Assumption Of Knowledge Research Articles

Dynamic trigger-based attacks against next-generation IoT malware family classifiers

The evolution of IoT malware and the effectiveness of defense strategies, e.g., leveraging malware family classification, have driven the development of advanced classification learning models. These models, particularly those that utilize model-extracted features, significantly enhance classification performance while minimizing the need for extensive expert knowledge from developers. However, a critical challenge lies in the interpretability of these learning models, which can obscure potential security risks. Among these risks are backdoor attacks, a sophisticated and deceptive threat where attackers induce malicious behaviors in the model under specific triggers.In response to the growing need for integrity and reliability in these models, this work assesses the vulnerability of state-of-the-art IoT malware classification models to backdoor attacks. Given the complexities of attacking model-based classifiers, we propose a novel trigger generation framework, B-CTG, supported by a specialized training procedure. This framework enables B-CTG to dynamically poison or attack samples to achieve specific objectives. From an attacker’s perspective, the design and training of B-CTG incorporate knowledge from the IoT domain to ensure the attack’s effectiveness. We conduct experiments under two distinct knowledge assumptions: the main evaluation, which assesses the attack method’s performance when the attacker has limited control over the model training pipeline, and the transferred setting, which further explores the significance of knowledge in predicting attacks in real-world scenarios.Our in-depth analysis focuses on attack performance in specific scenarios rather than a broad examination across multiple scenarios. Results from the main evaluation demonstrate that the proposed attack strategy can achieve high success rates even with low poisoning ratios, though stability remains a concern. Additionally, the inconsistent trends in model performance suggest that designers may struggle to detect the poisoned state of a model based on its performance alone. The transferred setting highlights the critical importance of model and feature knowledge for successful attack predictions, with feature knowledge proving particularly crucial. This insight prompts further investigation into model-agnostic mitigation methods and their effectiveness against the proposed attack strategy, with findings indicating that stability remains a significant concern for both attackers and defenders.

The Uber-Knowledge Assumption: A Bridge to the AGM

The generic-group model (GGM) and the algebraic-group model (AGM) have been exceptionally successful in proving the security of many classical and modern cryptosystems. These models, however, come with standard-model uninstantiability results, raising the question of whether the schemes analyzed under them can be based on firmer standard-model footing. We formulate the uber-knowledge (UK) assumption, a standard-model assumption that naturally extends the uber-assumption family to knowledge-type problems. We justify the soundness of UK in both the bilinear GGM and the bilinear AGM. Along the way we extend these models to account for hashing into groups, an adversarial capability that is available in many concrete groups—In contrast to standard assumptions, hashing may affect the validity of knowledge assumptions. These results, in turn, enable a modular approach to security in the GGM and the AGM. As example applications, we use the UK assumption to prove knowledge soundness of Groth's zero-knowledge SNARK (EUROCRYPT 2016) and of KZG polynomial commitments (ASIACRYPT 2010) in the standard model, where for the former we reuse the existing proof in the AGM without hashing.

Knowledge and Anguish at Work: the Clinical Case of a Telephone Operator

Objective: The objective of this study was to understand the meaning of mental symptoms manifested at work by articulating their material basis present in the work context and the subject's historical singularity. Theoretical Framework: Psychoanalytic theory is used in this case study as a basis for understanding the relationship between knowledge and anguish at work. On the other hand, it is in ergology that we find the necessary theorizations for a reading of what affects the subject in tele-service work. Method: This is a clinical case study based on the psychoanalytically oriented notes of a telemarketer with work-related symptoms. Results and Discussion: As the main result, we read symptomatic production both as a response on a level of direct material causality to the inconsistency of the assumption of knowledge attributed to the trade and the value of work, and as a way out that the subject finds for their symptomatic position. Implications of the research: We conclude that the orientation of the treatment must seek to preserve the assumption of knowledge in the subject and in their work and enable subjective and material accountability. Originality/Value: This study contributes to the literature by combining psychoanalysis and ergology in the search for an understanding of illness at work. The relevance and value of this research is evidenced by the fact that it presents an original clinical case that seeks to highlight the relationship between the place reserved for knowledge in contemporary work and the symptoms produced by it.

Impact of network topology changes on information source localization

Well-established methods of locating the source of information in a complex network are usually derived with the assumption of complete and exact knowledge of network topology. We study the performance of three such algorithms (Limited Pinto–Thiran–Vetterli Algorithm — LPTVA, Gradient Maximum Likelihood Algorithm — GMLA and Pearson Correlation Algorithm — PCA) in scenarios that do not fulfill this assumption by modifying the network before localization. This is done by adding superfluous new links, hiding existing ones, or reattaching links following the network’s structural Hamiltonian. Our results show that GMLA is highly resilient to adding superfluous edges, as its precision falls by more than statistical uncertainty only when the number of links is approximately doubled. On the other hand, if the edge set is underestimated or reattachment has taken place, the performance of GMLA drops significantly. In such a scenario, PCA is preferable, retaining most of its performance when other simulation parameters favor successful localization (high density of observers, highly deterministic propagation). It is also generally more accurate than LPTVA and orders of magnitude faster. The differences between localization algorithms can be intuitively explained, although further theoretical research is needed.

Personalized pricing decisions through adversarial risk analysis

AbstractPricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision‐making problems, pricing unfolds in a highly competitive and uncertain environment. Traditional analyses in this area have heavily relied on game theory and its variants. However, an important drawback of these approaches is their reliance on common knowledge assumptions, which are hardly tenable in competitive business domains. This paper introduces an innovative personalized pricing framework designed to assist decision‐makers in undertaking pricing decisions amidst competition, considering both buyer's and competitors' preferences. Our approach (i) establishes a coherent framework for modeling competition mitigating common knowledge assumptions; (ii) proposes a principled method to forecast competitors' pricing and customers' purchasing decisions, acknowledging major business uncertainties; and (iii) encourages structured thinking about the competitors' problems, thus enriching the solution process. To illustrate these properties, in addition to a general pricing template, we outline two specifications—one from the retail domain and a more intricate one from the pension fund domain.

Physical model learning based false data injection attack on power system state estimation

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

Influence of Orbit and Mass Constraints on Reflected Light Characterization of Directly Imaged Rocky Exoplanets

Survey strategies for upcoming exoplanet direct imaging missions have considered varying assumptions of prior knowledge. Precursor radial velocity surveys could have detected nearby exo-Earths and provided prior orbit and mass constraints. Alternatively, a direct imaging mission performing astrometry could yield constraints on the orbit and phase angle of target planets. Understanding the impact of prior mass and orbit information on planetary characterization is crucial for efficiently recognizing habitable exoplanets. To address this question, we use a reflected-light retrieval tool to infer the atmospheric and bulk properties of directly imaged Earth-analogs while considering varying levels of prior information and signal-to-noise ratio (S/N). Because of the strong correlation between the orbit-related parameters and the planetary radius, prior information on the orbital distance and planetary phase angle yield much tighter constraints on the planetary radius: from Rp=2.95−1.95+2.69R⊕ without prior knowledge, to Rp=1.01−0.19+0.33R⊕ with prior determination of the orbit for S/N = 20 in the visible/near-infrared spectral range, thus allowing size determination from reflected light observations. However, additional knowledge of planet mass does not notably enhance radius ( Rp=0.98−0.14+0.17R⊕ ) or atmospheric characterization. Also, prior knowledge of the mass alone does not yield a tight radius constraint ( Rp=1.64−0.80+1.29R⊕ ) nor improves atmospheric composition inference. By contrast, because of its sensitivity to gas column abundance, detecting a Rayleigh scattering slope or bounding Rayleigh opacity helps to refine gas mixing ratio inferences without requiring prior mass knowledge. Overall, apart from radius determination, increasing the S/N is more beneficial than additional prior observations.

Considering critical moments, co-authoring and active engagement in learning

ABSTRACT Background Within classroom research, there has long been agreement on the importance of dialogue and discussion for student learning. However, how the concept of knowledge influences classroom conversation needs further investigation, as this has key implications for students’ active participation. Purpose Our research sought to conceptualise the assumption of knowledge in standards-based curricula and explore some implications of teaching based on this kind of curriculum. To do this, we drew on a larger research project undertaken in Sweden, which involved a comparative classroom study. Methods Four natural science lessons were examined in terms of student’s opportunities to engage in the teaching content. The analytical framework was based on curriculum theory, the concepts of a lesson as a curriculum event, and students as co-authors of teaching content. We analysed two concepts of knowledge – social realism and transactional realism – in relation to an openness towards ‘critical moments’ during lessons, either noticed or unnoticed, and related them to the logics of curriculum and knowledge. Findings When framed by classroom teaching designed from knowledge criteria, students’ opportunities for acting as co-authors can become restricted, with critical moments overlooked because of a teaching focus necessarily limited by curriculum. Thus, opportunities for creating spaces to pay attention to students’ questions and reactions can be constrained. Conclusions Standards-based curricula, a concept of knowledge with a strong focus on subject-specific facts and ways of reasoning, together with high-stakes assessment, may lead to fewer openings for genuine discussion and student reflection. This highlights the need to leave larger spaces for teachers and students alike to influence content that engages students.

Learning to Persuade on the Fly: Robustness Against Ignorance

How Can Platforms Learn to Make Persuasive Recommendations? Many online platforms make recommendations to users on content from creators or products from sellers. The motivation underlying such recommendations is to persuade users into taking actions that also serve system-wide goals. To do this effectively, a platform needs to know the underlying distribution of payoff-relevant variables (such as content or product quality). However, this distributional information is often lacking—for example, when new content creators or sellers join a platform. In “Learning to Persuade on the Fly: Robustness Against Ignorance,” Zu, Iyer, and Xu study how a platform can make persuasive recommendations over time in the absence of distributional knowledge using a learning-based approach. They first propose and motivate a robust-persuasiveness criterion for settings with incomplete information. They then design an efficient recommendation algorithm that satisfies this criterion and achieves low regret compared with the benchmark of complete distributional knowledge. Overall, by relaxing the strong assumption of complete distributional knowledge, this research extends the applicability of information design to more practical settings.

Scientific Testability Following the Assumption of Insufficient Knowledge and Resources

Abstract Carnap described ways to test scientific hypotheses. However, Carnap acknowledged that confirmation can never be definite. This left open the issue about the criteria to accept hypotheses. On the other hand, Wang has developed a computer program working without sufficient knowledge or resources, which makes the action of the program akin to the manner the human mind thinks. Wang’s program includes quantitative indicators that can be assigned to the frequency and the confidence of sentences. The present paper tries to link both approaches. The goal is to show how quantitative indicators such as those in Wang’s program can also be attributed to scientific hypotheses. Those indicators can help make decisions about the acceptance of the hypotheses. All of this allows proposing general characteristics for a possible algorithm to decide whether a particular hypothesis is admissible.

Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts

Very recently, Janssens and Meyers (Wind Energy Sci., vol. 9, 65–95, 2024) proposed a time-decoupled model-predictive control (TD-MPC) framework for wind farm power optimization using a control model based on coarse-grid large-eddy simulation (LES). In a receding-horizon fashion, by computing the controls for the next time window based on a prediction of the flow (so ahead of time), they achieved (almost) real-time computational speed. The current paper further explores the potential of this approach in the context of dynamic yaw control, under the assumption of exact state knowledge. To that end, the LES-based, TD-MPC framework is validated on a reference wind farm for three different wind directions, for which we observe gains ranging from 4 (for staggered configurations) to 42% (for aligned configurations w.r.t. the wind direction). The LES-based controller always outperforms a Betz optimal controller and a static (wake model based) yaw controller at near real-time speed. An analysis of the yaw angles reveals that the framework is very effective in steering the wakes away from downstream turbines in upstream and downstream regions, synchronized to the turbulent inflow. By comparing the performance of the LES-based controllers (based on the actuator disc turbine model) against a reference simulation that uses a more accurate actuator sector model, we illustrate the robustness of the approach in the presence of the additional model error.

The role of uncertainties in the seismic assessment of masonry churches affected by compound rocking failure mechanism: Macro-block limit analysis investigations

Façade overturning is one of the most common failure mechanisms observed in single-nave masonry churches when subjected to seismic action. Several factors, including geometry, masonry patterns, mechanical properties, and loading conditions, influence the force and displacement capacities of these masonry churches. This study aims to assess the impact of various model parameters on the seismic assessment of single-nave masonry churches subjected to the compound rocking failure mechanism by adopting macro-block limit analysis. The analysis of variance (ANOVA) is employed to investigate the influence of geometrical and mechanical parameters under the assumption of complete knowledge of the structure. Furthermore, probabilistic analysis explores how incomplete knowledge may affect the structural assessment of the compound rocking failure mechanism in single-nave masonry churches. The findings of this study highlight the importance of accurate modelling and surveying in evaluating the seismic vulnerability of single-nave masonry churches and can be useful for developing effective seismic risk mitigation strategies.

Shorter ZK-SNARKs from square span programs over ideal lattices

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are cryptographic protocols that offer efficient and privacy-preserving means of verifying NP language relations and have drawn considerable attention for their appealing applications, e.g., verifiable computation and anonymous payment protocol. Compared with the pre-quantum case, the practicability of this primitive in the post-quantum setting is still unsatisfactory, especially for the space complexity. To tackle this issue, this work seeks to enhance the efficiency and compactness of lattice-based zk-SNARKs, including proof length and common reference string (CRS) length. In this paper, we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings. Compared with previous works, our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes. Particularly, the proof length of our scheme is around 23.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$23.3\\%$$\\end{document} smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al. (in: Proceedings of the 2021 ACM SIGSAC conference on computer and communications security, pp 212–234, 2021), and the CRS length is 3.6×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.6\ imes$$\\end{document} smaller. Our constructions follow the framework of Gennaro et al. (in: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 556–573, 2018), and adapt it to the ring setting by slightly modifying the knowledge assumptions. We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.

Nonequilibrium thermodynamics of uncertain stochastic processes

Stochastic thermodynamics is formulated under the assumption of perfect knowledge of all thermodynamic parameters. However, in any real-world situation, there is nonzero uncertainty about the precise value of temperatures, chemical potentials, energy spectrum, etc. Here we investigate how this uncertainty modifies the theorems of stochastic thermodynamics. We consider two scenarios: in the scenario, we fix the (unknown, randomly generated) parameters of an experimental apparatus and then repeatedly observe (stochastic) trajectories of the system for that fixed apparatus. In contrast, in the (consistently with the effective scenario) scenario, the (unknown) apparatus is re-generated for each trajectory. We derive expressions for thermodynamic quantities in both scenarios. For the effective scenario, we also discuss the physical interpretation of entropy production (EP) and derive the mismatch cost. To illustrate this scenario, we also provide a numerical analysis of the thermodynamics of a quantum dot implementing bit erasure with uncertain temperature. We also analyze the protocol for changing the distribution over states in a way that maximizes work extraction, again in the effective scenario. Next, we investigate the effective thermodynamic value of information, focusing on the case where there is a delay between the initialization of the system and the start of the protocol. Finally, we derive the detailed and integral fluctuation theorems (FTs) for the phenomenological EP. In particular, we show how the phenomenological FTs account for the fact that the longer a trajectory runs, the more information it provides concerning the precise experimental apparatus, and therefore the less EP it generates. Our results provide a very preliminary investigation of the myriad issues that arise when one tries to expand stochastic thermodynamics to account for uncertainty in the parameters governing a physical process. Published by the American Physical Society 2024

Generating a Template for an Educational Software Development Methodology for Novice Computing Undergraduates: An Integrative Review

Aim/Purpose: The teaching of appropriate problem-solving techniques to novice learners in undergraduate software development education is often poorly defined when compared to the delivery of programming techniques. Given the global need for qualified designers of information technology, the purpose of this research is to produce a foundational template for an educational software development methodology grounded in the established literature. This template can be used by third-level educators and researchers to develop robust educational methodologies to cultivate structured problem solving and software development habits in their students while systematically teaching the intricacies of software creation. Background: While software development methodologies are a standard approach to structured and traceable problem solving in commercial software development, educational methodologies for inexperienced learners remain a neglected area of research due to their assumption of prior programming knowledge. This research aims to address this deficit by conducting an integrative review to produce a template for such a methodology. Methodology: An integrative review was conducted on the key components of Teaching Software Development Education, Problem Solving, Threshold Concepts, and Computational Thinking. Systematic reviews were conducted on Computational Thinking and Software Development Education by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process. Narrative reviews were conducted on Problem Solving and Threshold Concepts. Contribution: This research provides a comprehensive analysis of problem solving, software development education, computational thinking, and threshold concepts in computing in the context of undergraduate software development education. It also synthesizes review findings from these four areas and combines them to form a research-based foundational template methodology for use by educators and researchers interested in software development undergraduate education. Findings: This review identifies seven skills and four concepts required by novice learners. The skills include the ability to perform abstraction, data representation, decomposition, evaluation, mental modeling, pattern recognition, and writing algorithms. The concepts include state and sequential flow, non-sequential flow control, modularity, and object interaction. The teaching of these skills and concepts is combined into a spiral learning framework and is joined by four development stages to guide software problem solving: understanding the problem, breaking into tasks, designing, coding, testing, and integrating, and final evaluation and reflection. This produces the principal finding, which is a research-based foundational template for educational software development methodologies. Recommendations for Practitioners: Focusing introductory undergraduate computing courses on a programming syllabus without giving adequate support to problem solving may hinder students in their attainment of development skills. Therefore, providing a structured methodology is necessary as it equips students with essential problem-solving skills and ensures they develop good development practices from the start, which is crucial to ensuring undergraduate success in their studies and beyond. Recommendation for Researchers: The creation of educational software development methodologies with tool support is an under-researched area in undergraduate education. The template produced by this research can serve as a foundational conceptual model for researchers to create concrete tools to better support computing undergraduates. Impact on Society: Improving the educational value and experience of software development undergraduates is crucial for society once they graduate. They drive innovation and economic growth by creating new technologies, improving efficiency in various industries, and solving complex problems. Future Research: Future research should concentrate on using the template produced by this research to create a concrete educational methodology adapted to suit a specific programming paradigm, with an associated learning tool that can be used with first-year computing undergraduates.

A visionary platform for decolonization: The Red Deal.

In this study, we discuss the colonial project as an eliminatory structure of indigenous ways of knowing and doing that is built into Canadian social and health institutions. We elaborate on the role nursing plays in maintaining systemic racism, marginalization and discrimination of Indigenous Peoples. Based on historical practices and present-day circumstances, we argue that changing language in research and school curriculums turns decolonization into what Tuck and Yang call a 'metaphor'. Rather, we propose decolonization as a political project where nurses acknowledge their involvement in colonial harms and disrupt the assumptions that continue to shape how nurses interact with Indigenous people, including knowledge systems that perpetuate colonial interests and privilege. Decolonization requires nurses to understand the colonial practices that led to dispossession of land, erasure of knowledge, culture and identity, while upholding indigenous ways of knowing and doing in health, healing and living. As a political manifesto that liberates indigenous life from oppressive structures of colonialism and capitalism, The Red Deal is presented as a visionary platform for decolonization. The aim of this study is to articulate three dimensions of caretaking within The Red Deal as a framework to decolonize nursing knowledge development and practice. Based on the philosophical dimension embedded in The Red Deal that revoke norms and knowledge assumptions of capitalism that destroy indigenous ways of knowing and doing, we underscore an approach toward decolonizing nursing. Our approach rejects the apolitical nature of nursing as well as the unilateral western scientific knowledge approach to knowledge development and recognition. A critical emancipatory approach that addresses the socio-political and historical context of health care, recognizes dispossession of land and adopts a 'multilogical' vision of knowledge that gives space for representation and voice is needed for true decolonization of nursing.

Measures of Information Leakage for Incomplete Statistical Information: Application to a Binary Privacy Mechanism

Information leakage is usually defined as the logarithmic increment in the adversary’s probability of correctly guessing the legitimate user’s private data or some arbitrary function of the private data when presented with the legitimate user’s publicly disclosed information. However, this definition of information leakage implicitly assumes that both the privacy mechanism and the prior probability of the original data are entirely known to the attacker. In reality, the assumption of complete knowledge of the privacy mechanism for an attacker is often impractical. The attacker can usually have access to only an approximate version of the correct privacy mechanism, computed from a limited set of the disclosed data, for which they can access the corresponding un-distorted data. In this scenario, the conventional definition of leakage no longer has an operational meaning. To address this problem, in this article, we propose novel meaningful information-theoretic metrics for information leakage when the attacker has incomplete information about the privacy mechanism—we call them average subjective leakage , average confidence boost , and average objective leakage , respectively. For the simplest, binary scenario, we demonstrate how to find an optimized privacy mechanism that minimizes the worst-case value of either of these leakages.

Algorithmic Minimization of Uncertain Continuous-Time Markov Chains

The assumption of perfect knowledge of rate parameters in continuous-time Markov chains (CTMCs) is undermined when confronted with reality, where they may be uncertain due to lack of information or because of measurement noise. Here we consider uncertain CTMCs (UCTMCs), where rates are assumed to vary non-deterministically with time from bounded continuous intervals. An uncertain CTMC can be therefore seen as a specific type of Markov decision process for which the analysis is computationally difficult. To tackle this, we develop a theory of minimization which generalizes the notion of lumpability for CTMCs. Our first result is a quantitative and logical characterization of minimization. Specifically, we show that the reduced UCTMC model has a macro-state for each block of a partition of the state space, which preserves value functions and logical formulae whenever rewards are equal within each block. The second result is an efficient minimization algorithm for UCTMCs by means of partition refinement. As application, we show that reductions in a number of CTMCbenchmark models are robust with respect to uncertainties in original rates.

Decolonizing bird knowledge: More-than-Western bird–human relations

Abstract Traditional ecological knowledge (TEK) or local ecological knowledge (LEK) has only recently gained traction as “legitimate” science in Western academic discourse. Such approaches to inclusivity continue to face institutional, sociocultural, and equity barriers to being fully accepted in academic discourse in comparison to Western-based frameworks. Postcolonial studies have attempted to rectify this Western-domination in characterizing diverse forms of bird–human relationships. However, the integration of multiple cosmologies (worldviews) and ontologies (realities) in research or management creates challenges that we discuss. We elucidate commonalities and antithetical positions between Western-derived bird knowledge and management with that of TEK or LEK in both local and global contexts. We combine ecological/ornithological studies with key terms, theories, and methods from the social sciences to integrate the approaches and facilitate understanding. For example, we follow a “theory synthesis” approach in this conceptual paper to question epistemological and ontological assumptions of bird knowledge and how we acquire it to question, “how do we move from a decolonial approach (discussions and acknowledgement) to decolonization (action)?” This paper is a product of ongoing discourse among global researchers of an academic ethno-ornithology research lab based in the United Kingdom, who partner with global collaborators. The 3 case studies draw from ongoing research in Southeast Asia, South America, and decolonializing policy efforts in New Zealand. We analyzed these case studies using a postcolonial theoretical lens to provide insights into how Western scientists can embrace TEK and LEK and actively work to decolonize ethno-ornithology and ornithology in theory and practice. Further, we discuss perceived core tenets to equity and inclusion in community-based TEK and LEK conservation projects from the Global South. Diversity, equity, inclusivity, and justice in bird–human relations and knowledge were identified as targets for systemic change within the academic institutions of Western scientists. By recognizing, discussing, and embracing non-Western cosmologies and ontologies, non-Indigenous scientists can help influence the decolonization of ethno-ornithology, ornithology, and bird–human relations through respectful, participatory, equitable, culturally considerate, and “non-extractive” community-based initiatives in partnership with local groups.

The Complexities of Mechanical Ventilation: Toppling the Tower of Babel.

The exponential increase in the complexity of ventilator technology has created a growing knowledge gap that hinders education, research, and ultimately the quality of patient care. This gap is best addressed with a standardized approach to educating clinicians, just as education for basic and advanced life support classes is standardized. We have developed such a program, called Standardized Education for Ventilatory Assistance (SEVA), based on a formal taxonomy for modes of mechanical ventilation. The SEVA program is a progressive system of 6 sequential courses starting from an assumption of no prior knowledge and proceeding to full mastery of advanced techniques. The vision of the program is to provide a unique platform for standardizing training by unifying the concepts of physics, physiology, and technology of mechanical ventilation. The mission is to use both online and in-person simulation-based instruction that has both self-directed and instructor-led components to elevate the skills of health care providers to the mastery level. The first 3 levels of SEVA are free and open to the public. We are developing mechanisms to offer the other levels. Spinoffs of the SEVA program include a free smartphone app that classifies virtually all modes on all ventilators used in the United States (Ventilator Mode Map), a free biweekly online training sessions focusing on waveform interpretation (SEVA-VentRounds), and modifications to the electronic health care record system for entering and charting ventilator orders.