Hasil untuk "Human ecology. Anthropogeography"

A. Sih, J. Cote, M. Evans et al.

R. Seidl, T. Spies, D. Peterson et al.

Mak Ahmad, Andrew Macvean, JJ Geewax et al.

Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals (AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly "perfect." We characterize this as a "Perfection Paradox" -- where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer's role from the "drafter" of specifications to the "curator" of AI-generated patterns.

Shing Yan Li, Zhijie Feng, Akshit Goyal et al.

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the classic theoretical results of population genetics (e.g., Kimura's fixation formula) largely ignore ecological effects. Here, we address this shortcoming by using dynamical mean-field theory to integrate ecology into classical population genetics models. We show that ecological interactions between parents and mutants result in frequency-dependent selection and can be characterized by a single emergent parameter that measures the strength of ecological feedbacks. We derive an explicit analytic expression for the fixation probability that generalizes Kimura's famous result and analyze it in various ecological and evolutionary limits. We find that ecological interactions suppress fixation probabilities for moderately beneficial mutants when compared to Kimura's predictions, with the strength of suppression increasing with larger effective population sizes, greater selective differences between parent and mutant, and for ecosystems with a large number of "open niches" (i.e., ecosystems well below the packing bound). Frequency-dependent selection also gives rise to prolonged parent-mutant coexistence in complex communities, a phenomenon absent in classical population genetics. Our study establishes a framework for integrating ecological interactions into population genetics models and helps illuminate how ecology can affect evolutionary outcomes.

Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu et al.

Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliability due to hallucinations, difficulty in handling complex tasks, and substantial safety and ethical risks, all of which limit their feasibility and trustworthiness in real-world applications. To overcome these limitations, LLM-based human-agent systems (LLM-HAS) incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability and safety. These human-agent collaboration systems enable humans and LLM-based agents to collaborate effectively by leveraging their complementary strengths. This paper provides the first comprehensive and structured survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment & profiling, human feedback, interaction types, orchestration and communication, explores emerging applications, and discusses unique challenges and opportunities arising from human-AI collaboration. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-Human-Agent-Collaboration-Interaction-Systems.

Chitralekha Gupta, Hanjun Wu, Praveen Sasikumar et al.

Wearable devices are transforming human capabilities by seamlessly augmenting cognitive functions. In this position paper, we propose a voice-based, interactive learning companion designed to amplify and extend cognitive abilities through informal learning. Our vision is threefold: (1) to enable users to discover new knowledge on-the-go through contextual interactive quizzes, fostering critical thinking and mindfulness, (2) to proactively detect misinformation, empowering users to critically assess information in real time, and (3) to provide spoken language correction and prompting hints for second language learning and effective communication. As an initial step toward this vision, we present Factually - a proactive, wearable fact-checking system integrated into devices like smartwatches or rings. Factually discreetly alerts users to potential falsehoods via vibrotactile feedback, helping them assess information critically. We demonstrate its utility through three illustrative scenarios, highlighting its potential to extend cognitive abilities for real-time misinformation detection. Early qualitative feedback suggests that Factually can enhance users' fact-checking capabilities, offering both practical and experiential benefits.

Kimiora Raerino, Rau Hoskins, Kara Beckford et al.

Indigenous photovoice was utilised to explore the iwi cultural landscapes of Ngā Hau Māngere, Aotearoa New Zealand, through the unique perspectives of ten Mana Whenua photographers. This study demonstrates how iwi cultural landscapes, such as signage, buildings, artworks, and ‘nature’ features, function as vital expressions and facilitators of iwi identity, history, and values. By employing Te Aranga Design Principles as an analytic tool, the research provides insights into the significance of iwi cultural landscapes, their meanings, and their role in evoking emotional and cultural connections. The findings highlight the importance of these landscapes in reinforcing Indigenous identity and sovereignty, strengthening community resilience, and promoting wellbeing. Advocating for Mana Whenua co-design and collaboration, this study underscores the urgent need for genuine engagement to create more inclusive and culturally responsive urban spaces while contributing to global discussions on integrating and grounding Indigenous knowledge into urban design and planning.

Ester Gruppelli Kurz, William Daldegan

A pesquisa investiga como o BRICS acomoda os interesses chineses na economia-mundo capitalista. O objetivo é compreender de que forma o grupo pode contribuir para um possível próximo Ciclo Sistêmico de Acumulação liderado pela China. São examinadas as Declarações de Cúpula do BRICS, os Planos Quinquenais e os Relatórios do Congresso do Partido Comunista da China desde 2011. A hipótese é que, por meio do BRICS, a China constrói consenso internacional, elemento necessário à hegemonia. O grupo reforça a política externa chinesa ao projetar a imagem de um país em desenvolvimento preocupado com a periferia e disposto a assumir papel ativo na governança global. Nesse processo, a China oferta bens públicos internacionais, como estabilidade econômica, segurança, proteção ambiental e infraestrutura.

Thomas O. Okimi

Abstract The construction sector faces increasing pressure to integrate sustainable supply chain practices, yet traditional collaboration methods often fall short in addressing stakeholder complexity, resource inefficiencies, and environmental impacts. This study explores how human-centric digital twins can enhance collaboration and promote sustainability throughout construction supply chains. Through a systematic review of Scopus-indexed publications, industry reports, and conference proceedings, the research identifies key factors for collaboration, evaluates the capabilities of digital twins, and measures their impact on sustainability. Results show that human-centric digital twins enable real-time communication, shared information, and collaborative decision-making. The comprehensive view of the supply chain is provided, allowing for bottleneck detection, scenario simulation, and behavioral analysis by incorporating human factors. Challenges such as data interoperability, cybersecurity, and workforce readiness remain. The study suggests a phased implementation approach focused on stakeholder engagement, data standardization, and user-centered design. Ongoing monitoring and evaluation are vital for achieving long-term sustainability goals. By addressing implementation barriers, construction firms can unlock the full benefits of human-centric digital twins, turning supply chain collaboration into a driver of resilience, efficiency, and environmental stewardship.

Morten Roed Frederiksen, Katrin Fischer, Maja Matarić

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot's affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot's affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants' willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants' willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.

Mohamad Shaaban, Simone Macci`o, Alessandro Carf`ı et al.

This article investigates mixed reality (MR) to enhance human-robot collaboration (HRC). The proposed solution adopts MR as a communication layer to convey a mobile manipulator's intentions and upcoming actions to the humans with whom it interacts, thus improving their collaboration. A user study involving 20 participants demonstrated the effectiveness of this MR-focused approach in facilitating collaborative tasks, with a positive effect on overall collaboration performances and human satisfaction.

Weizi Liu

With the rise of human-machine communication, machines are increasingly designed with humanlike characteristics, such as gender, which can inadvertently trigger cognitive biases. Many conversational agents (CAs), such as voice assistants and chatbots, default to female personas, leading to concerns about perpetuating gender stereotypes and inequality. Critiques have emerged regarding the potential objectification of females and reinforcement of gender stereotypes by these technologies. This research, situated in conversational AI design, aims to delve deeper into the impacts of gender biases in human-CA interactions. From a behavioral and communication research standpoint, this program focuses not only on perceptions but also the linguistic styles of users when interacting with CAs, as previous research has rarely explored. It aims to understand how pre-existing gender biases might be triggered by CAs' gender designs. It further investigates how CAs' gender designs may reinforce gender biases and extend them to human-human communication. The findings aim to inform ethical design of conversational agents, addressing whether gender assignment in CAs is appropriate and how to promote gender equality in design.

Christine P Lee

My research centers on the development of context-adaptive AI systems to improve end-user adoption through the integration of technical methods. I deploy these AI systems across various interaction modalities, including user interfaces and embodied agents like robots, to expand their practical applicability. My research unfolds in three key stages: design, development, and deployment. In the design phase, user-centered approaches were used to understand user experiences with AI systems and create design tools for user participation in crafting AI explanations. In the ongoing development stage, a safety-guaranteed AI system for a robot agent was created to automatically provide adaptive solutions and explanations for unforeseen scenarios. The next steps will involve the implementation and evaluation of context-adaptive AI systems in various interaction forms. I seek to prioritize human needs in technology development, creating AI systems that tangibly benefit end-users in real-world applications and enhance interaction experiences.

Aivin V. Solatorio

Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility. Our proposed model is rigorously tested in the context of the HuMob Challenge 2023 -- a competition designed to evaluate the performance of prediction models on standardized datasets to predict human mobility. The challenge leverages two datasets encompassing urban-scale data of 25,000 and 100,000 individuals over a longitudinal period of 75 days. GeoFormer stands out as a top performer in the competition, securing a place in the top-3 ranking. Its success is underscored by performing well on both performance metrics chosen for the competition -- the GEO-BLEU and the Dynamic Time Warping (DTW) measures. The performance of the GeoFormer on the HuMob Challenge 2023 underscores its potential to make substantial contributions to the field of human mobility prediction, with far-reaching implications for disaster preparedness, epidemic control, and beyond.

Zheng Yuan, Hongyi Yuan, Chuanqi Tan et al.

Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions between humans and models. InstructGPT implements RLHF through several stages, including Supervised Fine-Tuning (SFT), reward model training, and Proximal Policy Optimization (PPO). However, PPO is sensitive to hyperparameters and requires multiple models in its standard implementation, making it hard to train and scale up to larger parameter counts. In contrast, we propose a novel learning paradigm called RRHF, which scores sampled responses from different sources via a logarithm of conditional probabilities and learns to align these probabilities with human preferences through ranking loss. RRHF can leverage sampled responses from various sources including the model responses from itself, other large language model responses, and human expert responses to learn to rank them. RRHF only needs 1 to 2 models during tuning and can efficiently align language models with human preferences robustly without complex hyperparameter tuning. Additionally, RRHF can be considered an extension of SFT and reward model training while being simpler than PPO in terms of coding, model counts, and hyperparameters. We evaluate RRHF on the Helpful and Harmless dataset, demonstrating comparable alignment performance with PPO by reward model score and human labeling. Extensive experiments show that the performance of RRHF is highly related to sampling quality which suggests RRHF is a best-of-n learner. Codes available at https://github.com/GanjinZero/RRHF.

Meriam Moujahid, David A. Robb, Christian Dondrup et al.

Social Robots in human environments need to be able to reason about their physical surroundings while interacting with people. Furthermore, human proxemics behaviours around robots can indicate how people perceive the robots and can inform robot personality and interaction design. Here, we introduce Charlie, a situated robot receptionist that can interact with people using verbal and non-verbal communication in a dynamic environment, where users might enter or leave the scene at any time. The robot receptionist is stationary and cannot navigate. Therefore, people have full control over their personal space as they are the ones approaching the robot. We investigated the influence of different apparent robot personalities on the proxemics behaviours of the humans. The results indicate that different types of robot personalities, specifically introversion and extroversion, can influence human proxemics behaviours. Participants maintained shorter distances with the introvert robot receptionist, compared to the extrovert robot. Interestingly, we observed that human-robot proxemics were not the same as typical human-human interpersonal distances, as defined in the literature. We therefore propose new proxemics zones for human-robot interaction.

Yu Meng, Szabolcs Horvát, Carl D. Modes et al.

Does an ecological community allow stable coexistence? Identifying the general principles that determine the answer to this question is a central problem of theoretical ecology. Random matrix theory approaches have uncovered the general trends of the effect of competitive, mutualistic, and predator-prey interactions between species on stability of coexistence. However, an ecological community is determined not only by the counts of these different interaction types, but also by their network arrangement. This cannot be accounted for in a direct statistical description that would enable random matrix theory approaches. Here, we therefore develop a different approach, of exhaustive analysis of small ecological communities, to show that this arrangement of interactions can influence stability of coexistence more than these general trends. We analyse all interaction networks of $N\leqslant 5$ species with Lotka-Volterra dynamics by combining exact results for $N\leqslant 3$ species and numerical exploration. Surprisingly, we find that a very small subset of these networks are "impossible ecologies", in which stable coexistence is non-trivially impossible. We prove that the possibility of stable coexistence in general ecologies is determined by similarly rare "irreducible ecologies". By random sampling of interaction strengths, we then show that the probability of stable coexistence varies over many orders of magnitude even in ecologies that differ only in the network arrangement of identical ecological interactions. Finally, we demonstrate that our approach can reveal the effect of evolutionary or environmental perturbations of the interaction network. Overall, this work reveals the importance of the full structure of the network of interactions for stability of coexistence in ecological communities.

Merle M. Reimann, Florian A. Kunneman, Catharine Oertel et al.

As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human-robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informative and engaging interaction a more structural approach to combining HRI and DM is needed. In this systematic review, we analyse the current use of DM in HRI and focus on the type of dialogue manager used, its capabilities, evaluation methods and the challenges specific to DM in HRI. We identify the challenges and current scientific frontier related to the DM approach, interaction domain, robot appearance, physical situatedness and multimodality.

Cícero Francisco de Lima, Erivelton de Souza Nunes, Filipe Augusto Xavier Lima

Este artigo tem como objetivo verificar os condicionantes do diferencial de rendimentos entre as famílias pluriativas, agrícolas e não agrícolas no meio rural brasileiro. Foram utilizados, como base, os microdados da Pesquisa Nacional por Amostra de Domicílios (PNAD) de 2014, por meio do método não paramétrico de Ñopo (2008). Os resultados evidenciaram que as famílias pluriativas ganham aproximadamente 64% a mais do que as famílias agrícolas e cerca de 6% a menos do que as famílias não agrícolas. Os resultados econométricos reforçaram a existência de disparidades de rendimentos em benefício das famílias pluriativas (0,1169 Lnw) em relação às famílias agrícolas, e diferenciais de renda negativo (-0,0647 Lnw) para a pluriatividade, comparados à atividade não agrícola.

C. Wood, M. Vanhove

Many disease ecologists and conservation biologists believe that the world is wormier than it used to be - that is, that parasites are increasing in abundance through time. This argument is intuitively appealing. Ecologists typically see parasitic infections, through their association with disease, as a negative endpoint, and are accustomed to attributing negative outcomes to human interference in the environment, so it slots neatly into our worldview that habitat destruction, biodiversity loss, and climate change should have the collateral consequence of causing outbreaks of parasites. But surprisingly, the hypothesis that parasites are increasing in abundance through time remains entirely untested for the vast majority of wildlife parasite species. Historical data on parasites are nearly impossible to find, which leaves no baseline against which to compare contemporary parasite burdens. If we want to know whether the world is wormier than it used to be, there is only one major research avenue that will lead to an answer: parasitological examination of specimens preserved in natural history collections. Recent advances demonstrate that, for many specimen types, it is possible to extract reliable data on parasite presence and abundance. There are millions of suitable specimens that exist in collections around the world. When paired with contemporaneous environmental data, these parasitological data could even point to potential drivers of change in parasite abundance, including climate, pollution, or host density change. We explain how to use preserved specimens to address pressing questions in parasite ecology, give a few key examples of how collections-based parasite ecology can resolve these questions, identify some pitfalls and workarounds, and suggest promising areas for research. Natural history specimens are "parasite time capsules" that give ecologists the opportunity to test whether infectious disease is on the rise and to identify what forces might be driving these changes over time. This approach will facilitate major advances in a new sub-discipline: the historical ecology of parasitism.