Hasil untuk "Human ecology. Anthropogeography"

D. Wilson, A. Clark, K. Coleman et al.

SangYeop Jeong, Yeongseo Na, Seung Gyu Jeong et al.

In VR interactions with embodied conversational agents, users' emotional intent is often conveyed more by how something is said than by what is said. However, most VR agent pipelines rely on speech-to-text processing, discarding prosodic cues and often producing emotionally incongruent responses despite correct semantics. We propose an emotion-context-aware VR interaction pipeline that treats vocal emotion as explicit dialogue context in an LLM-based conversational agent. A real-time speech emotion recognition model infers users' emotional states from prosody, and the resulting emotion labels are injected into the agent's dialogue context to shape response tone and style. Results from a within-subjects VR study (N=30) show significant improvements in dialogue quality, naturalness, engagement, rapport, and human-likeness, with 93.3% of participants preferring the emotion-aware agent.

Asiri Dalugoda

Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents the Human Delegation Provenance (HDP) protocol, a lightweight token-based scheme that cryptographically captures and verifies human authorization context in multi-agent systems. An HDP token binds a human authorization event to a session, records each agent's delegation action as a signed hop in an append-only chain, and enables any participant to verify the full provenance record using only the issuer's Ed25519 public key and the current session identifier. Verification is fully offline, requiring no registry lookups or third-party trust anchors. We situate HDP within the existing landscape of delegation protocols, identify its distinct design point relative to OAuth 2.0 Token Exchange (RFC 8693), JSON Web Tokens (RFC 7519), UCAN, and the Intent Provenance Protocol (draft-haberkamp-ipp-00), and demonstrate that existing standards fail to address the multi-hop, append-only, human-provenance requirements of agentic systems. HDP has been published as an IETF Internet-Draft (draft-helixar-hdp-agentic-delegation-00) and a reference TypeScript SDK is publicly available.

Moustapha El Outmani, Manthan Venkataramana Shenoy, Ahmad Hatahet et al.

Automated regression testing is essential for maintaining rapid, high-quality delivery in Agile and Scrum organizations. Many teams, including Hacon (a Siemens company), face a persistent gap: validated test specifications accumulate faster than they are automated, limiting regression coverage and increasing manual work. This paper reports an exploratory industrial case study of the Hacon Test Automation Copilot, an agentic AI system that generates system-level regression test scripts from validated specifications using retrieval-augmented generation and a multi-agent workflow. Integrated with Hacon's CI pipelines, the Copilot operates asynchronously as a "silent AI teammate", producing candidate scripts for human review. Mixed-method evaluation shows the AI accelerates script authoring and increases throughput, with 30-50% code reuse. However, human review remains necessary for maintainability and correct domain interpretation. Clear specifications, explicit governance, and ongoing human-AI collaboration are critical. We conclude with lessons for scaling regression automation and enabling effective human-AI teaming in Agile settings.

En Wu, Mengjun Shen, Xue-e Chai

In the context of global sustainable development, the sustainable construction of urban parks has attracted much attention. The developing countries represented by China have insufficient experience in park construction. The park is mainly for sightseeing and lacks the methods of sustainable park construction. At present, studies on the sustainability of urban parks are mostly carried out from the perspectives of index evaluation and expert evaluation, and few are considered from the perspective of citizens' use. There are also few studies that establish correlation between sustainability and well-being for unified analysis. This study investigates the correlation between the sustainability of urban parks and recreational well-being from the citizens’ perspective, based on which a correlation model was constructed. Suggestions for the planning, design, construction, and management of sustainable urban parks (to improve recreational well-being) are provided. The Beijing Olympic Forest Park in China was investigated as a case study, and 444 valid questionnaires were used in a structural equation model analysis for empirical tests. The results show that: (1) The sustainability perception of urban parks can significantly positively affect the recreational well-being of citizens (2) The intensity of the impacts of park sustainability assessment factors on recreational well-being was as follows: sense of place > leisure and health > economic perception > green construction and nature conservation > education value > natural environment > infrastructure. The construction of parks should not only improve the ecological environment, but also consider green construction, nature protection, and the functions of space and facilities, which can increase surrounding business opportunities. Moreover, stakeholders should consider the protection and shaping of the sense of place so as to build people’s emotional connection to urban parks.

Md Mofijul Islam, Alexi Gladstone, Sujan Sarker et al.

As robots enter human workspaces, there is a crucial need for them to comprehend embodied human instructions, enabling intuitive and fluent human-robot interaction (HRI). However, accurate comprehension is challenging due to a lack of large-scale datasets that capture natural embodied interactions in diverse HRI settings. Existing datasets suffer from perspective bias, single-view collection, inadequate coverage of nonverbal gestures, and a predominant focus on indoor environments. To address these issues, we present the Refer360 dataset, a large-scale dataset of embodied verbal and nonverbal interactions collected across diverse viewpoints in both indoor and outdoor settings. Additionally, we introduce MuRes, a multimodal guided residual module designed to improve embodied referring expression comprehension. MuRes acts as an information bottleneck, extracting salient modality-specific signals and reinforcing them into pre-trained representations to form complementary features for downstream tasks. We conduct extensive experiments on four HRI datasets, including the Refer360 dataset, and demonstrate that current multimodal models fail to capture embodied interactions comprehensively; however, augmenting them with MuRes consistently improves performance. These findings establish Refer360 as a valuable benchmark and exhibit the potential of guided residual learning to advance embodied referring expression comprehension in robots operating within human environments.

Jan Batzner, Volker Stocker, Stefan Schmid et al.

Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficulties in distinguishing sycophantic responses from related concepts in AI alignment and offers actionable recommendations for future research.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Maxime Clenet, Maxime Dion, F. Guillaume Blanchet

With increased access to data and the advent of computers, the use of statistical tools and numerical simulations is becoming commonplace for ecologists. These approaches help improve our understanding of ecological phenomena and their underlying mechanisms in increasingly complex environments. However, the development of mathematical and computational tools has made it possible to study high-dimensional problems up to a certain limit. To overcome this issue, quantum computers could be used to study ecological problems on a larger scale by creating new bridges between fields that at first glance appear to be quite different. We introduce the basic concepts needed to understand quantum computers, give an overview of their applications, and discuss their challenges and future opportunities in ecology. Quantum computers will have a significant impact on ecology by improving the power of statistical tools, solve intractable problems in networks, and help understand the dynamics of large systems of interacting species. This innovative computational perspective could redefine our understanding of species interactions, improve predictive modeling of distributions, and optimize conservation strategies, thereby advancing the field of ecology into a new era of discovery and insight.

Luisa Eusse‐Villa, Cristiano Franceschinis, Viola Di Cori et al.

Abstract Forests contribute to human well‐being by offering various ecosystem services (ES), including wild food and other products. While previous studies have typically focused on formally marketed wild foods, there is a growing need to understand the broader significance of wild foods as cultural ES and the factors influencing societal preferences for their supply and maintenance. We conducted a study in Italy, a country with a rich cultural heritage associated with wild food, using data from a discrete choice experiment to analyse how people value wild food (specifically, mushrooms, wild berries and wild herbs) and map their preferences. Our findings revealed respondents' willingness to allocate resources to forest programmes that increase and conserve wild foods, indicating their high‐perceived value as ES. We found that regional traditions are a key motivation for collecting wild food, and that respondents typically collect within their regions. The results highlight the importance of integrating regional spatial dynamics to comprehensively understand societal preferences for ES, particularly in the context of local food systems. Read the free Plain Language Summary for this article on the Journal blog.

Ziwen He, Xiao Feng, Qipian Chen et al.

Mengyuan Liu, Chen Chen, Songtao Wu et al.

Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of video analysis and human-robot interaction. Considering the success of graph convolution in modeling topology-aware features from skeleton data, recent methods commonly operate graph convolution on separate entities and use late fusion for interactive action recognition, which can barely model the mutual semantic relationships between pairwise entities. To this end, we propose a mutual excitation graph convolutional network (me-GCN) by stacking mutual excitation graph convolution (me-GC) layers. Specifically, me-GC uses a mutual topology excitation module to firstly extract adjacency matrices from individual entities and then adaptively model the mutual constraints between them. Moreover, me-GC extends the above idea and further uses a mutual feature excitation module to extract and merge deep features from pairwise entities. Compared with graph convolution, our proposed me-GC gradually learns mutual information in each layer and each stage of graph convolution operations. Extensive experiments on a challenging hand-to-hand interaction dataset, i.e., the Assembely101 dataset, and two large-scale human-to-human interaction datasets, i.e., NTU60-Interaction and NTU120-Interaction consistently verify the superiority of our proposed method, which outperforms the state-of-the-art GCN-based and Transformer-based methods.

Benedetta Matcovich, Cristina Gena, Fabiana Vernero

In recent years, robotics has evolved, placing robots in social contexts, and giving rise to Human-Robot Interaction (HRI). HRI aims to improve user satisfaction by designing autonomous social robots with user modeling functionalities and user-adapted interactions, storing data on people to achieve personalized interactions. Personality, a vital factor in human interactions, influences temperament, social preferences, and cognitive abilities. Despite much research on personality traits influencing human-robot interactions, little attention has been paid to the influence of the robot's personality on the user model. Personality can influence not only temperament and how people interact with each other but also what they remember about an interaction or the person they interact with. A robot's personality traits could therefore influence what it remembers about the user and thus modify the user model and the consequent interactions. However, no studies investigating such conditioning have been found. This paper addresses this gap by proposing distinct user models that reflect unique robotic personalities, exploring the interplay between individual traits, memory, and social interactions to replicate human-like processes, providing users with more engaging and natural experiences

Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun et al.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.

Olga A. Bogatova

Based on the analysis of the results of qualitative and quantitative sociological research, the article characterises the influence of the center-peripheral stratification of regions on the stability degree of the social identity of the population in the capitals of the republics within the Russian Federation – on the example of Saransk, the administrative center of the Republic of Mordovia, and Izhevsk, the administrative center of the Udmurt Republic. The author evaluates the differences in the central-peripheral self-identification of the elites and the population of the republics, due to differences in the “scale effect” of the resource provision of the republics, as a significant component of the metropolitan identity of the central cities of the republics as “centers for the implementation of other people’s initiatives.” For example, the status of Izhevsk as the largest city is expressed not only in its comparison to medium-sized cities in the Udmurt Republic, but also in competition with large industrial cities in other regions. The contrast example of the negative impact of the scale effect is demonstrated by Mordovia as a relatively small (with a population of less than a million people) and low-resource region with its capital city of Saransk. The formation of a highly polarized population structure in such a region with a single large city in the absence of medium-sized ones does not prevent a negative comparison with the capital cities of more developed and large regions, forming an idea of their own non-competitiveness and periphery in relation to the largest cities, along with a willingness to join the administrative regions they manage, even at the cost of losing their central status. The results of the study explain the phenomenon of blurring the republican identity among the population of the capital cities of some republics and the dysfunction in their social development, which is expressed in their transformation from the “locomotives of modernization” of the republics into the donors of human resources for more developed regions.

Marianthi Tangili, Annabel J Slettenhaar, Joanna Sudyka et al.

Inferring the chronological and biological age of individuals is fundamental to population ecology and our understanding of ageing itself, its evolution, and the biological processes that affect or even cause ageing. Epigenetic clocks based on DNA methylation (DNAm) at specific CpG sites show a strong correlation with chronological age in humans, and discrepancies between inferred and actual chronological age predict morbidity and mortality. Recently, a growing number of epigenetic clocks have been developed in non‐model animals and we here review these studies. We also conduct a meta‐analysis to assess the effects of different aspects of experimental protocol on the performance of epigenetic clocks for non‐model animals. Two measures of performance are usually reported, the R2 of the association between the predicted and chronological age, and the mean/median absolute deviation (MAD) of estimated age from chronological age, and we argue that only the MAD reflects accuracy. R2 for epigenetic clocks based on the HorvathMammalMethylChip4 was higher and the MAD scaled to age range lower, compared with other DNAm quantification approaches. Scaled MAD tended to be lower among individuals in captive populations, and decreased with an increasing number of CpG sites. We conclude that epigenetic clocks can predict chronological age with relatively high accuracy, suggesting great potential in ecological epigenetics. We discuss general aspects of epigenetic clocks in the hope of stimulating further DNAm‐based research on ageing, and perhaps more importantly, other key traits.

D. Srivastava, Noam Harris, Nadia B. Páez et al.

Cities can have profound impacts on ecosystems, yet our understanding of these impacts is currently limited. First, the effects of socioeconomic dimensions of human society are often overlooked. Second, correlative analyses are common, limiting our causal understanding of mechanisms. Third, most research has focused on terrestrial systems, ignoring aquatic systems that also provide important ecosystem services. Here we compare the effects of human population density and low-income prevalence on the macroinvertebrate communities and ecosystem processes within water-filled artificial tree holes. We hypothesized that these human demographic variables would affect tree holes in different ways via changes in temperature, water nutrients, and the local tree hole environment. We recruited community scientists across Greater Vancouver (Canada) to provide host trees and tend 50 tree holes over 14 weeks of colonization. We quantified tree hole ecosystems in terms of aquatic invertebrates, litter decomposition, and chlorophyll-a. We compiled potential explanatory variables from field measurements, satellite images or census databases. Using structural equation models, we showed that invertebrate abundance was affected by low-income prevalence but not human population density. This was driven by cosmopolitan species of Ceratopogonidae (Diptera) with known associations to anthropogenic containers. Invertebrate diversity and abundance were also affected by environmental factors, such as temperature, elevation, water nutrients, litter quantity, and exposure. By contrast, invertebrate biomass, chlorophyll-a, and litter decomposition were not affected by any measured variables. In summary, this study shows that some urban ecosystems can be largely unaffected by human population density. Our study also demonstrates the potential of using artificial tree holes as a standardized, replicated habitat for studying urbanization. Finally, by combining community science and urban ecology, we were able to involve our local community in this pandemic research pivot. This article is protected by copyright. All rights reserved.

Zhenzhi Wang, Jingbo Wang, Yixuan Li et al.

Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human interactions. In our approach, we strive to explore this problem by synthesizing human motion with interactions for a group of characters of any size in a zero-shot manner. The key aspect of our approach is the adaptation of human-wise interactions as pairs of human joints that can be either in contact or separated by a desired distance. In contrast to existing methods that necessitate training motion generation models on multi-human motion datasets with a fixed number of characters, our approach inherently possesses the flexibility to model human interactions involving an arbitrary number of individuals, thereby transcending the limitations imposed by the training data. We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. It consists of a motion controller and an inverse kinematics guidance module that realistically and accurately aligns the joints of synthesized characters to the desired location. Furthermore, we demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model (LLM). Experimental results highlight the capability of our framework to generate interactions with multiple human characters and its potential to work with off-the-shelf physics-based character simulators. Code is available at https://github.com/zhenzhiwang/intercontrol

Jared Flowers, Marco Faroni, Gloria Wiens et al.

This paper addresses human-robot collaboration (HRC) challenges of integrating predictions of human activity to provide a proactive-n-reactive response capability for the robot. Prior works that consider current or predicted human poses as static obstacles are too nearsighted or too conservative in planning, potentially causing delayed robot paths. Alternatively, time-varying prediction of human poses would enable robot paths that avoid anticipated human poses, synchronized dynamically in time and space. Herein, a proactive path planning method, denoted STAP, is presented that uses spatiotemporal human occupancy maps to find robot trajectories that anticipate human movements, allowing robot passage without stopping. In addition, STAP anticipates delays from robot speed restrictions required by ISO/TS 15066 speed and separation monitoring (SSM). STAP also proposes a sampling-based planning algorithm based on RRT* to solve the spatio-temporal motion planning problem and find paths of minimum expected duration. Experimental results show STAP generates paths of shorter duration and greater average robot-human separation distance throughout tasks. Additionally, STAP more accurately estimates robot trajectory durations in HRC, which are useful in arriving at proactive-n-reactive robot sequencing.

Luciana L. Couso, Alfonso Soler-Bistue, Ariel A. Aptekmann et al.

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We are able to interpret microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a classical laboratory tool of bacterial physiology. Monod curves relate the specific growth rate of a microbe with the concentration of a limiting nutrient, and provide quantities that directly correspond to key ecological parameters in McArthur and Wilsons r/K selection theory, Tilmans resource competition and community structure theory and Grimes triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We analyzed some ecological context by considering the known viable carbon sources for heterotrophic microbes in the framework of community structure theory. This partly explains the microbial diversity observed using metagenomics. In sum, ecology theory in combination with Monod curves can be a unifying quantitative framework for the study of natural microbial communities, calling for the integration of modern laboratory and field experiments.