Hasil untuk "Human ecology. Anthropogeography"

Jianguo Wu, R. Hobbs

Zhuoqun Jiang, ShunYi Yeo, Dorien Herremans et al.

While reciprocal self-disclosure drives intimacy, digital tools seldom scaffold autonomy, competence, and relatedness -- the motivational underpinnings defined by Self-Determination Theory (SDT) that enable deep exchange. We introduce a chatbot employing dual-layer scaffolding to satisfy these needs: first providing enabling affordances (instrumental support) for vulnerability, then mediating affordances (relational support) for responsiveness. In a randomized study (N = 72; 36 couples) comparing Partner Support (PS: both layers), Direct Support (DS: enabling only), and Basic Prompt (BP: questions only), results reveal a critical distinction. While enabling affordances (PS, DS) were sufficient to deepen disclosure, only mediating affordances (PS) reliably elicited partner-provided need support and increased perceived closeness. Furthermore, controlled motivation decreased across conditions, and scaffolding buffered vitality, which remained stagnant in BP. We contribute empirical evidence that SDT-guided mediation fosters connection, offering a practical framework for designing AI-mediated conversations that support, rather than replace, human intimacy.

C. Vaughn, T. Hoellein

Bivalve molluscs are abundant in marine and freshwater ecosystems and perform important ecological functions. Bivalves have epifaunal or infaunal lifestyles but are largely filter feeders that couple the water column and benthos. Bivalve ecology is a large field of study, but few comparisons among aquatic ecosystems or lifestyles have been conducted. Bivalves impact nutrient cycling, create and modify habitat, and affect food webs directly (i.e., prey) and indirectly (i.e., movement of nutrients and energy). Materials accumulated in soft tissue and shells are used as environmental monitors. Freshwater mussel and oyster aggregations in rivers and estuaries are hot spots for biodiversity and biogeochemical transformations. Historically, human use includes food, tools, currency, and ornamentation. Bivalves provide direct benefits to modern cultures as food, building materials, and jewelry and provide indirect benefits by stabilizing shorelines and mitigating nutrient pollution. Research on bivalve-mediated ecological processes is diverse, and future synthesis will require collaboration across conventional disciplinary boundaries.

Jason Rohr, D. Civitello, F. Halliday et al.

The disease ecology community has struggled to come to consensus on whether biodiversity reduces or increases infectious disease risk, a question that directly affects policy decisions for biodiversity conservation and public health. Here, we summarize the primary points of contention regarding biodiversity–disease relationships and suggest that vector-borne, generalist wildlife and zoonotic pathogens are the types of parasites most likely to be affected by changes to biodiversity. One synthesis on this topic revealed a positive correlation between biodiversity and human disease burden across countries, but as biodiversity changed over time within these countries, this correlation became weaker and more variable. Another synthesis—a meta-analysis of generally smaller-scale experimental and field studies—revealed a negative correlation between biodiversity and infectious diseases (a dilution effect) in various host taxa. These results raise the question of whether biodiversity–disease relationships are more negative at smaller spatial scales. If so, biodiversity conservation at the appropriate scales might prevent wildlife and zoonotic diseases from increasing in prevalence or becoming problematic (general proactive approaches). Further, protecting natural areas from human incursion should reduce zoonotic disease spillover. By contrast, for some infectious diseases, managing particular species or habitats and targeted biomedical approaches (targeted reactive approaches) might outperform biodiversity conservation as a tool for disease control. Importantly, biodiversity conservation and management need to be considered alongside other disease management options. These suggested guiding principles should provide common ground that can enhance scientific and policy clarity for those interested in simultaneously improving wildlife and human health. There has been intense debate as to whether biodiversity increases or reduces the risk of infectious disease. This Review is the result of researchers from both sides of the debate attempting to reach a consensus.

E. Darling, Emily S. Darling, E. Darling et al.

Pinar Pamukcu-Albers, F. Ugolini, Daniele La Rosa et al.

The looming climate crisis and the ongoing COVID-19 pandemic have highlighted the importance of green infrastructure in and around cities, prompting an urgent call for more functional and sustainable urban planning and design. A number of recent studies have shown that green infrastructure offers a wide range of ecosystem functions and services essential to human wellbeing and urban sustainability (O’Brien et al. 2017; Staddon et al. 2018) which are of particular relevance under climatic and health crises. In this editorial we stress the importance of the existing green infrastructure to withstand climate change-induced stresses, namely those related to increasing climate variability and extreme temperature and precipitation events, and to contribute to human physical and mental health of urban dwellers during lockdown periods. In both cases, green infrastructure plays a major role in providing urban areas with resilience capacity that is key to urban sustainability. We also highlight the need to expand and improve green infrastructure, in particular in regions that are more vulnerable, based on integrative and participatory processes. This editorial was motivated by a webinar organized by the IUFRO (International Union of Forest Research Organizations) Landscape Ecology Working Party (https://iufrole-wp.weebly.com/) held on November 17th, 2020.

Haotian Li, Yun Wang, Huamin Qu

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Aimee Allen, Tom Drummond, Dana Kulić

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.

Yuxuan Xue, Xianghui Xie, Margaret Kostyrko et al.

Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in scale and diversity. The central question we address in this paper is: Can existing foundation models be distilled to generate theoretically unbounded, richly annotated 3D human data? We introduce InfiniHuman, a framework that synergistically distills these models to produce richly annotated human data at minimal cost and with theoretically unlimited scalability. We propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. User study shows our automatically generated identities are undistinguishable from scan renderings. InfiniHumanData contains 111K identities spanning unprecedented diversity. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body-shape parameters. Building on this dataset, we propose InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate significant improvements over state-of-the-art methods in visual quality, generation speed, and controllability. Our approach enables high-quality avatar generation with fine-grained control at effectively unbounded scale through a practical and affordable solution. We will publicly release the automatic data generation pipeline, the comprehensive InfiniHumanData dataset, and the InfiniHumanGen models at https://yuxuan-xue.com/infini-human.

Azizul Zahid, Jie Fan, Farong Wang et al.

Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation learning within unstructured environments. We propose a multimodal demonstration learning framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space. Focusing on the "pick and place" task from the RH20T dataset, we utilize data from 5 users across 10 diverse scenes. Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction. After 2000 training epochs, the human model reaches 71.67% accuracy, and the robot model achieves 71.8% accuracy, demonstrating the framework's potential for aligning complex, multimodal human and robot behaviors in manipulation tasks.

Dallin L. Cordon, Shaden Moss, Marc Killpack et al.

This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft manipulator mounted to an omni-directional mobile base. The system level configuration of the robot and design of the collaborative manipulation (co-manipulation) study are presented. The initial results, both quantitative and qualitative, are directly compared to previous similar human-human co-manipulation studies. These initial results show promise in the ability for large-scale soft robots to perform comparably to human partners acting as non-visual followers in a co-manipulation task. Furthermore, these results challenge traditional soft robot strength limitations and indicate potential for applications requiring strength and adaptability.

Yibowen Zhao, Yiming Cao, Zhiqi Shen et al.

Intensive Care Units (ICUs) are critical environments characterized by high-stakes monitoring and complex data management. However, current practices often rely on manual data transcription and fragmented information systems, introducing potential risks to patient safety and operational efficiency. To address these issues, we propose a human-AI synergy system based on a cloud-edge-end architecture, which integrates visual-aware data extraction and semantic interaction mechanisms. Specifically, a visual-aware edge module non-invasively captures real-time physiological data from bedside monitors, reducing manual entry errors. To improve accessibility to fragmented data sources, a semantic interaction module, powered by a Large Language Model (LLM), enables physicians to perform efficient and intuitive voice-based queries over structured patient data. The hierarchical cloud-edge-end deployment ensures low-latency communication and scalable system performance. Our system reduces the cognitive burden on ICU nurses and physicians and demonstrates promising potential for broader applications in intelligent healthcare systems.

Nthiwa Alex Ngolanye, Kisovi Leornard, Kibutu Thomas et al.

Abstract In modern times, cities around the world have grappled with the challenges of racial and ethnic segregation. In Nairobi city, with its diverse ethnic makeup, there is widening inequalities and emerging patterns of ethnic segregation, where the five main ethnic groups - Kamba, Luo, Kikuyu, Luhyia, and Kisii - experience varying levels of spatial concentration. This study analysed the spatial patterns of ethnic segregation in Nairobi, using geocoded questionnaire data from the 2019 Kenya population and housing census data. We used the Index of Dissimilarity in STATA software and Geo-segregation Analyzer and Anselin’s Local Moran I method in GIS to map ethnic segregation patterns. Our findings uncovered a striking socio-spatial divide based on ethnicity. Anselin Local Moran’s I indicators further pinpointed areas with the highest levels of segregation and spatial clustering of specific ethnic groups. These findings offer crucial insights for urban planners and policymakers. By pinpointing areas experiencing the most severe spatial segregation, our research could inform spatially targeted interventions and resource allocation. This could inform policies that foster inclusivity, reduce spatial inequalities, and build a more equitable and socially cohesive city.

Christin Juliana Müller, Sarah Karic

Over the past two decades, e‑participation has become increasingly relevant as a result of digitization and the evolution of information and communication technologies. Yet the perspectives of providers and users with regard to the requirements on an e‑participation system are not sufficiently considered jointly. This paper investigates the requirements and challenges of users and providers. The study is based on a mixed methods approach with an online survey of those responsible for e‑participation processes in various administrative and planning areas as well as semi-structured expert interviews. In addition, we conducted proband tests to investigate the usability of digital citizen participation tools. The results show that accessibility, retrievability, effectiveness, interaction in the digital arena, security, technical specifications, resources, media literacy and the use of participation as a basis for profound decision-making are key requirements for e‑participation systems. These demands requirements are interrelated and influence the quality and effectiveness of participation processes. To meet these requirements, we propose an e‑participation ecosystem that integrates the different dimensions of digital participation and takes into account the interaction between actors, demands and contextual conditions.

Jianbin Jiao, Xina Cheng, Weijie Chen et al.

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as human-computer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable self-attention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multi-perspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on this dataset. The source code will be available at https://github.com/WUJINHUAN/3D-human-pose.

Kristian González Barman, Simon Lohse, Henk de Regt

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.

Jianyuan Ni, Hao Tang, Syed Tousiful Haque et al.

The combination of increased life expectancy and falling birth rates is resulting in an aging population. Wearable Sensor-based Human Activity Recognition (WSHAR) emerges as a promising assistive technology to support the daily lives of older individuals, unlocking vast potential for human-centric applications. However, recent surveys in WSHAR have been limited, focusing either solely on deep learning approaches or on a single sensor modality. In real life, our human interact with the world in a multi-sensory way, where diverse information sources are intricately processed and interpreted to accomplish a complex and unified sensing system. To give machines similar intelligence, multimodal machine learning, which merges data from various sources, has become a popular research area with recent advancements. In this study, we present a comprehensive survey from a novel perspective on how to leverage multimodal learning to WSHAR domain for newcomers and researchers. We begin by presenting the recent sensor modalities as well as deep learning approaches in HAR. Subsequently, we explore the techniques used in present multimodal systems for WSHAR. This includes inter-multimodal systems which utilize sensor modalities from both visual and non-visual systems and intra-multimodal systems that simply take modalities from non-visual systems. After that, we focus on current multimodal learning approaches that have applied to solve some of the challenges existing in WSHAR. Specifically, we make extra efforts by connecting the existing multimodal literature from other domains, such as computer vision and natural language processing, with current WSHAR area. Finally, we identify the corresponding challenges and potential research direction in current WSHAR area for further improvement.

Houjiang Liu, Jacek Gwizdka, Matthew Lease

Given the volume of potentially false claims online, claim prioritization is essential in allocating limited human resources available for fact-checking. In this study, we perceive claim prioritization as an information retrieval (IR) task: just as multidimensional IR relevance, with many factors influencing which search results a user deems relevant, checkworthiness is also multi-faceted, subjective, and even personal, with many factors influencing how fact-checkers triage and select which claims to check. Our study investigates both the multidimensional nature of checkworthiness and effective tool support to assist fact-checkers in claim prioritization. Methodologically, we pursue Research through Design combined with mixed-method evaluation. Specifically, we develop an AI-assisted claim prioritization prototype as a probe to explore how fact-checkers use multidimensional checkworthy factors to prioritize claims, simultaneously probing fact-checker needs and exploring the design space to meet those needs. With 16 professional fact-checkers participating in our study, we uncover a hierarchical prioritization strategy fact-checkers implicitly use, revealing an underexplored aspect of their workflow, with actionable design recommendations for improving claim triage across multidimensional checkworthiness and tailoring this process with LLM integration.

Scott A. Humr, Mustafa Canan, Mustafa Demir

Intelligent autonomous systems are part of a system of systems that interact with other agents to accomplish tasks in complex environments. However, intelligent autonomous systems integrated system of systems add additional layers of complexity based on their limited cognitive processes, specifically shared situation awareness that allows a team to respond to novel tasks. Intelligent autonomous systems' lack of shared situation awareness adversely influences team effectiveness in complex task environments, such as military command-and-control. A complementary approach of shared situation awareness, called situations theory, is beneficial for understanding the relationship between system of systems shared situation awareness and effectiveness. The current study elucidates a conceptual discussion on situations theory to investigate the development of an system of systems shared situational awareness when humans team with intelligent autonomous system agents. To ground the discussion, the reviewed studies expanded situations theory within the context of a system of systems that result in three major conjectures that can be beneficial to the design and development of future systems of systems.

Cécile Sarabian, A. Wilkinson, M. Sigaud et al.

Disgust is an adaptive system hypothesized to have evolved to reduce the risk of becoming sick. It is associated with behavioural, cognitive and physiological responses tuned to allow animals to avoid and/or get rid of parasites, pathogens and toxins. Little is known about the mechanisms and outcomes of disease avoidance in wild animals. Furthermore, given the escalation of negative human-wildlife interactions, the translation of such knowledge into the design of evolutionarily relevant conservation and wildlife management strategies is becoming urgent. Contemporary methods in animal ecology and related fields, using direct (sensory cues) or indirect (remote sensing technologies and machine learning) means, provide a flexible toolbox for testing and applying disgust at individual and collective levels. In this review/perspective paper, we provide an empirical framework for testing the adaptive function of disgust and its associated disease avoidance behaviours across species, from the least to the most social, in different habitats. We predict various trade-offs to be at play depending on the social system and ecology of the species. We propose five contexts in which disgust-related avoidance behaviours could be applied, including endangered species rehabilitation, invasive species, crop-raiding, urban pests and animal tourism. We highlight some of the perspectives and current challenges of testing disgust in the wild. In particular, we recommend future studies to consider together disease, predation and competition risks. We discuss the ethics associated with disgust experiments in the above contexts. Finally, we promote the creation of a database gathering disease avoidance evidence in animals and its applications.