Hasil untuk "Communities. Classes. Races"

Junling Wang, Lahari Goswami, Gustavo Kreia Umbelino et al.

LLM-based chatbots like ChatGPT have become popular tools for assisting with coding tasks. However, they often produce isolated responses and lack mechanisms for social learning or contextual grounding. In contrast, online coding communities like Kaggle offer socially mediated learning environments that foster critical thinking, engagement, and a sense of belonging. Yet, growing reliance on LLMs risks diminishing participation in these communities and weakening their collaborative value. To address this, we propose Community-Enriched AI, a design paradigm that embeds social learning dynamics into LLM-based chatbots by surfacing user-generated content and social design feature from online coding communities. Using this paradigm, we implemented a RAG-based AI chatbot leveraging resources from Kaggle to validate our design. Across two empirical studies involving 28 and 12 data science learners, respectively, we found that Community-Enriched AI significantly enhances user trust, encourages engagement with community, and effectively supports learners in solving data science tasks. We conclude by discussing design implications for AI assistance systems that bridge -- rather than replace -- online coding communities.

Deva Menéndez García, Daniel Carmona Cardona , Isabella Tobón Franco

Este trabajo analiza el impacto del neoliberalismo en el diseño urbano y la sostenibilidad de Medellín, centrándose en la transformación de la ciudad bajo políticas neoliberales desde la década de 1980. A partir de la revisión de los principales instrumentos de planeación del Área Metropolitana de Medellín —como la Ordenanza Departamental n.º 34 de 1980, el Plan Integral de Desarrollo Metropolitano (PIDM) 2008-2020 y el Acuerdo Metropolitano 40 de 2007—, se evalúa la planeación del Valle de Aburrá como centro conurbado y la efectividad de sus políticas públicas ambientales. Asimismo, se examina la interacción entre los sectores público y privado en proyectos urbanos estratégicos, como Metroplús y el Parque Arví. Los hallazgos evidencian una fragmentación social, territorial y ambiental, así como una estética urbana orientada al turismo ecológico, cuyos efectos en la sostenibilidad y la equidad social resultan cuestionables. Se concluye que estos elementos han sido instrumentalizados como herramientas de neoliberalización urbana, lo que pone en entredicho su verdadera contribución a la justicia ambiental y social.

Juhoon Lee, Bich Ngoc Doan, Jonghyun Jee et al.

Platforms are increasingly adopting industrial models of moderation that prioritize scalability and consistency, frequently at the expense of context-sensitive and user-centered values. Building on the multi-level governance framework that examines the interdependent relationship between platforms and middle-level communities, we investigate community appeals systems on Discord as a model for successful community-led governance. We investigate how Discord servers operationalize appeal systems through a qualitative interview study with focus groups and individual interviews with 17 community moderators. Our findings reveal a structured appeals process that balances scalability, fairness, and accountability while upholding community-centered values of growth and rehabilitation. Communities design these processes to empower users, ensuring their voices are heard in moderation decisions and fostering a sense of belonging. This research provides insights into the practical implementation of community-led governance in a multi-level governance framework, illustrating how communities can maintain their core principles while integrating procedural fairness and tool-based design. We discuss how platforms can gain insights from community-led moderation work to motivate governance structures that effectively balance and align the interests of multiple stakeholders.

Vesa Kuikka, Kosti Koistinen, Kimmo K Kaski

Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can form either subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be identified as overlapping nodes or as part of an internal structure of nested communities. To identify overlapping nodes, we apply a threshold rule based on the number of nodes in the nested structure. As the threshold value increases, the number of selected overlapping nodes decreases. This approach allows us to analyse the roles of nodes considered overlapping according to selection criteria, for example to reduce the effect of noise. We illustrate our method by using three small and two larger real-world network structures. In larger networks, minor disturbances can produce a multitude of slightly different solutions, but the core communities remain robust, allowing other variations to be treated as noise. While this study employs our own method for community detection, other approaches can also be applied. Exploring the properties of shared nodes in overlapping communities of complex networks is a novel area of research with diverse applications in social network analysis, cybersecurity, and other fields in network science.

Bin Chen, Fanhua Kong, Michael E. Meadows et al.

Abstract While heat mitigation is crucial to achieving sustainable urban development, an inadequate understanding of the evolution of the urban thermal environment (UTE) and its relationship with socio-ecological systems (SESs) constrains the development of effective mitigation strategies. In this study, we use satellite observations from 2000–2021 to explore the evolving impact of SES interactions on the UTE of 136 Chinese urban areas. The results reveal a nonlinear intensification of the UTE over the period and an indication that an increasing number of urban areas have successfully applied UTE mitigation measures. Spatio-temporal patterns in UTE are shown to be strongly influenced by social and ecological factors and their interactions, whereby the higher the SES status, the stronger the decreasing UTE trend. These findings highlight the need for, and advantages of, developing win-win solutions for urban society and ecology and have important implications in creating integrated strategies for heat mitigation in promoting urban sustainability.

Elina Bertet, Camille Gontier, Julien Fuchs

This article focuses on the sporting event known as the Arctic Winter Games, which features young people (aged ten to twenty) from the Arctic regions, provinces, and nations. It demonstrates that young participants primarily experience this event as festive, thus fulfilling the Games’ stated promise of combining sports with the promotion of Arctic culture. Drawing on data collected during the 2023 edition, the article investigates the expectations of both the young athletes and the organizers and then compares them with how participants experience the event. The behaviors, interactions, and relationships that develop during flagship Games events, such as Arctic sports and Dene games, during which festive practices take center stage, are also part of this study. The article thus illustrates that the Arctic Winter Games place festivity, primarily as a means for participants to socialize and foster a symbolic unity among young people of the North, at the heart of the event. The festive and partying moments, which are integral to this youth-focused event, make the Arctic Winter Games an unique platform to observe how international sports events are striving today to renew their model by constructing a strong social and cultural sense of belonging.

Máté Fazekas, Zalán Demeter, János Tóth et al.

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.

Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed et al.

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitigation strategies across multiple automated facial analysis tasks, as it allows to analyse, characterise and synthesise human facial diversity. In this paper, we propose a novel GAN framework to enable fine-grained control over individual race-related phenotype attributes of the facial images. Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data. Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training. Unlike prior work, our framework only relies upon 2D imagery and related parameters to achieve state-of-the-art individual control over race-related phenotype attributes with improved photo-realistic output.

Albert R. Atlaskirov

<p>Expanding as a result of information and technological progress, the processes of globalization are immersing the material world and the sphere of human relationships in the space of digital technologies. Leading experts note that the world community is on the verge of fundamental changes associated with technological breakthroughs in various fields of knowledge. The article discusses the regional features of digital transformation in the Kabardino-Balkarian Republic. If in developed countries and the most dynamically developing regions of Russia, the transition of society to a new era of digital technologies is a logical, natural, from the point of view of evolutionary development, phenomenon, then what is this process like in traditional societies? This question was central to the present work. The results of the study showed that people rightly point out both the positive and negative aspects of digital transformation. The problem of mass layoffs is relevant. People are afraid that robots and computer programs will force them out of the labor market. This problem is especially acute in such an economically depressed region as the Kabardino-Balkarian Republic. Also, people are concerned about the erosion of the cultural foundations of the titular peoples of the region, as a result of the processes of globalization, which are taking place with particular intensity in the digital world. At the same time, the respondents noted the presence of significant positive aspects of digital transformation. Welfare increases, the solution of many everyday problems is simplified, the quality of human life improves.</p>

Quintino Francesco Lotito, Federico Musciotto, Alberto Montresor et al.

Many networks can be characterised by the presence of communities, which are groups of units that are closely linked. Identifying these communities can be crucial for understanding the system's overall function. Recently, hypergraphs have emerged as a fundamental tool for modelling systems where interactions are not limited to pairs but may involve an arbitrary number of nodes. In this study, we adopt a dual approach to community detection and extend the concept of link communities to hypergraphs. This extension allows us to extract informative clusters of highly related hyperedges. We analyze the dendrograms obtained by applying hierarchical clustering to distance matrices among hyperedges across a variety of real-world data, showing that hyperlink communities naturally highlight the hierarchical and multiscale structure of higher-order networks. Moreover, hyperlink communities enable us to extract overlapping memberships from nodes, overcoming limitations of traditional hard clustering methods. Finally, we introduce higher-order network cartography as a practical tool for categorizing nodes into different structural roles based on their interaction patterns and community participation. This approach aids in identifying different types of individuals in a variety of real-world social systems. Our work contributes to a better understanding of the structural organization of real-world higher-order systems.

Daegyu Lee, Hyunwoo Nam, Chanhoe Ryu et al.

This paper introduces an approach that enhances the state estimator for high-speed autonomous race cars, addressing challenges from unreliable measurements, localization failures, and computing resource management. The proposed robust localization system utilizes a Bayesian-based probabilistic approach to evaluate multimodal measurements, ensuring the use of credible data for accurate and reliable localization, even in harsh racing conditions. To tackle potential localization failures, we present a resilient navigation system which enables the race car to continue track-following by leveraging direct perception information in planning and execution, ensuring continuous performance despite localization disruptions. In addition, efficient computing is critical to avoid overload and system failure. Hence, we optimize computing resources using an efficient LiDAR-based state estimation method. Leveraging CUDA programming and GPU acceleration, we perform nearest points search and covariance computation efficiently, overcoming CPU bottlenecks. Simulation and real-world tests validate the system's performance and resilience. The proposed approach successfully recovers from failures, effectively preventing accidents and ensuring safety of the car.

Marilia C.P. Borges, Sérgio B. Abreu, Carlos H.R. Lima et al.

Basic sanitation services are essential for human development, promoting health and inhibiting the spread of waterborne diseases. The availability of information on water and sanitation services at the local level supports the formulation, implementation and improvement of public policies aimed at advancing the provision of basic sanitation services to the population. In Brazil, the National Water and Sanitation Data System (SNIS), administered by the Ministry of Regional Development (MDR), is the largest information system for water and sanitation services in the country. Here we present the significant aspects of SNIS and offer the most recent results of water and sanitation services in the country, which reveals that water supply is the sanitation service closest to achieve the universalization preconized by the United Nations with almost 93% of the population served. The situation of sanitary sewer services reveals that only 61.9% of the Brazilian population have sewer collection systems, while only 78.5% of the collected volume is actually treated. The remaining 22.5% of the raw sewer is directly disposed in the environment. With respect to the generated sewer, only 49.1% of the volume is treated. The solid waste data show that a large part of the urban population is served by home collection services. The major challenge of this component is to ensure that the final destination is environmentally appropriate, since there are still many dumps that receive waste from different municipalities. The urban drainage data show that most Brazilian municipalities still have deficiencies in the planning of drainage services.

Rodrigo Scheeren

O artigo apresenta um recorte histórico do cenário de uso das tecnologias digitais na América do Sul, mais especificamente, a assimilação da fabricação digital. O objetivo é compreender o papel de certos eventos, atores e como algumas dinâmicas foram instauradas nas áreas de design e arquitetura na primeira década dos anos 2000. Desse modo, configura um panorama de formação do cenário local frente a uma dimensão global, que aconteceu com a aquisição e domínio gradual de software e maquinário para a criação de estratégias que versavam entre a computação e a materialização de protótipos e elementos construtivos.

Xixi Wu, Yun Xiong, Yao Zhang et al.

Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a particular kind of community. This limits its applicability in real-world networks, e.g., distinguishing fraud groups from normal ones in transaction networks. Recently, semi-supervised community detection emerges as a solution. It aims to seek other similar communities in the network with few labeled communities as training data. Existing works can be regarded as seed-based: locate seed nodes and then develop communities around seeds. However, these methods are quite sensitive to the quality of selected seeds since communities generated around a mis-detected seed may be irrelevant. Besides, they have individual issues, e.g., inflexibility and high computational overhead. To address these issues, we propose CLARE, which consists of two key components, Community Locator and Community Rewriter. Our idea is that we can locate potential communities and then refine them. Therefore, the community locator is proposed for quickly locating potential communities by seeking subgraphs that are similar to training ones in the network. To further adjust these located communities, we devise the community rewriter. Enhanced by deep reinforcement learning, it suggests intelligent decisions, such as adding or dropping nodes, to refine community structures flexibly. Extensive experiments verify both the effectiveness and efficiency of our work compared with prior state-of-the-art approaches on multiple real-world datasets.

Edoardo Ghignone, Nicolas Baumann, Mike Boss et al.

Autonomous racing is becoming popular for academic and industry researchers as a test for general autonomous driving by pushing perception, planning, and control algorithms to their limits. While traditional control methods such as MPC are capable of generating an optimal control sequence at the edge of the vehicles physical controllability, these methods are sensitive to the accuracy of the modeling parameters. This paper presents TC-Driver, a RL approach for robust control in autonomous racing. In particular, the TC-Driver agent is conditioned by a trajectory generated by any arbitrary traditional high-level planner. The proposed TC-Driver addresses the tire parameter modeling inaccuracies by exploiting the heuristic nature of RL while leveraging the reliability of traditional planning methods in a hierarchical control structure. We train the agent under varying tire conditions, allowing it to generalize to different model parameters, aiming to increase the racing capabilities of the system in practice. The proposed RL method outperforms a non-learning-based MPC with a 2.7 lower crash ratio in a model mismatch setting, underlining robustness to parameter discrepancies. In addition, the average RL inference duration is 0.25 ms compared to the average MPC solving time of 11.5 ms, yielding a nearly 40-fold speedup, allowing for complex control deployment in computationally constrained devices. Lastly, we show that the frequently utilized end-to-end RL architecture, as a control policy directly learned from sensory input, is not well suited to model mismatch robustness nor track generalization. Our realistic simulations show that TC-Driver achieves a 6.7 and 3-fold lower crash ratio under model mismatch and track generalization settings, while simultaneously achieving lower lap times than an end-to-end approach, demonstrating the viability of TC-driver to robust autonomous racing.

Dvij Kalaria, Qin Lin, John M. Dolan

Delays endanger safety of autonomous systems operating in a rapidly changing environment, such as nondeterministic surrounding traffic participants in autonomous driving and high-speed racing. Unfortunately, delays are typically not considered during the conventional controller design or learning-enabled controller training phases prior to deployment in the physical world. In this paper, the computation delay from nonlinear optimization for motion planning and control, as well as other unavoidable delays caused by actuators, are addressed systematically and unifiedly. To deal with all these delays, in our framework: 1) we propose a new filtering approach with no prior knowledge of dynamics and disturbance distribution to adaptively and safely estimate the time-variant computation delay; 2) we model actuation dynamics for steering delay; 3) all the constrained optimization is realized in a robust tube model predictive controller. For the application merits, we demonstrate that our approach is suitable for both autonomous driving and autonomous racing. Our approach is a novel design for a standalone delay compensation controller. In addition, in the case that a learning-enabled controller assuming no delay works as a primary controller, our approach serves as the primary controller's safety guard.

Md Towhidul Absar Chowdhury, Naveen Sharma

A post-pandemic world resulted in economic upheaval, particularly for the cities' communities. While significant work in NLP4PI focuses on national and international events, there is a gap in bringing such state-of-the-art methods into the community development field. In order to help with community development, we must learn about the communities we develop. To that end, we propose the task of community learning as a computational task of extracting natural language data about the community, transforming and loading it into a suitable knowledge graph structure for further downstream applications. We study two particular cases of homelessness and education in showing the visualization capabilities of a knowledge graph, and also discuss other usefulness such a model can provide.

Hussam Habib, Maaz Bin Musa, Fareed Zaffar et al.

Reddit has found its communities playing a prominent role in originating and propagating problematic socio-political discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.

Praveen Venkatesh, Rwik Rana, Harish PM

The challenges presented in an autonomous racing situation are distinct from those faced in regular autonomous driving and require faster end-to-end algorithms and consideration of a longer horizon in determining optimal current actions keeping in mind upcoming maneuvers and situations. In this paper, we propose an end-to-end method for autonomous racing that takes in as inputs video information from an onboard camera and determines final steering and throttle control actions. We use the following split to construct such a method (1) learning a low dimensional representation of the scene, (2) pre-generating the optimal trajectory for the given scene, and (3) tracking the predicted trajectory using a classical control method. In learning a low-dimensional representation of the scene, we use intermediate representations with a novel unsupervised trajectory planner to generate expert trajectories, and hence utilize them to directly predict race lines from a given front-facing input image. Thus, the proposed algorithm employs the best of two worlds - the robustness of learning-based approaches to perception and the accuracy of optimization-based approaches for trajectory generation in an end-to-end learning-based framework. We deploy and demonstrate our framework on CARLA, a photorealistic simulator for testing self-driving cars in realistic environments.

Retno Susanti, Retno Widjajanti, Grandy Loranessa Wungo et al.

Population growth in the city of Semarang increases the need for residential land, shifting individuals from the center to the suburbs. Tembalang is a sub-district with a population growth of 3.69%. The trend in population growth is used to build gated homes, for middle and upper class individuals who need more comfortable, secure, quiet housing. However, the existence of a gated community makes a physical separation between community settlements. Privatization of public spaces in gated housing potentially leads to social inequality and lack of interaction with the surrounding community. The purpose of this study was to examine the social relations between the villagers around housing and residents of the gated community. The study uses questionnaires and open interviews interviews with 93 respondents from Kampong Gendong and a hierarchical analysis to examine social relations. The results show that there are social relations between gated housing residents and villagers based on residence, and they carry out several activities together. Also, housing typology influences the strength of the interaction between villagers and residents of the gated homes. In general, gated housing appear as a form of exclusive property with separate environmental facilities, which might be used by villagers to strengthen social interaction. The relations with the surrounding community play n important role in increasing the sense of security for residents of gated housing, unlike the use of perimeter fence or the guards.