Hasil untuk "Human ecology. Anthropogeography"

J. Kaye, P. Groffman, N. Grimm et al.

Michael Batty

Abstract Artificial Intelligence (AI) is entirely coincident with the emergence of the digital computer. It was assumed from the start, some 75 years ago, that the computer had more than the required power to simulate human intelligence. This led to the speculation that ultimately computers would take over many of our own tasks which we once considered could never be modelled using machines. Here, we sketch the history and evolution of AI, note the different phases in this history, define distinctions between strong and weak AI, and emphasise the difference between generative and discriminative processes. There are many possible applications in city planning with the most suggestive and possibly the most disruptive being those related to the development of new methods for generating sustainable plans and designs. We make a key distinction between inductive and deductive AI, demonstrating these differences with methods of machine learning (ML), showing how early network methods based on the perceptron, can be linked to deductive procedures that enable us to build new models for city design. Our key illustration links urban simulation models to land cover built around geospatial data infused with ML. The aim of this paper is to chart the progress in AI and its applicability to city science and city planning from its first applications and speculate on future developments.

Cynthia M. Baseman, Myeonghan Ryu, Nathaniel Swinger et al.

Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.

Федотова Т.К., Горбачева А.К.

Введение. Обсуждается процесс формирования величины и направления половых соматических различий в перипубертатном периоде (9–17 лет), этническая специфика динамики полового диморфизма основных антропометрических размеров. Материал и методы. Для построения межгруппового распределения стандартизованных величин полового диморфизма размеров тела в 9, 13 и 17 лет обобщены обширные литературные материалы по детям РФ и бывшего СССР 1950–2010 гг. (более 500 выборок). Для количественной оценки величины полового диморфизма (ПД) использована дивергенция Кульбака, аналог расстояния Махаланобиса. Для корректного учета вклада в вариации полового диморфизма этнического фактора из общего массива данных подобраны пары выборок разной этнической принадлежности (коренное население и русские), но из одной и той же экологической ниши, обследованных одновременно. Результаты. Показано значительное влияние возрастного фактора на межгрупповое распределение полового соматического диморфизма. Выявлены достоверные положительные корреляции полового диморфизма с величиной самих размеров для мальчиков в первую очередь в 13 и 17 лет (0,48–0,63 для весоростовых показателей) и отрицательные или близкие к нулю корреляции у девочек. Динамика полового диморфизма соматических размеров на интервале 9–17 лет для пар выборок эстонцы-русские, киргизы-русские, татары-русские, узбеки-русские в целом инвариантна относительно этнической принадлежности групп и свидетельствует о доминировании возрастных физиологических закономерностей над этническими. Заключение. Изменчивость соматического полового диморфизма в перипубертатном возрасте имеет примерно нормальную форму распределения, как и сами антропометрические показатели. Достоверные корреляции стандартизованных уровней полового диморфизма размеров со средневыборочными значениями самих размеров имеют разные знаки у мальчиков и девочек – положительные в первом случае, отрицательные или близкие к нулю во втором, что соответствует аналогичной закономерности у взрослых и может рассматриваться как подтверждение вклада соматической изменчивости мужского пола в формирование половых различий. При проведении «точечных» локальных сравнений ограниченного числа этнических групп одной экологической ниши не удалось зафиксировать межгрупповой ростовой специфики в связи с этническим фактором, что связано, видимо, с высокими скоростями роста в перипубертатном периоде и доминированием надэтнических видовых закономерностей над локальными этническими особенностями.

Ayyoob Sharifi, Melika Amirzadeh, Amir Reza Khavarian-Garmsir

Abstract Amidst rapid advancements in artificial intelligence and smart city technologies, this paper argues that the Metaverse, as a virtual form of smart cities, offers the potential to advance Sustainable Development Goals (SDGs), particularly in education, innovation, and sustainable urban development. However, goals related to inequality reduction and climate action are underrepresented. We argue that the Metaverse holds transformative potential to advance the SDGs, but its trade-offs for equality and environmental sustainability must be carefully considered. This requires adopting ethical and inclusive governance frameworks that are based on systems thinking.

Vaclav Knapp, Matyas Bohacek

Recent pose-transfer methods aim to generate temporally consistent and fully controllable videos of human action where the motion from a reference video is reenacted by a new identity. We evaluate three state-of-the-art pose-transfer methods -- AnimateAnyone, MagicAnimate, and ExAvatar -- by generating videos with actions and identities outside the training distribution and conducting a participant study about the quality of these videos. In a controlled environment of 20 distinct human actions, we find that participants, presented with the pose-transferred videos, correctly identify the desired action only 42.92% of the time. Moreover, the participants find the actions in the generated videos consistent with the reference (source) videos only 36.46% of the time. These results vary by method: participants find the splatting-based ExAvatar more consistent and photorealistic than the diffusion-based AnimateAnyone and MagicAnimate.

Maxence Grand, Damien Pellier, Francis Jambon

The primary objective of the dataset is to provide a better understanding of the coupling between human actions and gaze in a shared working environment with a cobot, with the aim of signifcantly enhancing the effciency and safety of humancobot interactions. More broadly, by linking gaze patterns with physical actions, the dataset offers valuable insights into cognitive processes and attention dynamics in the context of assembly tasks. The proposed dataset contains gaze and action data from approximately 80 participants, recorded during simulated industrial assembly tasks. The tasks were simulated using controlled scenarios in which participants manipulated educational building blocks. Gaze data was collected using two different eye-tracking setups -head-mounted and remote-while participants worked in two positions: sitting and standing.

Xiangrong, Zhu, Yuan Xu et al.

Human cognition is constrained by processing limitations, leading to cognitive overload and inefficiencies in knowledge synthesis and decision-making. Large Language Models (LLMs) present an opportunity for cognitive augmentation, but their current reactive nature limits their real-world applicability. This position paper explores the potential of context-aware cognitive augmentation, where LLMs dynamically adapt to users' cognitive states and task environments to provide appropriate support. Through a think-aloud study in an exhibition setting, we examine how individuals interact with multi-modal information and identify key cognitive challenges in structuring, retrieving, and applying knowledge. Our findings highlight the need for AI-driven cognitive support systems that integrate real-time contextual awareness, personalized reasoning assistance, and socially adaptive interactions. We propose a framework for AI augmentation that seamlessly transitions between real-time cognitive support and post-experience knowledge organization, contributing to the design of more effective human-centered AI systems.

Agnia Sergeyuk, Ilya Zakharov, Ekaterina Koshchenko et al.

The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally altering how developers interact with their tools. This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX), a field that explores the evolving dynamics of Human-Computer Interaction in AI-assisted coding environments. Despite rapid adoption, research on in-IDE HAX remains fragmented, which highlights the need for a unified overview of current practices, challenges, and opportunities. To provide a structured overview of existing research, we conduct a systematic literature review of 90 studies, summarizing current findings and outlining areas for further investigation. We organize key insights from reviewed studies into three aspects: Impact, Design, and Quality of AI-based systems inside IDEs. Impact findings show that AI-assisted coding enhances developer productivity but also introduces challenges, such as verification overhead and over-reliance. Design studies show that effective interfaces surface context, provide explanations and transparency of suggestion, and support user control. Quality studies document risks in correctness, maintainability, and security. For future research, priorities include productivity studies, design of assistance, and audit of AI-generated code. The agenda calls for larger and longer evaluations, stronger audit and verification assets, broader coverage across the software life cycle, and adaptive assistance under user control.

Jennifer Healey, Laurie Byrum, Md Nadeem Akhtar et al.

LLM evaluation is challenging even the case of base models. In real world deployments, evaluation is further complicated by the interplay of task specific prompts and experiential context. At scale, bias evaluation is often based on short context, fixed choice benchmarks that can be rapidly evaluated, however, these can lose validity when the LLMs' deployed context differs. Large scale human evaluation is often seen as too intractable and costly. Here we present our journey towards developing a semi-automated bias evaluation framework for free text responses that has human insights at its core. We discuss how we developed an operational definition of bias that helped us automate our pipeline and a methodology for classifying bias beyond multiple choice. We additionally comment on how human evaluation helped us uncover problematic templates in a bias benchmark.

Cameron J. Baker, Mariana A. Campbell, Vinay Udyawer et al.

Abstract Large predator attacks on humans often provoke calls for animal population reduction, assuming it will reduce such incidents. Whilst this seems logical, there is currently little evidence supporting a consistent link between large predator density and attacks on humans. Here, we assessed whether large predator density is linked to the frequency of attacks on humans using estuarine crocodile (Crocodylus porosus) data in the Northern Territory (NT), Australia. Over the past 50 years, the estuarine crocodile population in the NT has grown from a few thousand to over 100,000 non‐hatchling individuals. Crocodile and human population densities have been closely monitored throughout this period, allowing the frequency of attacks on humans to be assessed over a wide range of densities for both populations. Our analysis showed an increase in the frequency of attacks on humans as the crocodile population recovered from very low levels in the 1970s. However, the attack rates stabilised around 2009, despite crocodile density and the human population continuing to increase. Based on the relationship between crocodile density and human‐attack frequency, scenario modelling suggested that the crocodile population would need to be culled to a critically endangered level (e.g. 90% population reduction) to reduce attacks on humans from 2.16 to 1.16 attacks per year. We conclude that whilst crocodile density significantly influences crocodile attack rates at low crocodile population sizes, this relationship becomes weaker as the density increases. For estuarine crocodiles in the NT, a plateauing of attack risk occurred once crocodile density attained ~2 crocodiles per km of river, and we argue that this was because high crocodile densities instigated management (e.g. removal of bold animals, exclusion zones) and education initiates by the government (e.g. ‘Be crocwise’ campaign) that subsequently evoked a change in human behaviour around waterways and stabilised the attack rate. Read the free Plain Language Summary for this article on the Journal blog.

Aline de Souza Souto, Virgínia Carmem da Rocha Bezerra, Glaciene Mary da Silva Gonçalves et al.

Abstract The article analyzed the sugarcane-derived socioenvironmental conflicts and injustice in the Acaú-Goiana Extractive Reserve, Pernambuco, Brazil. A descriptive case study was conducted with rural workers based on social cartography and documents, analyzed under the EJAtlas categories. The Global Atlas of Environmental Justice (EJAtlas) is a web inventory that gathers environmental conflicts and injustice, serving as a basis for reporting affected territories. Even with the creation of the Reserve to protect natural assets and ecosystem life, new conflicts emerged from the installation of enterprises that put pressure on the territory and added to centuries-old problems such as sugarcane cultivation. Other impacts include air pollution, contamination of soil and water resources, biodiversity loss, food and nutritional insecurity, increased violence, human rights violations, and mental health problems.

Liqun Zhao, Keyan Miao, Konstantinos Gatsis et al.

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.

Ruican Zhong, Donghoon Shin, Rosemary Meza et al.

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.

Luca Castri, Gloria Beraldo, Sariah Mghames et al.

Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects. Modelling cause-and-effect relations through causal inference aids in predicting human behaviours and anticipating robot interventions. However, a critical challenge arises as existing causal discovery methods currently lack an implementation inside the ROS ecosystem, the standard de facto in robotics, hindering effective utilisation in robotics. To address this gap, this paper introduces ROS-Causal, a ROS-based framework for onboard data collection and causal discovery in human-robot spatial interactions. An ad-hoc simulator, integrated with ROS, illustrates the approach's effectiveness, showcasing the robot onboard generation of causal models during data collection. ROS-Causal is available on GitHub: https://github.com/lcastri/roscausal.git.

Takahiro Tsumura, Seiji Yamada

As anthropomorphic agents (AI and robots) are increasingly used in society, empathy and trust between people and agents are becoming increasingly important. A better understanding of agents by people will help to improve the problems caused by the future use of agents in society. In the past, there has been a focus on the importance of self-disclosure and the relationship between agents and humans in their interactions. In this study, we focused on the attributes of self-disclosure and the relationship between agents and people. An experiment was conducted to investigate hypotheses on trust and empathy with agents through six attributes of self-disclosure (opinions and attitudes, hobbies, work, money, personality, and body) and through competitive and cooperative relationships before a robotic agent performs a joint task. The experiment consisted of two between-participant factors: six levels of self-disclosure attributes and two levels of relationship with the agent. The results showed that the two factors had no effect on trust in the agent, but there was statistical significance for the attribute of self-disclosure regarding a person's empathy toward the agent. In addition, statistical significance was found regarding the agent's ability to empathize with a person as perceived by the person only in the case where the type of relationship, competitive or cooperative, was presented. The results of this study could lead to an effective method for building relationships with agents, which are increasingly used in society.

Luca Castri, Gloria Beraldo, Sariah Mghames et al.

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal

Catuxa Cerecedo-Iglesias, F. Bartumeus, A. Cortés‐Avizanda et al.

Background Changes in human-induced resource availability can alter the behaviour of free-living species and affect their foraging strategies. The future European Landfill Waste Directive and Circular Economy Action Plan will reduce the number of predictable anthropogenic food subsidies (PAFS), above all, by closing landfills to preclude negative effects on human health. Obligate avian scavengers, the most threatened group of birds worldwide, are the most likely group of species that will be forced to change their behaviour and use of space in response to landfill site closures. Here, we examine the possible consequences of these management decisions on the foraging patterns of Egyptian vultures ( Neophron percnopterus ) in an expanding population in the Iberian Peninsula. Methods We tracked 16 individuals in 2018–2021, including breeders and non-breeders, and, using a combination of spatial-use and spatial-network modelling, assessed landscape connectivity between key resources based on movement patterns. We then carried out simulations of future scenarios based on the loss of PAFS to predict likely changes in the movement patterns of both non-breeders and breeders. Results Our results show that foraging strategies in non-breeders and breeders differ significantly: non-breeders performed more dispersal movements than breeding birds across a spatial-use network. Non-breeding and breeding networks were found to be vulnerable to the removal of central foraging areas containing landfill sites, a highly predictable resource, while perturbation analysis showed dissimilar foraging responses to the gradual reduction of other predictable resources. Under a context of the non-availability of landfills for breeders and non-breeders, vultures will increase their use of extensive livestock as a trophic resource. Conclusions Future environmental policies should thus extend the areas used by scavengers in which livestock carcasses are allowed to remain in the wild, a strategy that will also mitigate the lack of food caused by any reduction in available waste if landfills close. In general, our results emphasize the capabilities of a spatial network approaches to address questions on movement ecology. They can be used to infer the behavioural response of animal species and, also demonstrate the importance of applying such approaches to endangered species conservation within a context of changing humanized scenarios.

D. Harris, D. Santos

One of political ecology's main strengths is its emphasis on critique, and, through critique, its ability to better understand nature-society relations. Recently, calls have been made from within the sub-discipline – and from the social sciences more broadly – to move beyond critique, to engage nature-society relations more experimentally. Experimental approaches to nature-society relations invite new techniques and methods to study issues as they emerge, as opposed to those that have already happened. To this end, much has been written about the limits of human reasoning and understanding in the face of large-scale environmental crises like climate change. Complementing experimental sensibilities, speculative approaches to nature-society relations engage directly in the politics of expanding imaginative, perspectival, and political capacity in the face of these changes. The aim of this article is threefold. First, we will highlight scholarship that informs already existing approaches to experimental and speculative political ecologies, tying these threads together to elucidate a larger research agenda. Second, by way of example, we will discuss two case studies – solar's role in Colorado's 'just transition' and speculative climate futures and CRISPR-based gene drives and environmental management – to inform our discussion. Finally, this article serves as the introduction to a Special Section, in which we will outline and connect three articles that point towards how political ecology can be done with an eye more explicitly trained towards the future.

O. Buzhdygan, S. Meyer, W. Weisser et al.