Search Results (20)

This research investigates the influence of environmental regulation on subjective evaluations of video quality within the Quality of Experience (QoE) paradigm. This work presents a supplementary experiment conducted in a controlled laboratory setting, building on our previous crowdsourcing studies carried out in uncontrolled, web-based conditions using the Prolific platform. Both tests utilized the identical crowdsourcing platform and complied with the International Telecommunication Union Telecommunication (ITU-T) P.910 Recommendations, ensuring external validity and methodological consistency. Participants assessed a collection of processed video sequences (PVS) comprising 46 distinct video clips utilizing the 5-point Absolute Category Rating (ACR) scale, while their response times were documented in milliseconds as measures of cognitive exertion and decision delay. The comparison analysis employs nonparametric tests (Mann–Whitney U and Kolmogorov–Smirnov) and a hierarchical Linear Mixed-Effects Model (LMM) to examine disparities in reaction time distributions, rating consistency, and the incidence of outliers across both environments. The results indicate that controlled settings produce statistically significantly less response variability and enhanced data reliability, whereas uncontrolled settings encompass greater external diversity and real-world unpredictability. These findings offer significant insights into the balance between experimental control and external validity in crowdsourced video quality assessment, advancing the development of scalable approaches for Quality of Experience research. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

Visual attractiveness perception—an individual’s capacity to recognise and evaluate the visual appeal of urban scene safety—has direct implications for well-being, economic vitality, and social cohesion. However, most empirical studies rely on single-source metrics or algorithm-centric pipelines that under-represent human perception. Addressing this gap, we introduce a fully reproducible, multimodal framework that measures and models this domain-specific facet of human intelligence by coupling Generative Pre-trained Transformer 4o (GPT-4o) with 1000 Street View images. The pipeline first elicits pairwise aesthetic judgements from GPT-4o, converts them into a latent attractiveness scale via Thurstone’s law of comparative judgement, and then validates the scale against 1.17 M crowdsourced ratings from MIT’s Place Pulse 2.0 benchmark (Spearman ρ = 0.76, p < 0.001). Compared with a Siamese CNN baseline (ρ = 0.60), GPT-4o yields both higher criterion validity and an 88% reduction in inference time, underscoring its superior capacity to approximate human evaluative reasoning. In this study, we introduce a standardised and reproducible streetscape evaluation pipeline using GPT-4o. We then combine the resulting attractiveness scores with network-based accessibility modelling to generate a “aesthetic–accessibility map” of urban central districts in Chongqing, China. Cluster analysis reveals four statistically distinct street types—Iconic Core, Liveable Rings, Transit-Rich but Bland, and Peripheral Low-Appeal—providing actionable insights for landscape design, urban governance, and tourism planning. Full article

Latinos/as are the largest ethnic group in the U.S. and are a continuous source of population growth. Therefore, their health and quality of life are important public health concerns. Acculturation is an important determinant of health for Latinos/as. However, few studies examine models identifying determinants of acculturation along with its relationship to other social and health behaviors. The current study uses social network data from a sample of crowdsourced recruited Latinos/as (N = 300) to examine a structural model between network acculturation, psychological acculturation, social capital, help-seeking, and quality of life (QoL). The model posits several paths through which social networks (i.e., network acculturation) relate to acculturation and other model variables. Directly, network acculturation was found to be significantly related to Latino/a enculturation (−0.83, p = 0.002) and White American Acculturation (0.47, p = 0.003). Latino/a enculturation was related to help-seeking (0.21, p = 0.029) and social capital (0.36, p < 0.001), while White American acculturation was only related to social capital (0.35, p = 0.003). Social capital demonstrated a robust relationship with help-seeking (0.48, p = 0.004) and QoL (0.96, p = 0.003). The findings suggest that determinants of acculturation (i.e., network acculturation) are meaningful contributors to psychological acculturation and other variables relating to Latino/as’ QoL. Full article

As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe Lighthouse project called ENFIELD: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) zero-knowledge proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) privacy-preserving machine learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization. Full article

Background/Objectives: Childhood maltreatment has been linked to numerous adverse outcomes in adulthood, including problem substance use. However, not all individuals exposed to childhood maltreatment develop substance use problems, indicating the role of other factors in influencing this outcome. Past work suggests that adverse early life experiences, including childhood maltreatment, lead to neurobiological changes in frontolimbic functions that, in turn, result in altered stress and reward responses, heightened impulsivity, affect dysregulation, and, ultimately, increased risk for maladaptive behaviors such as substance use. The aim of this preliminary investigation using cross-sectional data was to test associations between these factors in the relationship between childhood maltreatment and alcohol use problems in a sample of emerging adults. Methods: Emerging adults (18–30 years old) who identified as regular drinkers (i.e., drinking at least 2–4 times in the past month) were recruited from a crowd-sourcing platform (Prolific) as well as community samples. Participants completed online standardized questionnaires assessing reward sensitivity and responsiveness, impulsivity, emotion regulation, and alcohol consequences. Results: Path analyses demonstrated good fit for the data (SRMR = 0.057, RMSEA = 0.096, 90% CI [0.055, 0.142], CFI = 0.957). Childhood maltreatment was associated with reward responsiveness (β = −0.026, Z = −4.222, p < 0.001) and emotion dysregulation (β = 0.669, Z = 9.633, p < 0.001), which in turn was associated with urgency and, subsequently, alcohol consequences (β = 0.758, Z = 7.870, p < 0.001). Conclusions: Although these findings are preliminary, the current study is one of the first to test a comprehensive model addressing the relationship between childhood maltreatment and alcohol use problems. The findings have the potential to inform treatment strategies that target motivation and goal-directed action for reducing and managing consequences associated with childhood maltreatment. Future research should test the model using longitudinal data to address the limitations of a cross-sectional study and assess temporal associations between constructs. Full article

Introduction: Congruent with most guideline publishers, the Canadian Urological Association (CUA) recommends shared decision-making (SDM) on PSA screening (PSAS) for prostate cancer (PCa) following a discussion of its benefits and harms. However, there are limited data on how the general male population feels about these topics. Methods: A survey was completed by 906 male-identifying participants (age > 18) recruited via Amazon Mechanical Turk (MTurk), which is a crowdsourcing platform providing minimal compensation. Participants answered questions regarding demographics (15), personal/family history (9), PCa/PSA knowledge (41), and opinions regarding PSAS (45). Results: The median age was 38.2 (SD = 12.0), with 22% reporting a family history of PCa and 20% reporting personally undergoing PSAS. Although most participants had heard of PCa (85%) and that they could be screened for it (81%), they generally did not feel knowledgeable about PCa or PSAS guidelines. Most want to talk to their clinician about PCa and PSAS (74%) and are supportive of SDM (48%) or patient-centered decision-making (25%). In general, participants thought PSAS was still worthwhile, even if it led to additional testing or side effects. Similarly, participants thought higher-risk patients should be screened earlier (p < 0.001). A number of misconceptions were evident in the responses. Conclusions: Men approaching the age of PSAS do not feel knowledgeable about PCa or PSAS and want their clinician to discuss these topics with them. The majority believe in PSAS and would like to undergo this screening following SDM. Clinicians also have a role in correcting common misconceptions about PCa. Full article

Large-scale computer platforms that process users’ online requests must be capable of handling unexpected spikes in arrival rates. These platforms, which are composed of distributed components, can be configured with parameters to ensure both the quality of the results obtained for each request and low response times. In this work, we propose a dynamic optimization engine based on metric space indexing to address this problem. The engine is integrated into the platform and periodically monitors performance metrics to determine whether new configuration parameter values need to be computed. Our case study focuses on a P2P platform designed for classifying crowdsourced images related to natural disasters. We evaluate our approach under scenarios with high and low workloads, comparing it against alternative methods based on deep reinforcement learning. The results show that our approach reduces processing time by an average of 40%. Full article

This paper introduces a decentralized architecture designed for the sharing and distribution of user-generated video streams. The proposed system employs HTTP Live Streaming (HLS) as the delivery method for these video streams. In the architecture, a creator who captures a video stream using a smartphone camera subsequently transcodes it into a sequence of video chunks called HLS segments. These chunks are then stored in a distributed manner across the worker network, forming the core of the proposed architecture. Despite the presence of a coordinator for bootstrapping within the worker network, the selection of worker nodes for storing generated video chunks and autonomous load balancing among worker nodes are conducted in a decentralized fashion, eliminating the need for central servers. The worker network is implemented using the Golang-based IPFS (InterPlanetary File System) client, called kubo, leveraging essential IPFS functionalities such as node identification through Kademlia-DHT and message exchange using Bitswap. Beyond merely delivering stored video streams, the worker network can also amalgamate multiple streams to create a new composite stream. This bundling of multiple video streams into a unified video stream is executed on the worker nodes, making effective use of the FFmpeg library. To enhance download efficiency, parallel downloading with multiple threads is employed for retrieving the video stream from the worker network to the requester, thereby reducing download time. The result of the experiments conducted on the prototype system indicates that those concerned with the transmission time of the requested video streams compared with a server-based system using AWS exhibit a significant advantage, particularly evident in the case of low-resolution video streams, and this advantage becomes more pronounced as the stream length increases. Furthermore, it demonstrates a clear advantage in scenarios characterized by a substantial volume of viewing requests. Full article

The article addresses modelling of consumer engagement in video advertising based on automatically derived low-level video features. The focus is on a young consumer group (18–24 years old) that uses ad-supported online streaming more than any other group. The reference ground truth for consumer engagement was collected in an online crowdsourcing study (N = 150 participants) using the User Engagement Scale-Short Form (UES-SF). Several aspects of consumer engagement were modeled: focused attention, aesthetic appeal, perceived usability, and reward. The contribution of low-level video features was assessed using both the linear and nonlinear models. The best predictions were obtained for the UES-SF dimension Aesthetic Appeal ($R^2=0.35$) using a nonlinear model. Overall, the results show that several video features are statistically significant in predicting consumer engagement with an ad. We have identified linear relations with Lighting Key and quadratic relations with Color Variance and Motion features ($p<0.02$). However, their explained variance is relatively low (up to 25%). Full article

Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard of the International Roughness Index (IRI) or visual inspections. This paper looks at the use of on-board sensors integrated in Original Equipment Manufacturer (OEM) connected vehicles to obtain crowdsource estimates of ride quality using the International Rough Index (IRI). This paper presents a case study where over 112 km (70 mi) of Interstate-65 in Indiana were assessed, utilizing both an inertial profiler and connected production vehicle data. By comparing the inertial profiler to crowdsourced connected vehicle data, there was a linear correlation with an R² of 0.79 and a p-value of <0.001. Although there are no published standards for using connected vehicle roughness data to evaluate pavement quality, these results suggest that connected vehicle roughness data is a viable tool for network level monitoring of pavement quality. Full article

Tool use has not yet been confirmed in horses, mules or donkeys. As this subject is difficult to research with conventional methods, we used a crowdsourcing approach to gather data. We contacted equid owners and carers and asked them to report and video examples of “unusual” behaviour via a dedicated website. We also searched YouTube and Facebook for videos of equids showing tool use. From 635 reports, including 1014 behaviours, we found 20 cases of tool use, 13 of which were unambiguous in that it was clear that the behaviour was not trained, caused by reduced welfare, incidental or accidental. We then assessed (a) the effect of management conditions on tool use and (b) whether the animals used tools alone, or socially, involving other equids or humans. We found that management restrictions were associated with corresponding tool use in 12 of the 13 cases (p = 0.01), e.g., equids using sticks to scrape hay within reach when feed was restricted. Furthermore, 8 of the 13 cases involved other equids or humans, such as horses using brushes to groom others. The most frequent tool use was for foraging, with seven examples, tool use for social purposes was seen in four cases, and there was just one case of tool use for escape. There was just one case of tool use for comfort, and in this instance, there were no management restrictions. Equids therefore can develop tool use, especially when management conditions are restricted, but it is a rare occurrence. Full article

The purpose of this study is to investigate the fluctuations that occur in stock returns of US stock indices when there is an increase in the volume of Google internet searches for the phrase “quantitative easing” in the US. The exponential generalized autoregressive conditional heteroscedasticity model (EGARCH) was applied based on weekly data of stock indices using the three-factor model of Fama and French for the period of 1 January 2006 to 30 October 2020. The existence of a statistically significant relationship between searches and financial variables, especially in the stock market, is evident. The result is strong in three of the four stock indices studied. Specifically, the SVI index was statistically significant, with a positive trend for the S&P 500 and Dow Jones indices and a negative trend for the VIX index. Investor focus on quantitative easing (QE), as determined by Google metrics, seems to calm stock market volatility and increase stock returns. Although there is a large body of research using Google Trends as a crowdsourcing method of forecasting stock returns, this paper is the first to examine the relationship between the increase in internet searches of “quantitative easing” and stock market returns. Full article

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote. Full article

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers—defined as vetted members of popular crowdsourcing platforms—to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine. Full article