Hasil untuk "Human ecology. Anthropogeography"

R. O'Neill, J. Krummel, R. Gardner et al.

M. Willi, Ross T. Pitman, Anabelle W. Cardoso et al.

Ecologists often study wildlife populations by deploying camera traps. Large datasets are generated using this approach which can be difficult for research teams to manually evaluate. Researchers increasingly enlist volunteers from the general public as citizen scientists to help classify images. The growing number of camera trap studies, however, makes it ever more challenging to find enough volunteers to process all projects in a timely manner. Advances in machine learning, especially deep learning, allow for accurate automatic image classification. By training models using existing datasets of images classified by citizen scientists and subsequent application of such models on new studies, human effort may be reduced substantially. The goals of this study were to (a) assess the accuracy of deep learning in classifying camera trap data, (b) investigate how to process datasets with only a few classified images that are generally difficult to model, and (c) apply a trained model on a live online citizen science project. Convolutional neural networks (CNNs) were used to differentiate among images of different animal species, images of humans or vehicles, and empty images (no animals, vehicles, or humans). We used four different camera trap datasets featuring a wide variety of species, different habitats, and a varying number of images. All datasets were labelled by citizen scientists on Zooniverse. Accuracies for identifying empty images across projects ranged between 91.2% and 98.0%, whereas accuracies for identifying specific species were between 88.7% and 92.7%. Transferring information from CNNs trained on large datasets (“transfer‐learning”) was increasingly beneficial as the size of the training dataset decreased and raised accuracy by up to 10.3%. Removing low‐confidence predictions increased model accuracies to the level of citizen scientists. By combining a trained model with classifications from citizen scientists, human effort was reduced by 43% while maintaining overall accuracy for a live experiment running on Zooniverse. Ecology researchers can significantly reduce image classification time and manual effort by combining citizen scientists and CNNs, enabling faster processing of data from large camera trap studies.

A. Newton, J. Icely, S. Cristina et al.

Coastal wetlands, such as saltmarshes and mangroves that fringe transitional waters, deliver important ecosystem services that support human development. Coastal wetlands are complex social-ecological systems that occur at all latitudes, from polar regions to the tropics. This overview covers wetlands in five continents. The wetlands are of varying size, catchment size, human population and stages of economic development. Economic sectors and activities in and around the coastal wetlands and their catchments exert multiple, direct pressures. These pressures affect the state of the wetland environment, ecology and valuable ecosystem services. All the coastal wetlands were found to be affected in some ways, irrespective of the conservation status. The main economic sectors were agriculture, animal rearing including aquaculture, fisheries, tourism, urbanization, shipping, industrial development and mining. Specific human activities include land reclamation, damming, draining and water extraction, construction of ponds for aquaculture and salt extraction, construction of ports and marinas, dredging, discharge of effluents from urban and industrial areas and logging, in the case of mangroves, subsistence hunting and oil and gas extraction. The main pressures were loss of wetland habitat, changes in connectivity affecting hydrology and sedimentology, as well as contamination and pollution. These pressures lead to changes in environmental state, such as erosion, subsidence and hypoxia that threaten the sustainability of the wetlands. There are also changes in the state of the ecology, such as loss of saltmarsh plants and seagrasses, and mangrove trees, in tropical wetlands. Changes in the structure and function of the wetland ecosystems affect ecosystem services that are often underestimated. The loss of ecosystem services impacts human welfare as well as the regulation of climate change by coastal wetlands. These cumulative impacts and multi-stressors are further aggravated by indirect pressures, such as sea-level rise.

Jinliang Wang

Thomas Herrmann

This contribution explores how the integration of Artificial Intelligence (AI) into organizational practices can be effectively framed through a socio-technical perspective to comply with the requirements of Human-centered AI (HCAI). Instead of viewing AI merely as a technical tool, the analysis emphasizes the importance of embedding AI into communication, collaboration, and decision-making processes within organizations from a human-centered perspective. Ten case-based patterns illustrate how AI support of predictive maintenance can be organized to address quality assurance and continuous improvement and to provide different types of sup-port for HCAI. The analysis shows that AI adoption often requires and enables new forms of organizational learning, where specialists jointly interpret AI output, adapt workflows, and refine rules for system improve-ment. Different dimensions and levels of socio-technical integration of AI are considered to reflect the effort and benefits of keeping the organization in the loop.

Wenge Xu, Foroogh Hajiseyedjavadi, Kurtis Weir et al.

External Human-Machine Interfaces (eHMIs) have been proposed to facilitate communication between Automated Vehicles (AVs) and pedestrians. However, no attention was given to Deaf and Hard-of-Hearing (DHH) people. We conducted a formative study through focus groups with 6 DHH people and 6 key stakeholders (including researchers, assistive technologists, and automotive interface designers) to compare proposed eHMIs and extract key design requirements. Subsequently, we investigated the effects of visual and auditory eHMI in a virtual reality user study with 32 participants (16 DHH). Results from our scenario suggesting that (1) DHH participants spent more time looking at the AV; (2) both visual and auditory eHMIs enhanced trust, usefulness, and perceived safety; and (3) only visual eHMIs reduced the time to step into the road, time looking at the AV, gaze time, and percentage looking at active visual eHMI components. Lastly, we provided five practical implications for making eHMI inclusive of DHH people.

Yiru Chen, Sally Fang, Sai Sree Harsha et al.

Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constraints such as data sparsity, quality assurance, and rapid deployment. To address these challenges, we adopt a human-in-the-loop development workflow that combines prompt engineering, retrieval grounding, and lightweight human validation. We describe the system architecture, development process, and real-world deployment outcomes. Our experience shows that agile, feedback-driven development enables scalable and reliable AI assistants, even in cold-start scenarios.

Mingkang Wu, Devin White, Evelyn Rose et al.

Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we leverage human ratings in reward-free environments to infer a reward function, introducing learnable weights that balance the contributions of both classification and regression models. This design captures the inherent uncertainty in human decision-making and allows the model to adaptively emphasize different strategies. We conduct several experiments using synthetic human ratings to validate the effectiveness of the proposed approach. Results show that our method consistently outperforms existing rating-based RL methods, and in some cases, even surpasses traditional RL approaches.

Rune Møberg Jacobsen, Joel Wester, Helena Bøjer Djernæs et al.

This paper investigates the impact of artificial intelligence integration on remote operations, emphasising its influence on both distributed and team cognition. As remote operations increasingly rely on digital interfaces, sensors, and networked communication, AI-driven systems transform decision-making processes across domains such as air traffic control, industrial automation, and intelligent ports. However, the integration of AI introduces significant challenges, including the reconfiguration of human-AI team cognition, the need for adaptive AI memory that aligns with human distributed cognition, and the design of AI fallback operators to maintain continuity during communication disruptions. Drawing on theories of distributed and team cognition, we analyse how cognitive overload, loss of situational awareness, and impaired team coordination may arise in AI-supported environments. Based on real-world intelligent port scenarios, we propose research directions that aim to safeguard human reasoning and enhance collaborative decision-making in AI-augmented remote operations.

Haiwei Xue, Xiangyang Luo, Zhanghao Hu et al.

Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys in this field focus on individual methods, lacking a comprehensive overview of the entire generative process. This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output. Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation. Our survey reviews the latest developments and technological trends in human motion video generation across three primary modalities: vision, text, and audio. By covering over two hundred papers, we offer a thorough overview of the field and highlight milestone works that have driven significant technological breakthroughs. Our goal for this survey is to unveil the prospects of human motion video generation and serve as a valuable resource for advancing the comprehensive applications of digital humans. A complete list of the models examined in this survey is available in Our Repository https://github.com/Winn1y/Awesome-Human-Motion-Video-Generation.

Alireza Sarsangi Aliabad, Ara Toomanian, Majid Kiavarz et al.

Extended Abstract:1. IntroductionElectricity is an essential input for all production systems and a necessity for all modern families. Hence, relevant energy policies are needed to induce efficient electricity consumption in the residential sector in many countries due to the effects of global warming and security of energy supply. Forecasting electricity demand at a regional or national level is crucial for planning to ensure optimal energy management. Various factors influence household consumption patterns. Factors such as employment rate, residential area, distance from green space, etc. affect electricity consumption. The purpose of this study is to investigate the impact of various factors on electricity consumption in residential homes in Yazd city. The results of this study will be useful for making management decisions for planning to reduce electricity consumption.2. Research MethodologyThe present study was conducted in the city of Yazd, which has a hot and dry climate and is extremely hot in the summer. Data on electricity consumption of Yazd city subscribers was obtained from the provincial electricity distribution company for the years 2016 to 2019. Data related to the city's buildings, such as (current use, building height, area, building shape, and building age), as well as streets, existing street widths, and the location of parks and green spaces, were obtained from the municipality. Spatial configuration indices including: connectivity, depth, coherence and control were estimated. The urban physical parameters of the components of parcel area, building area, yard area, building height, building volume were calculated. Then, association rules were used to examine the existing relationships. Spatial Association Rules are a set of rules that describe the relationships between different features in spatial data. These rules are a capability to find unknown relationships in spatial data. Spatial association rules are rules that indicate the implication of a set of features on another set of features in a spatial database. These rules are introduced to discover the rules between products in large-scale transactional data. 3. Results and discussionResidential electricity consumption data was analyzed using Moran's spatial autocorrelation index and based on Euclidean distance. The results of the study of hot and cold spots of residential electricity consumption data in the study area showed that the distribution of electricity consumption in residential homes is asymmetrical. That is, the number of homes with very high electricity consumption is greater than the number of homes with very low electricity consumption.In total, 3.2 percent of the number of parcels in the region is made up of Low_High outliers and 4.7 percent is High_Low. In the present study, the Apriori algorithm was used. The Apriori algorithm is known as one of the main methods in data mining for discovering association rules. The results of the rule review using Apriori showed that in rule one: buildings with a height of 5 to 8 meters that are located in a new urban context are most likely (93%) to have an annual electricity consumption of more than 3,500 units. Rule two: buildings that are located in a new urban context and their control is less than 1 are most likely (87%) to have an annual electricity consumption of more than 3,500 units. Rule three: buildings that are located in parcels with an area of 150 to 250 square meters and a local connectivity of 2-3 are most likely (74%) to have an annual electricity consumption of more than 3,500 units. Rule four: buildings that are located in parcels with an area of 150 to 250 square meters and in a new urban context and with a yard area of less than 75 square meters are most likely (61%) to have an annual electricity consumption of more than 3,500 units.4. ConclusionAssociation rules are able to extract patterns that cannot be easily identified by traditional methods and provide useful information for optimizing energy consumption.One of the major challenges in using association rules in big data is the need for time-consuming and resource-intensive processing, especially when the data is complex and contains a large number of features. Association rules are usually designed for discrete data, and for numerical data, complex preprocessing such as converting the data to categorical values may be required. Also, the appropriate selection of parameters such as minimum support and confidence can be difficult and have a significant impact on the quality and applicability of the extracted results. It is suggested that in future studies, hourly electricity consumption data should be used if possible so that the effects of more factors can be examined. -

Adalberto Navidad Sánchez, Carlos Alberto Pérez Ramírez, Lilia Zizumbo Villarreal

[Introducción]: A lo largo del proceso histórico, el capitalismo ha propiciado el despojo tanto de tierras como de recursos e incide en la separación de las personas productoras del campo de sus medios de vida. En esta realidad, la actividad turística ha sido impulsada con fines de acumulación, en detrimento de la conservación ambiental y las condiciones de vida de las poblaciones locales, por lo que es necesario contribuir a la discusión de perspectivas teórico-metodológicas que permitan comprender la compleja situación actual. [Objetivo]: El trabajo tuvo como objetivo relacionar diversos planteamientos teóricos y metodológicos que han sido empleados para el abordaje de la expansión y las dinámicas capitalistas asociadas al desarrollo de la actividad turística, con el propósito de establecer pautas para su análisis, las cuales permitan comprender los efectos ambientales que está generado en el contexto iberoamericano. [Metodología]: Se realizó un análisis crítico del acervo documental, que permitió identificar los diversos enfoques disciplinarios, aportes teóricos y metodológicos que han sido empleados para el abordaje de este objeto de estudio. [Resultados]: Se delinean diversas pautas y un diseño estructural, soportados en diversos tópicos, categorías y subcategorías de análisis, los cuales posibilitarían comprender los efectos ambientales que la actividad está generando. [Conclusiones]: Esta propuesta puede dar soporte teórico y metodológico para el desa- rrollo de una investigación empírica, aunque es necesario avanzar en el diseño procedimental y la determinación de los medios verificadores.

Ariela Mortara, Rosantonietta Scramaglia

As has been pointed out, the ongoing pandemic had a profound impact on both the urban landscape of cities and the consumption patterns of residents, city users, and tourists. This led to changes in the lifestyle of users and in the use of spaces. Indeed, the pandemic months have transformed how people work, study, travel, spend leisure time, and, in general, live and plan for the future. Furthermore, the availability of living space for each household, the necessity to accommodate various activities and requirements within the same space, and the available technological resources are three variables that have significantly shaped the quality of life during the social distancing measures imposed by lockdowns. Nevertheless, with the gradual easing of COVID-19 restrictions since the second quarter of 2021, there has been a gradual return to pre-pandemic routines, leading to a rejuvenation of urban areas and to urban rebirth. This notion encompasses the process of revitalization, renewal, and transformation that numerous cities have undergone in the aftermath of the global health crisis. This paper presents findings from a research project titled "The Rebirth of Cities as an Economic and Social Opportunity: Realities, Perceptions, and Hopes," which focuses on the analysis of 100 face-to-face interviews conducted with merchants, entrepreneurs, and managers of commercial, leisure, and workspace establishments scattered throughout Milan's urban area. Many of these commercial activities can be described as hybrid spaces, multifunctional environments that integrate different activities, often providing opportunities for engaging in nonprofit cultural pursuits. The interviews, conducted in November 2021, following a period in which Milan experienced the resurgence of a vibrant summer season marked by the return of tourists, including international visitors, and the revival of numerous institutional events, reveal a positive attitude toward the future. The analysis highlights: 1) the desire to innovate; 2) the willingness to draw insights from the negative experience of the lockdown in order to improve the quality of the offered services also thanks to the adoption of social media; 3) the willingness to improve the established relationship with the inhabitants and the public and private institutions of the neighborhood.

Amir Hamed Saeidi, Arash Saghafi Asl, Masoud Haqlesan et al.

Public spaces, in their various typologies, constitute critical infrastructures for fostering social interaction, urban leisure, and civic participation, thereby serving as foundational components in advancing urban livability. The central district of Tehran—characterized by a rich mosaic of public spatial forms such as parks, pedestrian zones, squares, and urban open spaces—provides a compelling case for investigating the spatial and functional dynamics of livability enhancement. This research, grounded in an interdisciplinary framework and employing a descriptive-analytical methodology, explores the extent to which different categories of public spaces contribute to the multidimensional construct of urban livability. Utilizing a mixed-methods approach, the study integrates quantitative data—collected via structured questionnaires distributed among 364 users and residents of selected public spaces (sample size determined through Cochran’s formula)—with qualitative insights derived from semi-structured interviews conducted with domain experts. Analytical procedures were executed using SPSS and AMOS software. The empirical results highlight that public spaces with user-oriented design principles, high spatial accessibility, and functional versatility exert statistically significant effects on key livability indicators, including vibrancy, perceived safety, social cohesion, and environmental satisfaction. The study concludes by articulating strategic recommendations for the design and governance of public spaces, emphasizing their pivotal role in reinforcing urban livability and improving the experiential quality of life in the dense core of megacities such as Tehran.

B. Daru, Jordan Rodriguez

R. Kays, M. Wikelski

One of the biggest trends in ecology over the past decade has been the creation of standardized databases. Recently, this has included live data, formal linkages between disparate databases, and automated analytics, a synergy that we recognize as the Internet of Animals (IoA). Early IoA systems relate animal locations to remote-sensing data to predict species distributions and detect disease outbreaks, and use live data to inform management of endangered species. However, meeting the future potential of the IoA concept will require solving challenges of taxonomy, data security, and data sharing. By linking data sets, integrating live data, and automating workflows, the IoA has the potential to enable discoveries and predictions relevant to human societies and the conservation of animals.

Andreas Aagaard Christensen, V. Eetvelde

Context Models of human agency within research on land systems and landscapes do not fully account for social and cultural factors in decision making. Conversely, within social theory, parallel concepts of agency do not fully take biophysical and spatial factors into account. This calls for a synthesis of conceptual models addressing human decision making in land systems. Objectives The review identifies parallels between social and ecological perspectives on humans as co-constituent parts of complex land systems. On this basis selected models of agency combining insights from social theory and land systems research are outlined and compared, and improved concepts are outlined. Methods Elements of agency in modern agricultural land systems are reviewed. A case study illustrating the application of agency concepts in an analysis of decision making among farmers on the Canterbury Plains (New Zealand) is presented. On this basis it is discussed how to improve understandings of human agency in land systems. Results The review identifies and compares parallel conceptions of agency, practice and holism in landscape ecology and social theory. Taking the agency of farmers in contemporary agricultural landscapes as an example, theories currently used to characterise and interpret the agency of farmers are discussed and improvements considered. Potentials for improvement of current conceptual models are indicated and discussed, and an improved model of agency is suggested. Conclusions Based on the review, the article presents an improved conceptual model of agency in land systems emphasizing the position of agents in social-ecological contexts of action.

Vincenza Ferrara, G. Sala, T. La Mantia

Intercropping landscapes characterised by the presence of certain plant features are usually considered traditional landscapes, important for their biocultural heritage. In recent decades, olive agroforestry systems previously widespread throughout Sicily have transitioned to monocultures alongside the disappearance of other tree species. To analyse the dynamics of land use, we combine mathematical representations and oral narratives of spatial change, focussing our case study on a rural area of inner Sicily, Cozzo del Lampo, characterised by a high presence of century-old olive trees. By using local geonarratives in combination with the results of change detection analysis using historical aerial images spanning 50 years (1955 – 2005), we gain insights into the relationality of people and places over time, highlighting how biocultural heritage is correlated to both local culture and ecology, and demonstrating the value of ecological perspectives to understand past and current human actions. The active engagement of the local population in the interpretation of their own (past-present) practices is key to access new ecological knowledge.

Safaa Baydoun, Nizar Hani, Hicham EL Zein et al.

Ben Wang, Jiqun Liu, Jamshed Karimnazarov et al.

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.