conditions. Lastly, we comprehensively analyze the performance of our approach under an open-world setting and propose ways in which an adversary can enhance their odds of inferring additional information about unseen devices (e.g., similar devices manufactured by the same company). 

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4. Naturalness in the making: naturalizing naturalness in plant morphology

   Kendig, Catherine ( July 2024 , Australasian Association of Philosophy)

   Abstract: What role, if any, does the concept of naturalness play in the development of scientific knowledge and understanding? Whether naturalness is taken to be an ontological dimension of the world or a cognitive dimension of our human perspective within it, assumptions of naturalness seem to frame concepts and practices that inform the partitioning of parts and the kinding of kinds. Within the natural sciences, knowledge of what something is and how it’s studied rely on conceptual commitments. These conceptual commitments often shape how entities and processes are categorized as natural depending on how naturalness has been understood within that discipline. I explore how commitments to naturalness shaped different incompatible conceptualizations of what were (and in some cases still are) considered to be fundamental parts in plant morphology. Employing an historically informed epistemological approach, I trace the development of three models of plant morphology: Goethe’s LEAF-ROOT-STEM archetype; Agnes Arber’s partial-shoot theory of the leaf; and Rolf Sattler’s processual model of plant morphology. These models are ontologically and epistemologically inconsistent. I explore what this inconsistency means for the concept of naturalness and the role it plays in plant morphology? 

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5. Learning Association between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions

   https://doi.org/10.1007/978-3-030-78270-2_57  

   Shimmei, M. ; Matsuda, N. ( January 2021 , Proceedings of the International Conference on Artificial Intelligence in Education)

   Roll, I. ; McNamara, D. (Ed.)

   It has been shown that answering questions contributes to students learning effectively. However, generating questions is an expensive task and requires a lot of effort. Although there has been research reported on the automa- tion of question generation in the literature of Natural Language Processing, these technologies do not necessarily generate questions that are useful for educational purposes. To fill this gap, we propose QUADL, a method for generating questions that are aligned with a given learning objective. The learning objective reflects the skill or concept that students need to learn. The QUADL method first identifies a key concept, if any, in a given sentence that has a strong connection with the given learning objective. It then converts the given sentence into a question for which the predicted key concept becomes the answer. The results from the survey using Amazon Mechanical Turk suggest that the QUADL method can be a step towards generating questions that effectively contribute to students’ learning. 

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