Hasil untuk "Human settlements. Communities"

Umberto Domanti, Lorenzo Campidelli, Sergio Agnoli et al.

The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user's mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs.

Nancy Lamenza Sholl da Silva, Maria Tavares Cavalcanti, Emiliano de Camargo David et al.

Esse trabalho pretende apresentar reflexões sobre uma experiência educacional na pós-graduação no campo da atenção psicossocial a partir de três questões: Quais existências se erguem e que corpos carregamos quando conjugamos o verbo aquilombar? Quais existências se erguem e que corpos carregamos quando reforçamos ou desconstruímos a branquitude? Quais existências se erguem e que corpos carregamos quando produzimos e legitimamos saberes decoloniais/contracoloniais? Essas questões surgem do desafio de constituir uma prática educacional decolonial antirracista. As aulas se transformaram numa experiência de coletivo que vem funcionando há um ano e meio, sua composição inclui relações intergeracionais, interraciais, interprofissionais, trabalhadores do “front” da saúde e da educação e ouvintes. Aquelas/es que trazem as marcas e traumas do colonialismo e da colonialidade têm que assentar-se em experiências de despedaçamentos e refazimentos de si. Essa é a mais visceral prática educacional à qual somos condenados. A principal característica de uma educação antirracista contracolonial/decolonial é aquela que forma e cultiva ouvintes e ouvidos.

Madison Wesenberg, Sandy Jung, John Tedeschini

Coercively controlling behaviours are highly prevalent in the context of intimate partner violence. However, coercive control often goes undetected because, unlike physical violence, it has not always been recognized as a criminal offence, is often perceived as less severe, and does not produce visible signs of physical violence. This paper outlines the importance of understanding what coercive control is, what coercive control looks like, why it is difficult to identify, and how investigative interviewing approaches can be employed to capture behaviours associated with coercive control when working with individuals who have engaged in partner abuse. Investigative interviewing approaches and motivational interviewing can help uncover coercively controlling behaviours that would otherwise be undetected by police and other justice-involved practitioners. Use of these approaches are illustrated to emphasize the importance of planning and preparation prior to the interview process, establishing rapport, and creating collaborative, non-adversarial relationships between the interviewer and the interviewee. These factors are likely to increase the quantity and quality of information gathered during the interview process, capture the nuances of coercive control, and reduce the likelihood that the interviewee will engage in controlling behaviours that could negatively impact the interview process.

Deividson Sá Fernandes de Souza, Simone Sehnem, Patricia Guarnieri et al.

Purpose – This paper aims to provide a comprehensive literature review on the practices and challenges in managing waste electrical and electronic equipment (WEEE) in smart cities. Design/methodology/approach – A systematic literature review was conducted using the Methodi Ordinatio. Articles published between 2012 and 2022 were analyzed, totaling 149 references, of which 30 were included in the final review. Findings – Emerging technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI) are frequently highlighted as promising solutions for efficient e-waste management. Governance models and public policies are widely recognized as crucial for the successful implementation of WEEE management practices in smart cities. Originality/value – This study underscores the role of advanced technologies, such as IoT and AI, in enhancing urban mobility and WEEE management. Key challenges include information security, privacy, interoperability, costs and sustainability. The findings reveal a convergence between smart cities and WEEE management, fostering the circular economy and the recovery of valuable materials.

R. M Cherepanyn, M. V. Franchuk, J. Kubala et al.

In landscapes affected by human activity, understanding the spatial dynamics, predation behaviour and habitat preferences of large carnivores is essential for developing effective conservation strategies. The Eurasian lynx (Lynx lynx) plays a vital role in maintaining the ecological balance of temperate and boreal forests in Europe and Asia. However, the ecological patterns and behavior of lynx populations in Ukraine remain poorly studied. This study, based on GPS telemetry data collected from February to August 2023 in the Rivnenskyi Nature Reserve and adjacent territories, is the first to provide a comprehensive assessment of lynx home range, predation ecology and habitat selection in Ukraine. The average annual home range size of the lynx ranged from 181 to 255 km², depending on the home range estimator used (95 % MCP, KDE, and AKDE), with significant seasonal variation: larger ranges in summer (172 km², 95 % MCP) compared to smaller winter ranges (113.2 km², 95 % MCP). The lynx primarily preyed on roe deer (Capreolus capreolus) and brown hare (Lepus europaeus), occasionally targeting smaller prey and carnivores, including the raccoon dog (Nyctereutes procyonoides). Kills and resting sites were located in dense, low-visibility areas such as swampy coniferous and deciduous forests. Moreover, the study demonstrated that lynx actively avoided human settlements and roads, particularly during summer, highlighting the influence of anthropogenic factors. While our findings align with patterns observed in other European lynx populations, they also reveal regional variations driven by local landscape features. Of the lynx telemetry observations, 30.5% occurred within protected areas and 69.5% in forestry enterprises, degraded marshlands and hunting grounds. These results emphasise the importance of spatial ecology in carnivore conservation and highlight the need for continued monitoring to assess the impact of human activity on lynx populations in Ukraine. Furthermore, this study provides valuable insights for developing targeted conservation strategies involving local communities and stakeholders for the species in Ukraine and Eastern Europe. It also emphasises the need for standardised monitoring to facilitate comparative analyses of lynx ecology across different regions, including the Baltic and the Carpathians.

Subrata Haldar, Somnath Mandal, Subhasis Bhattacharya et al.

Abstract The peri-urban region of Durgapur Municipal Corporation (DMC) area has experienced substantial socioeconomic changes throughout the last decade (2011–2023). Most of the literature focused on urban expansion, landuse changes and industrial expansion with little attention to complex interaction between urbanization, industrialization and their effects on livelihoods and quality of life (QoL). This study examines the socio-economic transformations in the peri-urban zone of the Durgapur Municipal Corporation (DMC) from 2011–2023, emphasizing how urbanization and industrialization shape livelihoods and quality of life (QoL). The study collected primary data and used satellite-driven data for constructing several indices like the Peri-Urban Development Index (PUDI), Peri-Urban Development Transition Index (PUDTI), Livelihood Diversity Index (LDI), and Quality of life (QoL). By the systematic sampling method, the study considered 830 households with 10% marginal error and 20% non-sampling for the primary survey. Furthermore, statistical analyses like multiple linear regression and ANOVA have been applied to identify the variation in QoL across the study units. The study reveals a positive association between livelihood diversification and PUDTI, underscoring how economic diversification supports socio-economic advancement in peri-urban areas. Multilinear regression analysis highlights that demographic and economic factors especially sex ratio, household mobility, and educational opportunities are stronger predictors of QoL than land use and infrastructure improvements. Additionally, ANOVA results show that inner peri-urban areas experience more substantial QoL improvements than outer areas, likely due to better access to educational institutions, healthcare, transportation, and banking facilities, which have all seen significant upgrades. Despite these advancements, the study also identifies challenges, including displacement from traditional occupations and rising income inequality. These findings underscore the need for integrated development policies to address the diverse and complex factors influencing urbanization and the well-being of peri-urban residents, offering valuable insights for policymakers aiming to foster balanced growth in peri-urban zones.

Maurice Chiodo, Dennis Müller, Paul Siewert et al.

We use the notion of oracle machines and reductions from computability theory to formalise different Human-in-the-loop (HITL) setups for AI systems, distinguishing between trivial human monitoring (i.e., total functions), single endpoint human action (i.e., many-one reductions), and highly involved human-AI interaction (i.e., Turing reductions). We then proceed to show that the legal status and safety of different setups vary greatly. We present a taxonomy to categorise HITL failure modes, highlighting the practical limitations of HITL setups. We then identify omissions in UK and EU legal frameworks, which focus on HITL setups that may not always achieve the desired ethical, legal, and sociotechnical outcomes. We suggest areas where the law should recognise the effectiveness of different HITL setups and assign responsibility in these contexts, avoiding human "scapegoating". Our work shows an unavoidable trade-off between attribution of legal responsibility, and technical explainability. Overall, we show how HITL setups involve many technical design decisions, and can be prone to failures out of the humans' control. Our formalisation and taxonomy opens up a new analytic perspective on the challenges in creating HITL setups, helping inform AI developers and lawmakers on designing HITL setups to better achieve their desired outcomes.

Ruben Janssens, Jens De Bock, Sofie Labat et al.

Detecting miscommunication in human-robot interaction is a critical function for maintaining user engagement and trust. While humans effortlessly detect communication errors in conversations through both verbal and non-verbal cues, robots face significant challenges in interpreting non-verbal feedback, despite advances in computer vision for recognizing affective expressions. This research evaluates the effectiveness of machine learning models in detecting miscommunications in robot dialogue. Using a multi-modal dataset of 240 human-robot conversations, where four distinct types of conversational failures were systematically introduced, we assess the performance of state-of-the-art computer vision models. After each conversational turn, users provided feedback on whether they perceived an error, enabling an analysis of the models' ability to accurately detect robot mistakes. Despite using state-of-the-art models, the performance barely exceeds random chance in identifying miscommunication, while on a dataset with more expressive emotional content, they successfully identified confused states. To explore the underlying cause, we asked human raters to do the same. They could also only identify around half of the induced miscommunications, similarly to our model. These results uncover a fundamental limitation in identifying robot miscommunications in dialogue: even when users perceive the induced miscommunication as such, they often do not communicate this to their robotic conversation partner. This knowledge can shape expectations of the performance of computer vision models and can help researchers to design better human-robot conversations by deliberately eliciting feedback where needed.

Tianqi Song, Jack Jamieson, Tianwen Zhu et al.

AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes.

Parag Khanna, Karen Jane Dsouza, Chunyu Wang et al.

This paper introduces the YCB-Handovers dataset, capturing motion data of 2771 human-human handovers with varying object weights. The dataset aims to bridge a gap in human-robot collaboration research, providing insights into the impact of object weight in human handovers and readiness cues for intuitive robotic motion planning. The underlying dataset for object recognition and tracking is the YCB (Yale-CMU-Berkeley) dataset, which is an established standard dataset used in algorithms for robotic manipulation, including grasping and carrying objects. The YCB-Handovers dataset incorporates human motion patterns in handovers, making it applicable for data-driven, human-inspired models aimed at weight-sensitive motion planning and adaptive robotic behaviors. This dataset covers an extensive range of weights, allowing for a more robust study of handover behavior and weight variation. Some objects also require careful handovers, highlighting contrasts with standard handovers. We also provide a detailed analysis of the object's weight impact on the human reaching motion in these handovers.

Nick von Felten

Isolated perspectives have often paved the way for great scientific discoveries. However, many breakthroughs only emerged when moving away from singular views towards interactions. Discussions on Artificial Intelligence (AI) typically treat human and AI bias as distinct challenges, leaving their dynamic interplay and compounding potential largely unexplored. Recent research suggests that biased AI can amplify human cognitive biases, while well-calibrated systems might help mitigate them. In this position paper, I advocate for transcending beyond separate treatment of human and AI biases and instead focus on their interaction effects. I argue that a comprehensive framework, one that maps (compound human-AI) biases to mitigation strategies, is essential for understanding and protecting human cognition, and I outline concrete steps for its development.

Kevin L. Wei, Patricia Paskov, Sunishchal Dev et al.

In this position paper, we argue that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance, and we provide recommendations and a reporting checklist towards this end. Human performance baselines are vital for the machine learning community, downstream users, and policymakers to interpret AI evaluations. Models are often claimed to achieve "super-human" performance, but existing baselining methods are neither sufficiently rigorous nor sufficiently well-documented to robustly measure and assess performance differences. Based on a meta-review of the measurement theory and AI evaluation literatures, we derive a framework with recommendations for designing, executing, and reporting human baselines. We synthesize our recommendations into a checklist that we use to systematically review 115 human baselines (studies) in foundation model evaluations and thus identify shortcomings in existing baselining methods; our checklist can also assist researchers in conducting human baselines and reporting results. We hope our work can advance more rigorous AI evaluation practices that can better serve both the research community and policymakers. Data is available at: https://github.com/kevinlwei/human-baselines

Sujan Sarker, Haley N. Green, Mohammad Samin Yasar et al.

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated and efficient task execution are needed, enabling studies on how robot collaboration policies affect team performance and teammates' perceived fairness, trust, and safety. Such a system can also be utilized to study the impact of a robot's normative behavior on team collaboration. Additionally, it allows for investigation into how the legibility and predictability of robot actions affect human-robot teamwork and perceived safety and trust. Existing systems are limited, typically involving one human and one robot, and thus require more insight into broader team dynamics. Many rely on games or virtual simulations, neglecting the impact of a robot's physical presence. Most tasks are turn-based, hindering simultaneous execution and affecting efficiency. This paper introduces CoHRT (Collaboration System for Human-Robot Teamwork), which facilitates multi-human-robot teamwork through seamless collaboration, coordination, and communication. CoHRT utilizes a server-client-based architecture, a vision-based system to track task environments, and a simple interface for team action coordination. It allows for the design of tasks considering the human teammates' physical and mental workload and varied skill labels across the team members. We used CoHRT to design a collaborative block manipulation and jigsaw puzzle-solving task in a team of one Franka Emika Panda robot and two humans. The system enables recording multi-modal collaboration data to develop adaptive collaboration policies for robots. To further utilize CoHRT, we outline potential research directions in diverse human-robot collaborative tasks.

Dustin Tahisin Gómez Rodríguez, Miguel Arturo Aguirre Nieto

The Department of Caquetá is located in the Amazon region of the Colombian state. Since its creation in the 19th century, it has been neglected by institutions, which has contributed, among other variables, to socio-economic and socio-environmental problems that have increased with the incursion of coca cultivation. Quantitative data on competitiveness and poverty reduction are among the lowest among the other departments of the South American country. Therefore, the general objective of the article was to characterize the results of the intervention of a pro-ject carried out by Pastoral Social Caritas Colombia and Caritas Norway in 5 municipalities of the department in the period 2018-2022 to develop food sovereignty and security in 400 farming families. The methodology, methods, and instruments are based on the Theory of Change and MEAL: Monitoring, Evaluating, All Counts and Learning used by Pastoral Social Caritas Colombia. The main conclusion is that the project's contribution to civil society focuses on strengthening organizations to enable them to move forward in spaces that can transform productivity and the recognition of rights by the department's population.

Sam Bliss, Alexandra Bramsen, Raven Graziano et al.

It has become fashionable to call for ending food charity. Anti-hunger activists and scholars advocate instead for ensuring through government programs that everybody has enough money or vouchers to purchase all the food they need. Their criticisms rightly denounce charitable food for being incapable of eradicating hunger, but they neglect the advantages that charity confers as a non-market food practice—that is, an activity that produces or distributes food that is not for sale. Our interviews with non-market food practitioners in the Brattleboro, Vermont, area demonstrated that distributing food for free strengthens relationships, fosters resilience, puts edible-but-not-sellable food to use, and aligns with an alternative, non-market vision of a desirable food future. Interviewees suggested that market food systems, in which food is distributed via selling it, cannot replicate these benefits. Yet food pantries and soup kitchens tend to imitate supermarkets and restaurants—their market counterparts—since purchasing food is considered the dignified way to feed oneself in a market economy. We suggest that charities might do well to emphasize the benefits specific to non-market food rather than suppressing those benefits by mimicking markets. But charities face limits to making their food distribution dignified, since they are essentially hierarchies that funnel gifts from well-off people to poor people. Food sharing among equals is an elusive ambition in this highly unequal world, yet it is only by moving in this direction that non-market food distribution can serve society without stigmatizing recipients.

Sheidy Yudhiasta, Joko Mijiarto

The Covid-19 pandemic has driven changes in various fields in the application of technology, including in the tourism sector. Increasing the use of social media is a new challenge for tourism workers to carry out digital marketing. However, human resources capable of using technology and conducting digital marketing in tourism management are not always available. The purpose of this community service is to improve the quality and ability of Sunrise Land Lombok (SLL) employees to digitize and use social media. The implementation of community service will be carried out from October to December 2022 by providing training and assistance SLL tourism workers. The stages carried out in this service include the stages of observation and interviews, training, mentoring, and evaluation. Based on observations and interviews with the director of SLL, it is known that SLL workers face several obstacles in utilizing social media. These obstacles include more understanding about creating engaging social media content. Based on these problems, the training materials provided include: 1) Content planning, Canva, and website training for scheduled posting; 2) Press release writing and copywriting training and 3) practice video editing using CapCut and VN. The training is carried out online and is divided into several meetings, with each meeting accompanied by assignments for workers to practice the material given during the delivery.

Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga

This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of the human-AI team, and moving past the framing of collaboration as classification to focus on decision-making tasks, we introduce a novel approach to policy learning. Specifically, we develop a robust solution for human-AI collaboration when outcomes are only observed under assigned actions. We propose a deferral collaboration approach that maximizes decision rewards by exploiting the distinct strengths of humans and AI, strategically allocating instances among them. Critically, our method is robust to misspecifications in both the human behavior and reward models. Leveraging the insight that performance gains stem from divergent human and AI behavioral patterns, we demonstrate, using synthetic and real human responses, that our proposed method significantly outperforms independent human and algorithmic decision-making. Moreover, we show that substantial performance improvements are achievable by routing only a small fraction of instances to human decision-makers, highlighting the potential for efficient and effective human-AI collaboration in complex management settings.

Bharat Dahiya, Francesco De Pascale, Orlando De Pietro et al.

Anna Zoodsma, Mary Jo Dudley, Laura-Anne Minkoff-Zern

This article looks at the United States’ federal H-2A Temporary Agricultural Visa Program and reforms proposed by the Farm Workforce Mod­ernization Act. In this policy analysis, we draw on media content analysis and qualitative inter­views to compare the viewpoints of farmers, workers, grower and worker advocacy groups, intermediary agents, and politicians. We find that perspectives on the program are dependent upon actors’ level of direct interaction with workers. Moderate-sized farmers and regionally based worker advocacy groups tend to be the most concerned with day-to-day program operations and fair working condi­tions. In contrast, national-level advocacy groups, intermediary agents, and politi­cians are less critical of the program and seek to broadly expand farmer access to guestworkers, justifying proposed pro­gram reforms with dis­courses of national food security and immigration reform. Ultimately, we suggest that engaging a food systems lens to under­stand these policies provides a more nuanced per­spective, addressing national food security and immigration as related issues.

Kori Inkpen, Shreya Chappidi, Keri Mallari et al.

Human-AI collaboration for decision-making strives to achieve team performance that exceeds the performance of humans or AI alone. However, many factors can impact success of Human-AI teams, including a user's domain expertise, mental models of an AI system, trust in recommendations, and more. This work examines users' interaction with three simulated algorithmic models, all with similar accuracy but different tuning on their true positive and true negative rates. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Our results show that while recommendations from an AI-Assistant can aid user decision making, factors such as users' baseline performance relative to the AI and complementary tuning of AI error types significantly impact overall team performance. Novice users improved, but not to the accuracy level of the AI. Highly proficient users were generally able to discern when they should follow the AI recommendation and typically maintained or improved their performance. Mid-performers, who had a similar level of accuracy to the AI, were most variable in terms of whether the AI recommendations helped or hurt their performance. In addition, we found that users' perception of the AI's performance relative on their own also had a significant impact on whether their accuracy improved when given AI recommendations. This work provides insights on the complexity of factors related to Human-AI collaboration and provides recommendations on how to develop human-centered AI algorithms to complement users in decision-making tasks.