Crowdsourcing - Science topic

The rural revitalization strategy requires the establishment of an express delivery industry service system with high-quality service, advanced technology, universal urban and rural benefits, green energy saving, safety and efficiency. In order to improve the efficiency of rural logistics terminal distribution, this paper first proposes a crowdsour...

New technologies are significantly reshaping the business model of last-mile logistics, impacting how companies manage delivery and fulfillment processes. As e-commerce continues to grow, the demand for efficient and innovative solutions in last-mile delivery has become critical for enterprises facing changing consumer preferences, market competiti...

Safe overnight truck parking in the United States is in critical shortage, jeopardizing road safety, logistics efficiency, and driver well-being. This study proposes a framework that integrates nationwide parking data, traffic patterns, and crowdsourced reports to predict real-time truck parking availability. Advanced techniques—clustering with the...

This paper unpacks the effects of social networks on county‐level COVID19 vaccinations in the US. We jointly assess the contemporaneous and dynamic network ef‐fects of vaccination exposure, to distinguish between network‐mediated contemporane‐ous effects (e.g., “vaccine‐hunter” Facebook groups crowd‐source information about ac‐cess and efficacy) an...

DaanSetu is an innovative platform designed to overcome the fragmentation in the charitable sector by offering a centralized space for NGOs and donors to connect seamlessly. Many NGOs struggle with visibility and communication when it comes to their material and finances, and donors are often uncertain about where their contributions would have the...

Medical corruption poses a widespread and growing threat to healthcare systems globally, with the World Health Organization estimating that 10-25% of global healthcare spending is lost annually to corrupt practices. This systematic review synthesizes existing evidence on the causes, socioeconomic and clinical impacts, and effectiveness of anti-corr...

Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of vegetation surveys, citizen science observations, and trait measurement...

Prediction markets have long served as crowdsourced crystal balls, distilling desperced information to forecast elections, economic shifts, and technological breakthroughs. But in science, they transcend mere prediction-becoming living laboratories where hypotheses are tested, refined, and financially incentivized in real time. This article dives i...

Accurate graph annotation typically requires substantial labeled data, which is often challenging and resource-intensive to obtain. In this paper, we present Crowdsourced Homophily Ties Based Graph Annotation via Large Language Model (CSA-LLM), a novel approach that combines the strengths of crowdsourced annotations with the capabilities of large l...

This study explores a methodology for assessing territorial innovation potential using OpenStreetMap (OSM) data and geoinformation technologies. Traditional assessment methods often rely on aggregated statistical data, which provide a generalized view but overlook the spatial heterogeneity within regions. To address this limitation, the proposed me...

Parents play an important role in protecting their children from childhood sexual abuse. Yet, there is relatively little empirical research examining what parents in the United States are currently doing to keep their children safe. Thus, our study examined which strategies a sample of United States parents reported using to prevent childhood sexua...

The rapid advancement of large language models (LLMs) necessitates robust, human-centric evaluation frameworks to ensure ethical alignment and contextual relevance. This paper proposes a novel ecosystem integrating an open-source user interface (UI) and a social platform to facilitate decentralized, community-driven evaluation and auditing of LLMs....

Improving bikeability is an urban policy goal that cities are pursuing to reduce their transport-related carbon emissions. To support this goal, this paper introduces BiciZen: a collaborative platform that aims to make cities and regions more bikeable. We describe the lessons learned from the development of this citizen science project. BiciZen is...

This article reviews the limitations of experimental factorial design studies, and discusses the advantages and contributions of big data megastudies in understanding how words are processed.

In the information retrieval (IR) domain, evaluation plays a crucial role in optimizing search experiences and supporting diverse user intents. In the recent LLM era, research has been conducted to automate document relevance labels, as these labels have traditionally been assigned by crowd-sourced workers - a process that is both time and consumin...

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developi...

Ensembling in deep learning improves accuracy and calibration over single networks. The traditional aggregation approach, ensemble averaging, treats all individual networks equally by averaging their outputs. Inspired by crowdsourcing we propose an aggregation method called soft Dawid Skene for deep ensembles that estimates confusion matrices of en...

Rapid and accurate acquisition of urban flood information is crucial for flood prevention, disaster mitigation, and emergency management. With the development of mobile internet, crowdsourced images on social media have been emerged as a novel and effective data source for flood information collection. However, selecting appropriate targets and emp...

In classical game theory, the players are assumed to be rational and intelligent, which is often contradictory to reality. We consider more realistic behavioral game dynamics where the players choose actions in a turn-by-turn manner and exhibit two prominent behavioral traits --- α-fraction of them are myopic who strategically choose optimal action...

The scholarly publishing process relies on peer review to uphold the quality of scientific knowledge. However, challenges such as increasing submission volumes and potential malicious behavior undermine its effectiveness. In this study, we evaluate Readersourcing, an alternative peer review approach that leverages community-driven judgments. Using...

We present SEED (\textbf{Se}mantic \textbf{E}valuation for Visual Brain \textbf{D}ecoding), a novel metric for evaluating the semantic decoding performance of visual brain decoding models. It integrates three complementary metrics, each capturing a different aspect of semantic similarity between images. Using carefully crowd-sourced human judgment...

A frequent problem in document clustering and topic modeling is the lack of ground truth. Models are typically intended to reflect some aspect of how human readers view texts (the general theme, sentiment, emotional response, etc), but it can be difficult to assess whether they actually do. The only real ground truth is human judgement. To enable r...

The pandemic threat from newly emerging viral diseases constitutes a major unsolved issue for global health. Antiviral therapy can play an important role in treating and preventing the spread of unprecedented viral infections. A repository of compounds exhibiting broad-spectrum antiviral activity against a series of different viral families would b...

The number of reported drought events per year and their impacts have significantly increased in the last two decades. In addition to monitoring drought conditions, forecasting is essential for planning activities. Various Machine Learning (ML) algorithms have experienced a substantial increase in popularity in geoscience applications. This study p...

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 tr...

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences...

Crowdsourced manufacturing, which has emerged as a pivotal paradigm in the era of Industry 4.0, redefines traditional production models by leveraging decentralized decision-making and collaborative networks. This paper reviews the evolution of the open business model in the manufacturing sector and examines fundamental issues toward a holistic fram...

Objective To identify social stressors and supports for expectant parents after prenatal diagnosis of congenital heart disease (CHD) and inform interventions to reduce distress. Method Parents of children diagnosed prenatally with CHD (N = 37) were purposively sampled across eight health systems. Qualitative data were collected using crowdsourcing...

The prevailing paradigm in the domain of Open-Domain Dialogue agents predominantly focuses on the English language, encompassing both models and datasets. Furthermore, the financial and temporal investments required for crowdsourcing such datasets for finetuning are substantial, particularly when multiple languages are involved. Fortunately, advanc...

We present a building damage dataset following the 2024 Noto Peninsula Earthquake. The database was compiled from freely available, multi-source, remote sensing data, verified through opt-in crowd-sourced information. The dataset consists of geo-referenced vector polygons representing the pre-event building footprints of 140,208 structures. Each bu...

The LCZ4r is a novel toolkit designed to streamline Local Climate Zones (LCZ) classification and Urban Heat Island (UHI) analysis. Built on the open-source R statistical programming platform, the LCZ4r package aims to improve the usability of the LCZ framework for climate and environment researchers. The suite of LCZ4r functions is categorized into...

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening pai...

Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm based on spatial or noise parameters to recruit mobile phones t...

Antimicrobial resistance (AMR) poses a global threat to human, animal and environmental health. Among the multidisciplinary tasks aimed at collectively tackling the AMR crisis, surveillance, research and education stand as major priorities. Based on a crowdsourcing research strategy, the MicroMundo project, a partner of the Tiny Earth initiative in...

There appears to be a significant positive relation between problematic cannabis use and problem gambling behaviors. Recent reviews have noted that individuals who use cannabis more frequently may experience less acute executive functioning impairment than those who use cannabis less often. The current study explored the relation between cannabis u...

The rapid proliferation of mobile applications has heightened security concerns related to malware infections, data breaches, and unauthorized access. Traditional security mechanisms primarily rely on centralized verification models, which introduce vulnerabilities such as tampering, single points of failure, and slow response times to emerging thr...

After over a half‐century of development, bathymetric lidar is a mature and widely used technology for mapping the littoral zone in support of nautical charting, benthic habitat assessment, inundation modeling and other applications. In 2018, bathymetric lidar transitioned from a purely airborne technology to also a spaceborne capability with the l...

While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task,...

Modern streetview image data provide two types of valuable information: the objective built environment and humans' subjective perception of the streetscape. In the road safety domain, the built environment has been identified as playing a significant role while indicators of human perception are commonly used to evaluate street quality in urban pl...

A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening pai...

Online crowdsourcing platforms such as MTurk and Prolific have revolutionized how researchers recruit human participants. However, since these platforms primarily recruit computer-based respondents, they risk not reaching respondents who may have exclusive access or spend more time on mobile devices that are more widely available. Additionally, the...

This study investigates the influence mechanism of customer engagement intention in corporate crowdsourcing initiatives. Through a comprehensive literature review and empirical analysis, the research constructs a conceptual model of factors influencing customer participation intention and proposes corresponding managerial implications. The findings...

المُستخلص: تناولت هذه الدراسة المشكلات التي تواجه مياه الشرب بمحليّة أبو زبد – ولاية غرب كُـردُفَانْ، وهدفت إلى التعرف علىَ حجم النقص في مياه الشرب للإنسان والحيوان، والوقوف علىَ مشاكلها الأخرىَ كالشح، البُعد المكاني لمصادرها، ازدحام مصادرها، ورداءة نوعها، حيث تم الاعتماد عدة مناهج للبحث منها المنهج التاريخي للتدوين الموثق للأحداث الماضية بطريقة تح...

Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. This paper investigates the potential...

Video generation models have rapidly progressed, positioning themselves as video world models capable of supporting decision-making applications like robotics and autonomous driving. However, current benchmarks fail to rigorously evaluate these claims, focusing only on general video quality, ignoring important factors to world models such as physic...

Education is a fundamental right, supported by initiatives like Education for All (EFA) and the Millennium Development Goals (MDGs). Despite progress, full educational access remains challenging, particularly in highly criminal areas. This paper examines the impact of crime on school access in the municipality of Rio de Janeiro. Using ancillary dat...

Truncation of proper names (e.g. English Robert > Rob) has been claimed to be a marginal or extragrammatical word-formation process due to the presumed unpredictability and irregularity of its outputs (Dressler & Merlini-Barbaresi 1994 et seq.). In this study, the hypothesis is pursued that the coexistence of multiple truncation patterns in a singl...

Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd A...

Objectives To describe how crowdsourcing contests soliciting art, letters, stories, and poetry were focused on promoting well-being and health information dissemination from the public to the public. Design LIGHT (Leaders Igniting Generational Healing and Transformation) launched three online crowdsourcing open calls that were designed using the W...

This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. $10^{70}$ nodes) with a focus on Cayley graphs and mathematical applications. The open-source CayleyPy project is a central component of our research. The present paper proposes a novel comb...

What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' personal attributes - in this case, their race. As online retail and digital purchases become increasingly common, consumer data has become increasingly valuable, raising the ri...

Tourism plays a crucial role in regional economic development, yet the extent to which a city transforms its tourism potential into realized visitor attraction remains an area of ongoing research. This study presents a novel methodology for assessing and comparing tourist attraction levels in 16 Romanian cities by integrating Monte Carlo simulation...

Theoretical work on sequential choice and large-scale experiments in online ranking and voting systems has demonstrated that social influence can have a drastic impact on social and technological systems. Yet, the effect of social influence on online rating systems remains understudied and the few existing contributions suggest that online ratings...

Pickup and delivery problem (PDP) and dynamic vehicle routing problem (DVRP) are two key components of crowdsourced freight delivery services. Although previous research has focused predominantly on static vehicle routing problems, this study formally defines the dynamic problem specific to crowdsourced freight delivery and presents a mixed-integer...

This article examines the evolving landscape of omnichannel retailing, specifically focusing on last-mile logistics optimization strategies in the contemporary digital marketplace. By analyzing current industry practices and emerging technologies, thisarticle explores how enterprise retailers can effectively integrate artificial intelligence, micro...

The aim of this research is to develop a comprehensive platform—GeoSphere—that aggregates real-time geospatial data from a multitude of sources, such as satellite imagery, in situ sensors, and crowd-sourced information. This platform will monitor and analyze critical environmental and geophysical parameters including temperature fluctuations, water...

Aim We tested the utility of showing “selfie” videos to increase adolescents’ climate change hope, agency, and behavioral intentions, and to decrease their climate anxiety. Methods We conducted a randomized controlled trial among healthy volunteers, ages 14 to 18, enrolled through a crowdsourcing platform. We randomly assigned participants (N = 10...

This research investigates the utilization of entertainment approaches, such as serious games and gamification technologies, to address various challenges and implement targeted tasks. Specifically, it details the design and development of an innovative gamified application named “J-Plus”, aimed at both professionals and non-professionals in journa...

This interactive, unplugged workshop will enable participants to reflect on critical approaches to digital learning design through the exploration of sustainability literacies. Modelling the reuse of ephemera through zine making, participants will examine creative approaches to embedding sustainability practices in their learning design and curricu...

Large Language Models (LLMs) are increasingly used in tasks requiring interpretive and inferential accuracy. In this paper, we introduce ExpliCa, a new dataset for evaluating LLMs in explicit causal reasoning. ExpliCa uniquely integrates both causal and temporal relations presented in different linguistic orders and explicitly expressed by linguist...

How good are people at judging the veracity of news? We conducted a systematic literature review and pre-registered meta-analysis of 303 effect sizes from 67 experimental articles evaluating accuracy ratings of true and fact-checked false news (NParticipants = 194,438 from 40 countries across 6 continents). We found that people rated true news as m...

As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also raised increasing concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems. How to intervene on such system outputs to mitigate anthropomorphic beha...

Two commonly-employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on c...

To address the pressing challenges of quality and sustainability in agricultural product supply chains, this paper proposes a multi-stakeholder collaborative governance framework. Adopting the perspective of collaborative governance and sustainability, the paper develops an evolutionary game model of the Chinese agricultural product supply chain. T...

In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is a significant challenge. This study explores whether Large Language Models (LLMs) can emulate the breadth of human reasoning by predicting both intuitive, fast System 1 and deliberate, slow System 2 pro...

Globally, over a billion people live with disabilities, facing significant challenges in accessing public transport, which impacts their autonomy, social participation, and economic status. Current research indicates that common problems include inadequate service in terms of destination, timing, and travel duration, as well as physical barriers at...

As a collaboratively edited and open-access knowledge archive, Wikipedia offers a vast dataset for training artificial intelligence (AI) applications and models, enhancing data accessibility and access to information. However, reliance on the crowd-sourced encyclopedia raises ethical issues related to data provenance, knowledge production, curation...

Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based saliency models, even though visual attention is inherently task-dependent. Collecting task-relevant importance data remains a resource-intensive challenge. To address this, we introduce Grid Labeling, a novel annot...

Differentiating letter shapes accurately is a core competence for any reader. Are letter shapes as distinctive as they could be? The visual shapes of letters, contrary to the phonemes of spoken languages, lack a unified description-an equivalent of the phonological features that describe most phonemes in the world's languages. Using a gamified crow...

Climate change can lead to "secondary extinction risks" for plants owing to the decoupling of life-cycle events of plants and their pollinators (i.e., phenological mismatch). However, forecasting secondary extinction risk under future climate change remains challenging. We developed a new framework to quantify plants' secondary extinction risk asso...

DNN- or AI-based up-scaling algorithms are gaining in popularity due to the improvements in machine learning. Various up-scaling models using CNNs, GANs or mixed approaches have been published. The majority of models are evaluated using PSRN and SSIM or only a few example images. However, a performance evaluation with a wide range of real-world ima...

Objective To evaluate the potential of two large language models (LLMs), GPT-4 (OpenAI) and PaLM2 (Google), in automating migraine literature analysis by conducting sentiment analysis of migraine medications in clinical trial abstracts. Background Migraine affects over one billion individuals worldwide, significantly impacting their quality of lif...

Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this re...

With the acceleration of Large Language Model (LLM) use by the public, there is an urgent need to make sure the downstream effects have beneficial impacts on humans and society. Therefore, there is an increasing push for ethical evaluation of LLMs to look for potential bias, toxic behaviour, and misinformation. Thus, crowdsourcing has become a popu...

Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educatio...

Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Access to street-level data on bicycle traffic would assist cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists’ needs. Yet, the data currently available to cities...

Semantic transparency refers to the degree to which the meaning of the whole word can be inferred from its constituents. For Chinese, semantic radicals generally carry information about the meanings of Chinese characters and, thus, can be used to reflect semantic transparency of Chinese characters. For those Chinese characters having the same seman...

This short concluding chapter reflects on the work of an ongoing collaborative academic project focused on the C18th home tour. Curious Travellers could be described as a ‘crucible’ project—a space in which different media, different perspectives, and different research skills combine and collide. Currently funded by the AHRC, it is a digital human...

Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collabora...

For decades, the search for extraterrestrial intelligence (SETI) has focused on detecting radio signals or other classical technosignatures. However, if intelligence follows a thermodynamic trajectory toward greater coherence and efficiency, then its ultimate form may be quantum-native, utilizing entanglement, nonlocality, and vacuum fluctuations r...

This thesis investigates the assessment and enhancement of factual reliability in knowledge graphs and language models, two pivotal resources in artificial intelligence (AI) applications that require accurate delivery of information. Recognizing both scenarios where external authoritative factual knowledge information is available and where it is n...

Current research has not fully explored how streetscape elements in different street spaces affect long-distance, high-speed recreational cycling. As a result, the applicability of existing findings across different street environments is limited, hindering their practical value in urban street design. To address this issue, this study focuses on t...

Access to health resources is a critical determinant of public well-being and societal resilience, particularly during public health crises when demand for medical services and preventive care surges. However, disparities in accessibility persist across demographic and geographic groups, raising concerns about equity. Traditional survey methods oft...

This paper revisits the Age of Acquisition (AoA) norms of Kuperman et al. (2012). Three studies were conducted. Study 1 reports a crowdsourcing 'megastudy' obtaining 790,024 estimates from participants providing the age they could first read and write 11,074 early acquired words from Kuperman et al. (2012). The study aimed to differentiate between...

Web-based management systems have been widely used in risk control and industrial safety. However, effectively integrating source search capabilities into these systems, to enable decision-makers to locate and address the hazard (e.g., gas leak detection) remains a challenge. While prior efforts have explored using web crowdsourcing and AI algorith...

The morphological classification of galaxies provides vital physical information about the orbital motions of stars in galaxies, and correlates in interesting ways with star formation history, and other physical properties. Galaxy morphological classification is a field with a history of more than 100 years of development, and many scientists have...

Outdoor jogging plays a critical role in active mobility and transport-related physical activity (TPA), contributing to both urban health and sustainability. While existing studies have primarily focused on jogging participation volumes through survey data, they often overlook the real-time dynamics that shape jogging experiences. This study seeks...

The ScamAlert is an innovative solution addressing the growing threat of online scams by combining real-time education, scam detection, and community reporting. By leveraging AI-driven analytics and crowd-sourced data, the platform offers users actionable insights to identify and avoid scams while enabling swift reporting to alert others. Unique fe...

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof datab...

Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly changing information environments and measuring beliefs within hard-to-reach communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural langu...

Using 1281 pedestrian tunnels scattered over Stockholm, Sweden’s capital, we investigate the nature and distribution of graffiti in three types of underpasses (pathways, cycleways, and stairways). Combining crowdsourced data and police records, we compare graffiti levels and geography using fieldwork inspections, regression models, and Geographic I...

Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution a...

Traditionally, linters are code analysis tools that help developers by flagging potential issues from syntax and logic errors to enforcing syntactical and stylistic conventions. Recently, linting has been taken as an interface metaphor, allowing it to be extended to more complex inputs, such as visualizations, which demand a broader perspective and...