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1. Hazard analysis for human-on-the-loop interactions in sUAS systems

   https://doi.org/10.1145/3468264.3468534  

   Vierhauser, Michael; Islam, Md Nafee; Agrawal, Ankit; Cleland-Huang, Jane; Mason, James (August 2021, Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

   null (Ed.)

   With the rise of new AI technologies, autonomous systems are moving towards a paradigm in which increasing levels of responsibility are shifted from the human to the system, creating a transition from human-in-the-loop systems to human-on-the-loop (HoTL) systems. This has a significant impact on the safety analysis of such systems, as new types of errors occurring at the boundaries of human-machine interactions need to be taken into consideration. Traditional safety analysis typically focuses on system-level hazards with little focus on user-related or user-induced hazards that can cause critical system failures. To address this issue, we construct domain-level safety analysis assets for sUAS (small unmanned aerial systems) applications and describe the process we followed to explicitly, and systematically identify Human Interaction Points (HiPs), Hazard Factors and Mitigations from system hazards. We evaluate our approach by first investigating the extent to which recent sUAS incidents are covered by our hazard trees, and second by performing a study with six domain experts using our hazard trees to identify and document hazards for sUAS usage scenarios. Our study showed that our hazard trees provided effective coverage for a wide variety of sUAS application scenarios and were useful for stimulating safety thinking and helping users to identify and potentially mitigate human-interaction hazards. 

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2. Towards a ‘people and nature’ paradigm for biodiversity and infectious disease

   https://doi.org/10.1098/rstb.2023.0259  

   Gibb, Rory; Redding, David W; Friant, Sagan; Jones, Kate E (January 2025, Philosophical Transactions of the Royal Society B: Biological Sciences)

   Zoonotic and vector-borne infectious diseases are among the most direct human health consequences of biodiversity change. The COVID-19 pandemic increased health policymakers’ attention on the links between ecological degradation and disease, and sparked discussions around nature-based interventions to mitigate zoonotic emergence and epidemics. Yet, although disease ecology provides an increasingly granular knowledge of wildlife disease in changing ecosystems, we still have a poor understanding of the net consequences for human disease. Here, we argue that a renewed focus on wildlife-borne diseases as complex socio-ecological systems—a‘people and nature’paradigm—is needed to identify local interventions and transformative system-wide changes that could reduce human disease burden. We discuss longstanding scientific narratives of human involvement in zoonotic disease systems, which have largely framed people as ecological disruptors, and discuss three emerging research areas that provide wider system perspectives: how anthropogenic ecosystems construct new niches for infectious disease, feedbacks between disease, biodiversity and social vulnerability and the role of human-to-animal pathogen transmission (‘spillback’) in zoonotic disease systems. We conclude by discussing new opportunities to better understand the predictability of human disease outcomes from biodiversity change and to integrate ecological drivers of disease into health intervention design and evaluation. This article is part of the discussion meeting issue ‘Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future’. 

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3. Design of a Real-Time Human-Robot Collaboration System Using Dynamic Gestures

   https://doi.org/10.1115/IMECE2020-23650  

   Chen, Haodong; Leu, Ming C.; Tao, Wenjin; Yin, Zhaozheng (November 2020, Proceedings of the ASME 2020 International Mechanical Engineering Congress and Exposition (IMECE 2020))

   Abstract With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part of factory production, and the human-robot collaboration (HRC) becomes a new trend in the industrial field. In our previous work, ten dynamic gestures have been designed for communication between a human worker and a robot in manufacturing scenarios, and a dynamic gesture recognition model based on Convolutional Neural Networks (CNN) has been developed. Based on the model, this study aims to design and develop a new real-time HRC system based on multi-threading method and the CNN. This system enables the real-time interaction between a human worker and a robotic arm based on dynamic gestures. Firstly, a multi-threading architecture is constructed for high-speed operation and fast response while schedule more than one task at the same time. Next, A real-time dynamic gesture recognition algorithm is developed, where a human worker’s behavior and motion are continuously monitored and captured, and motion history images (MHIs) are generated in real-time. The generation of the MHIs and their identification using the classification model are synchronously accomplished. If a designated dynamic gesture is detected, it is immediately transmitted to the robotic arm to conduct a real-time response. A Graphic User Interface (GUI) for the integration of the proposed HRC system is developed for the visualization of the real-time motion history and classification results of the gesture identification. A series of actual collaboration experiments are carried out between a human worker and a six-degree-of-freedom (6 DOF) Comau industrial robot, and the experimental results show the feasibility and robustness of the proposed system. 

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4. On the Generalizability and Predictability of Recommender Systems

   McElfresh, Duncan; Khandagale, Sujay; Valverde, Jonathan; Dickerson, John P.; White, Colin (January 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022))

   While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natural follow-up question is, "how do we choose the right algorithm for a new dataset and performance metric?" In this work, we start by giving the first large-scale study of recommender system approaches by comparing 24 algorithms and 100 sets of hyperparameters across 85 datasets and 315 metrics. We find that the best algorithms and hyperparameters are highly dependent on the dataset and performance metric. However, there is also a strong correlation between the performance of each algorithm and various meta-features of the datasets. Motivated by these findings, we create RecZilla, a meta-learning approach to recommender systems that uses a model to predict the best algorithm and hyperparameters for new, unseen datasets. By using far more meta-training data than prior work, RecZilla is able to substantially reduce the level of human involvement when faced with a new recommender system application. We not only release our code and pretrained RecZilla models, but also all of our raw experimental results, so that practitioners can train a RecZilla model for their desired performance metric: https://github.com/naszilla/reczilla. 

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5. Human-in-the-loop Schema Induction

   https://doi.org/10.18653/v1/2023.acl-demo.1  

   Zhang, Tianyi; Tham, Isaac; Hou, Zhaoyi; Ren, Jiaxuan; Zhou, Leon; Xu, Hainiu; Zhang, Li; Martin, Lara; Dror, Rotem; Li, Sha; et al (January 2023, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics)

   Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction (IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface. 

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