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As social media and web-based forums have grown in popularity, the fast-spreading trend of fake news has become a major threat to the government and other agencies. With the rise of social media and internet platforms, misinformation may quickly spread over borders and language boundaries. Detecting and neutralizing fake news in several languages can help to protect the integrity of global elections, political discourse, and public opinion. The lack of a robust multilingual database for training the classification models makes detecting fake news a difficult task. This paper looks at it by describing several forms of fake news (like serious fabrications, large-scale hoaxes, stance news, deceptive news, satire news, clickbait, misinformation, rumour). This review paper includes different steps, features, tools for mitigating the scourge of information pollution, and different available datasets. This study presented a taxonomy for detecting fake news, which gives a comprehensive overview and analysis of existing DL-based algorithms focusing on diverse techniques. This paper also includes the monolingual and multilingual fake news detection models. Finally, this paper ends with the technical challenges. [ABSTRACT FROM AUTHOR]

Background: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015. [ABSTRACT FROM AUTHOR]

Crowdsourcing has emerged as a pivotal data source for diverse smart city applications, ranging from health and traffic to security and safety. However, the integration of users’ location data in crowdsourced information poses a significant privacy challenge. Current privacy protection approaches of location-based services have become inadequate to face the evolving attackers’ techniques and tools. Moreover, these protection methods ignored the issue of preserving the accuracy and reliability of data. This paper introduces a novel approach, termed Double Cloak Area (DCL-Ar), designed to effectively safeguard users’ location privacy and ensure the reliability of data based on crowdsourcing. DCL-Ar differentiates by offering dual-layer protection for identity. The first layer involves users creating an initial cloak zone, while the second layer utilizes fog nodes to establish an extended cloak zone. Furthermore, the proposed method introduces three distinct scenarios for managing collaboration among fog nodes to select the optimal anonymizer and address the limitations of existing protection methods which are related to saving the reliability and the accuracy of data. DCL-Ar maintains maximum entropy, achieving complete uncertainty about user locations, thereby ensuring a high level of privacy protection. Through simulation and comparative analysis, the efficacy of the proposed approach is demonstrated where it provides a superior privacy level without significant performance. Experimental results demonstrate that DCL-Ar outperforms traditional methods, improving cache hit ratios and response times while reducing server query loads. Specifically, our approach reduces the number of queries sent to the service provider (SP) by up to 50% compared to existing methods and maintains a high cache hit ratio of nearly 100% over time. It further impacts on the traditional cloak-area and other protection approaches.

The aim of this study is to analyze small- and medium-scale enterprises' social media-enabled crowdsourcing capability for new product development. This research employs a quantitative methodology. The survey design was to collect data for analysis, and an online questionnaire was utilized to assess the proposed framework. In this research, we analyze empirical data from SME business owners and managers. Tests of the quantitative model suggest that social media-enabled crowdsourcing capabilities are empirically related to new product development. The findings that highlight the importance of firm size and type of business as moderating variables strengthen the correlation between crowdsourcing capability and new product development. This research contributes to the enrichment of empirical research that uses a lens based on theoretical resources. [ABSTRACT FROM AUTHOR]

Global food supply chains are unprepared for the increasing number and severity of the expected environmental, social and economic shocks in the coming years. The price-setting process of commodities is directly impacted by such shocks, influencing consumer behavior regarding food choice and consumption. Both the market and advances in precision agriculture drive increased production and consumption. However, there has been a lack of consideration of how consumer behavior could be harnessed to mitigate such shocks through decreased consumption and reduced waste. The SAPPhIRE model of causality was applied to design sustainable and ecologically embedded futures derivatives that could have a role in affecting commodity markets. Multi-agent systems were combined with artificial intelligence and edge computing to provide the necessary functionality. The impact of war in Ukraine was used to exemplify the design of consumer "food choice" derivatives. This resulted in a mechanism to bring aggregated acts of consumer compassion and sustainability to commodities markets to mitigate food security shocks. When implementing food choice derivatives, care must be taken to ensure that consumer food choices are rational and compatible with individual nutritional needs and financial situations, and that the legitimate interests of agri-food businesses are protected. [ABSTRACT FROM AUTHOR]

The existence of a link between the gut microbiome and autism spectrum disorder (ASD) is well established in mice, but in human populations, efforts to identify microbial biomarkers have been limited due to a lack of appropriately matched controls, stratification of participants within the autism spectrum, and sample size. To overcome these limitations, we crowdsourced the recruitment of families with age-matched sibling pairs between 2 and 7 years old (within 2 years of each other), where one child had a diagnosis of ASD and the other did not. Parents collected stool samples, provided a home video of their ASD child's natural social behavior, and responded online to diet and behavioral questionnaires. 16S rRNA V4 amplicon sequencing of 117 samples (60 ASD and 57 controls) identified 21 amplicon sequence variants (ASVs) that differed significantly between the two cohorts: 11 were found to be enriched in neurotypical children (six ASVs belonging to the Lachnospiraceae family), while 10 were enriched in children with ASD (including Ruminococcaceae and Bacteroidaceae families). Summarizing the expected KEGG orthologs of each predicted genome, the taxonomic biomarkers associated with children with ASD can use amino acids as precursors for butyragenic pathways, potentially altering the availability of neurotransmitters like glutamate and gamma aminobutyric acid (GABA).IMPORTANCE Autism spectrum disorder (ASD), which now affects 1 in 54 children in the United States, is known to have comorbidity with gut disorders of a variety of types; however, the link to the microbiome remains poorly characterized. Recent work has provided compelling evidence to link the gut microbiome to the autism phenotype in mouse models, but identification of specific taxa associated with autism has suffered replicability issues in humans. This has been due in part to sample size that sufficiently covers the spectrum of phenotypes known to autism (which range from subtle to severe) and a lack of appropriately matched controls. Our original study proposes to overcome these limitations by collecting stool-associated microbiome on 60 sibling pairs of children, one with autism and one neurotypically developing, both 2 to 7 years old and no more than 2 years apart in age. We use exact sequence variant analysis and both permutation and differential abundance procedures to identify 21 taxa with significant enrichment or depletion in the autism cohort compared to their matched sibling controls. Several of these 21 biomarkers have been identified in previous smaller studies; however, some are new to autism and known to be important in gut-brain interactions and/or are associated with specific fatty acid biosynthesis pathways.

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Psoriasis is a skin condition that affects over 100 million people worldwide, while multiple myeloma (MM) accounts for 10% of all hematologic malignancies in the US. There has been limited research on the intersection of psoriasis and MM, and clinicians often face difficult decisions in treating patients diagnosed with both conditions. For instance, the management of psoriasis with systemic immunotherapies in MM patients can be challenging because of concern about immunosuppression and possible worsening of MM. Online crowdsourcing platforms have recently become innovative tools that can actively empower patients in scientific research by enabling the contribution of health data. One such platform, HealthTree®, helps MM patients find optimal myeloma treatments and has registered > 6000 patients, many of whom have uploaded medical records and genetic profiles. By taking advantage of patient health data available on HealthTree, researchers can gain a greater understanding of the clinical characteristics and treatment responses of patients diagnosed with psoriasis and MM. In this case series, we first report a psoriasis and MM patient treated with the IL-17 inhibitor ixekizumab who demonstrated a temporary, 2-month improvement in MM biomarkers (M-protein, kappa, and kappa:lambda ratio). We then report on the clinical characteristics of 18 patients with verified profiles on HealthTree indicating concurrent psoriasis and MM conditions. We surveyed gender, age, psoriasis type, psoriasis treatment history, myeloma type, myeloma genetic features, and myeloma association with bone damage, hypercalcemia, or osteopenia. Four patients were treated with systemic immunomodulators for psoriasis, with responses suggesting that these therapies did not worsen MM progression. Our results validate crowdsourcing as a way to assess patient demographics and treatment responses for use in dermatology research. We examine the demographics of patients diagnosed with psoriasis and MM and investigate the use of systemic immunomodulators for treatment of psoriasis in MM patients.

This paper proposes a concept of adaptive centralized radio access network (C-RAN) architecture for a smart city using crowdsourced radio units (CRUs). To efficiently manage the ever-increasing mobile traffic, mobile networks have improved their capacity. The C-RAN architecture with the dense deployment of small cells has become prevalent in today’s mobile networks. However, the spatiotemporal fluctuation in traffic demand deteriorates the cost and operation efficiencies of conventional networks. Such fluctuation is expected to be intensified in the future in accordance with the increase in the data rate. To address this problem, the proposed architecture utilizes the power of citizens in the network deployment by employing the concept of vehicle-mounted small cells. The onboard CRUs are activated by edge servers when vehicles move to high-demand areas and deactivated when they move to low-demand areas. This paper also proposes the activation algorithm executed by edge servers based on the traffic information measured by roadside units (RSUs). Computer simulations demonstrate that the proposed scheme can improve network throughput during both daytime and nighttime. The proposed architecture contributes to the efficient deployment of mobile networks and better energy use in a smart city.

Research that involves a large and broad sample of museums can produce a representative picture of the entire museum sector and lead to global insights that may not be attainable through a more local lens. However, many museum research projects use a small sample of museums, meant to represent the entire field. We propose a research method that distributes data collection across a broad swath of museums to provide local detail that can be used to assemble a collective picture on a topic of interest to the field. This method, called crowdsourced data collection, was used in a yearlong study of zoos and aquariums in North America, in which 95 institutions were asked to collect data for one to two survey modules per month. We hoped this approach would produce data comparable to data gathered with conventional methods and reduce burden on participating institutions. We found the method replicated nationally representative studies with two validated scales. While only one third of the institutions completed all modules, institutions typically did 8-9 modules, with only slight decreases in the probability of completing the study over time. These results suggest researchers can use crowdsourced data collection to reliably study the museum sector. We also discuss the challenges of this method for researchers and institutions participating as data collection sites. [ABSTRACT FROM AUTHOR]

Data relevant to flood vulnerability is minimal and infrequently collected, if at all, for much of the world. This makes it difficult to highlight areas for humanitarian aid, monitor changes, and support communities in need. It is time consuming and resource intensive to do an exhaustive study for multiple flood relevant vulnerability variables using a field survey. We use a mixed methods approach to develop a survey on variables of interest and utilize an open-source crowdsourcing technique to remotely collect data with a human-machine interface using high-resolution satellite images and Google Street View. Finally, we perform an inter-rater agreement to assess if this technique provides consistent results. This paper focuses on Quito, Ecuador as a case study, but the methodology can be replicated to produce labeled training data in other areas. The overall goal is to advance methods to help build training datasets that allow for assessing and automating the mapping of flood vulnerability for urban areas.

The sciences struggle to integrate across disciplines, coordinate across data generation and modeling activities, produce connected open data, and build strong networks to engage stakeholders within and beyond the scientific community. The American Geophysical Union (AGU) is divided into 25 sections intended to encompass the breadth of the geosciences. Here, we introduce a special collection of commentary articles spanning 19 AGU sections on challenges and opportunities associated with the use of ICON science principles. These principles focus on research intentionally designed to be Integrated, Coordinated, Open, and Networked (ICON) with the goal of maximizing mutual benefit (among stakeholders) and cross‐system transferability of science outcomes. This article (a) summarizes the ICON principles; (b) discusses the crowdsourced approach to creating the collection; (c) explores insights from across the articles; and (d) proposes steps forward. There were common themes among the commentary articles, including broad agreement that the benefits of using ICON principles outweigh the costs, but that using ICON principles has important risks that need to be understood and mitigated. It was also clear that the ICON principles are not monolithic or static, but should instead be considered a heuristic tool that can and should be modified to meet changing needs. As a whole, the collection is intended as a resource for scientists pursuing ICON science and represents an important inflection point in which the geosciences community has come together to offer insights into ICON principles as a unified approach for improving how science is done across the geosciences and beyond. Plain Language Summary: The way that scientific research is designed and carried out influences who and what benefits from the research outcomes, and how transferable those outcomes are. ICON principles are a tool designed to help scientists maximize the mutual benefit and transferability of their work. These principles are based on intentionally designing research to Integrate disciplines, Coordinate use of consistent methods, openly share ideas/data, and Network with diverse stakeholders for mutual benefit. The relevance of these principles and how to best use them across a spectrum of research is unknown. A collection of commentary articles was crowdsourced from across the geosciences to fill this gap. We report on the creation of the collection and summarize themes that emerged across the 19 articles written by 181 researchers. The articles indicate that the geosciences community sees significant value in using ICON principles, while acknowledging there are risks as well. We also observed that ICON principles should be considered a flexible tool to meet diverse needs. ICON principles represent a unified approach that can be used across the geosciences to improve how research is designed and implemented with the aim of maximizing the benefits and transferability of research efforts within and beyond the research team. Key Points: All 19 represented AGU sections agree that ICON science principles are key to producing stronger, more robust, and more equitable scienceThe benefits of all ICON principles outweigh associated costs, but risks need to be understood and mitigatedICON principles are not static; details of their use are context dependent, emphasizing a need for resources to guide ICON implementation [ABSTRACT FROM AUTHOR]

Smartphones have gained immense popularity among people and they incorporate several sophisticated capabilities like sensors that broadens the domain of usability. Consequently, smartphone based crowdsourcing methods show importance as an efficient data acquisition strategy. GPS receiver and sensors like accelerometer and gyroscope can be used to gather data which helps identify the travel mode of the smartphone user. Numerous research have been conducted regarding the area of concern and this study uses GPS and Accelerometer data collected from smartphones using crowdsourced methods to develop a robust public transport mode detection approach using machine learning algorithms.

Abstract Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion‐activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best‐practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open‐grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Evaluating exposure to radio frequencies (RF) at population-scale is important for conducting sound epidemiological studies about possible health impact of RF radiations. Numerous studies reported population exposure to RF radiations used in wireless telecommunication technologies, but used very small population samples. In this context, the real exposure of the population at scale remains poorly understood. Here, to the best of our knowledge, we report the largest crowd-based measurement of population exposure to RF produced by cellular antennas, Wi-Fi access points, and Bluetooth devices for 254,410 unique users in 13 countries from January 2017 to December 2020. First, we present methods to assess the population exposure to RF radiations using smartphone measurements obtained using the ElectroSmart Android app. Then, we use these methods to evaluate and characterize the evolution of RF exposure. We show that total exposure has been multiplied by 2.3 in the four-year period considered, with Wi-Fi as the largest contributor. The cellular exposure levels are orders of magnitude lower than regulation limits and are not correlated to national regulation policies. The population tends to be more exposed at home; for half of the study subjects, personal Wi-Fi routers and Bluetooth devices contributed to more than 50% of their total exposure. In this work, we showcase how crowdsource-based data allow large-scale and long-term assessment of population exposure to RF radiations.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Despite the significant amount of energy spent on Heating, Ventilation, and Air Conditioning (HVAC) at universities, thermal comfort conditions in campus buildings are frequently poor. Conventional HVAC management systems at universities are typically out of the hands of building occupants and facilities management departments have limited resources to involve them. These factors can lead to over-heating or over-cooling and undiagnosed mechanical issues. Previous research has shown that thermal comfort feedback, or participatory thermal sensing, can simultaneously improve energy efficiency and occupant comfort in university buildings. However, these studies have been limited to single campus buildings and restricted populations of occupants. The success, scalability, and sustainability of any participatory thermal sensing program is dependent upon ongoing participation that is meaningful to occupants and useful to facilities management. Therefore, research is warranted to explore patterns of voluntary participation, thermal comfort, and occupant satisfaction in extended, campus-wide deployments of participatory thermal sensing programs. The present research begins to address these gaps in the context of an 18-month, campus-wide deployment of TherMOOstat, a participatory thermal sensing app and HVAC management system at University of California, Davis.

The use of non-probability Internet panels and crowdsource websites is increasing in gambling research. These paid online sampling methods offer a convenient and inexpensive recruitment strategy. The quality of data may be questionable due to careless responding and identity misrepresentation which can bias study results. It is necessary to investigate data quality given the important implications of gambling research in guiding policy decisions, public health initiatives, and treatments. In this review article, we 1) critically analyze the advantages and limitations of paid online recruitment methods, including associated threats to data quality in the gambling literature; 2) present findings from a rapid review of gambling studies using online panel and crowdsource data; and 3) outline recommendations for maximizing data quality and trustworthiness of findings. Substantially overinflated problem gambling rates were found in the 63 gambling studies we reviewed; less than one-quarter had incorporated data quality checks and reported participation rates. Future studies should incorporate pre-registration of methodology and analysis plans, robust participant screening procedures, mid-survey attention and response consistency items, and an analysis of response quality post data collection. Applying these recommendations to nonrepresentative online panel and crowdsource-based studies may enhance the replicability of findings in additional studies using representative samples [ABSTRACT FROM AUTHOR]

Abstract Psoriasis is a skin condition that affects over 100 million people worldwide, while multiple myeloma (MM) accounts for 10% of all hematologic malignancies in the US. There has been limited research on the intersection of psoriasis and MM, and clinicians often face difficult decisions in treating patients diagnosed with both conditions. For instance, the management of psoriasis with systemic immunotherapies in MM patients can be challenging because of concern about immunosuppression and possible worsening of MM. Online crowdsourcing platforms have recently become innovative tools that can actively empower patients in scientific research by enabling the contribution of health data. One such platform, HealthTree®, helps MM patients find optimal myeloma treatments and has registered > 6000 patients, many of whom have uploaded medical records and genetic profiles. By taking advantage of patient health data available on HealthTree, researchers can gain a greater understanding of the clinical characteristics and treatment responses of patients diagnosed with psoriasis and MM. In this case series, we first report a psoriasis and MM patient treated with the IL-17 inhibitor ixekizumab who demonstrated a temporary, 2-month improvement in MM biomarkers (M-protein, kappa, and kappa:lambda ratio). We then report on the clinical characteristics of 18 patients with verified profiles on HealthTree indicating concurrent psoriasis and MM conditions. We surveyed gender, age, psoriasis type, psoriasis treatment history, myeloma type, myeloma genetic features, and myeloma association with bone damage, hypercalcemia, or osteopenia. Four patients were treated with systemic immunomodulators for psoriasis, with responses suggesting that these therapies did not worsen MM progression. Our results validate crowdsourcing as a way to assess patient demographics and treatment responses for use in dermatology research. We examine the demographics of patients diagnosed with psoriasis and MM and investigate the use of systemic immunomodulators for treatment of psoriasis in MM patients.

Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage to properties. A number of cities are subject to monsoons influences and hence face the disaster almost every year. Early notification of flood incident could benefit the authorities and public to devise both short and long terms preventive measures, to prepare evacuation and rescue mission, and to relieve the flood victims. Geographical locations of affected areas and respective severities, for instances, are among the key determinants in most flood administration. Thus far, an effective means of anticipating flood in advance remains lacking. Existing tools were typically based on manually input and prepared data. The processes were tedious and thus prohibitive for real-time and early forecasts. Furthermore, these tools did not fully exploit more comprehensive information available in current big data platforms. Therefore, this paper proposes a novel flood forecasting system based on fusing meteorological, hydrological, geospatial, and crowdsource big data in an adaptive machine learning framework. Data intelligence was driven by state-of-the-art learning strategies. Subjective and objective evaluations indicated that the developed system was able to forecast flood incidents, happening in specific areas and time frames. It was also later revealed by benchmarking experiments that the system configured with an MLP ANN gave the most effective prediction, with correct percentage, Kappa, MAE and RMSE of 97.93, 0.89, 0.01 and 0.10, respectively.

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

Background: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality., Objective: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data., Methods: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips., Results: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance., Conclusions: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems., (©Nicole M Duggan, Mike Jin, Maria Alejandra Duran Mendicuti, Stephen Hallisey, Denie Bernier, Lauren A Selame, Ameneh Asgari-Targhi, Chanel E Fischetti, Ruben Lucassen, Anthony E Samir, Erik Duhaime, Tina Kapur, Andrew J Goldsmith. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.07.2024.)

Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences. The study also highlights crowdsourcing usage in the field of software engineering. All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed. The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing. [ABSTRACT FROM AUTHOR]

Experimental evidence suggests that crowdsourced online experiments, where suitable, may produce data with quality comparable to in-lab studies (Buhrmester, Kwang, Gosling, 2011). The absence of a reliable screening method for headphones and diotic auditory context (perfect separation of the stereo channels) is one of the main reasons why online crowdsourcing is rarely possible for auditory studies. Here we show that the interference beating phenomenon can be used as a screening method for diotic context with satisfactory results. We collected data through an in-lab experiment from over 2000 participants to test the method's performance against the reference, achieving Cohen's Kappa of 0.79 (95% CI, [0.52, 1.06], p<0.001), yielding "Substantial agreement". The in-lab and online results suggest that the method introduced in this study is suitable, and therefore, an enabler of auditory online crowdsourced studies. [ABSTRACT FROM AUTHOR]

Abstract Mobile ads are plagued with fraudulent clicks which is a major challenge for the advertising community. Although popular ad networks use many techniques to detect click fraud, they do not protect the client from possible collusion between publishers and ad networks. In addition, ad networks are not able to monitor the user’s activity for click fraud detection once they are redirected to the advertising site after clicking the ad. We propose a new crowdsource-based system called Click Fraud Crowdsourcing (CFC) that collaborates with both advertisers and ad networks in order to protect both parties from any possible click fraudulent acts. The system benefits from both a global view, where it gathers multiple ad requests corresponding to different ad network-publisher-advertiser combinations, and a local view, where it is able to track the users’ engagement in each advertising website. The results demonstrated that our approach offers a lower false positive rate (0.1) when detecting click fraud as opposed to proposed solutions in the literature, while maintaining a high true positive rate (0.9). Furthermore, we propose a new mobile ad charging model that benefits from our system to charge advertisers based on the duration spent in the advertiser’s website.

ABSTRACT The existence of a link between the gut microbiome and autism spectrum disorder (ASD) is well established in mice, but in human populations, efforts to identify microbial biomarkers have been limited due to a lack of appropriately matched controls, stratification of participants within the autism spectrum, and sample size. To overcome these limitations, we crowdsourced the recruitment of families with age-matched sibling pairs between 2 and 7 years old (within 2 years of each other), where one child had a diagnosis of ASD and the other did not. Parents collected stool samples, provided a home video of their ASD child’s natural social behavior, and responded online to diet and behavioral questionnaires. 16S rRNA V4 amplicon sequencing of 117 samples (60 ASD and 57 controls) identified 21 amplicon sequence variants (ASVs) that differed significantly between the two cohorts: 11 were found to be enriched in neurotypical children (six ASVs belonging to the Lachnospiraceae family), while 10 were enriched in children with ASD (including Ruminococcaceae and Bacteroidaceae families). Summarizing the expected KEGG orthologs of each predicted genome, the taxonomic biomarkers associated with children with ASD can use amino acids as precursors for butyragenic pathways, potentially altering the availability of neurotransmitters like glutamate and gamma aminobutyric acid (GABA). IMPORTANCE Autism spectrum disorder (ASD), which now affects 1 in 54 children in the United States, is known to have comorbidity with gut disorders of a variety of types; however, the link to the microbiome remains poorly characterized. Recent work has provided compelling evidence to link the gut microbiome to the autism phenotype in mouse models, but identification of specific taxa associated with autism has suffered replicability issues in humans. This has been due in part to sample size that sufficiently covers the spectrum of phenotypes known to autism (which range from subtle to severe) and a lack of appropriately matched controls. Our original study proposes to overcome these limitations by collecting stool-associated microbiome on 60 sibling pairs of children, one with autism and one neurotypically developing, both 2 to 7 years old and no more than 2 years apart in age. We use exact sequence variant analysis and both permutation and differential abundance procedures to identify 21 taxa with significant enrichment or depletion in the autism cohort compared to their matched sibling controls. Several of these 21 biomarkers have been identified in previous smaller studies; however, some are new to autism and known to be important in gut-brain interactions and/or are associated with specific fatty acid biosynthesis pathways.

Given the increasing popularity of crowdsourced fundraisers, understanding how characteristics of funding initiatives and donors influence donations has critical real-world implications. Across two studies, we identified potential situational factors most conducive to successful crowdsourcing while also determining whether individual differences in various personality factors predicted differing levels of donation. Participants in Study 1 (MAge = 19.99; 309 women, 75 men) viewed descriptions that manipulated donation type (organizer donation, anonymous donation, no donation) and type of fundraiser (self-organized, other-organized), and reported their willingness to donate to an individual’s medical treatment and completed inventories assessing Big Five personality traits. In Study 2 participants (MAge = 20.22; 322 women, 102 men) viewed vignettes describing fundraisers for an individual’s vacation fun and completed inventories assessing participantslevels of narcissism using the Pathological Narcissism Inventory. Higher agreeableness in men predicted heightened donation interest, regardless of type of cause, particularly when someone else has already donated (Study 1). Unexpectedly, narcissistic men and women both reported heightened donation interest (Study 2). We frame these findings through a framework assessing the adaptive utility of altruism as a function of personality in modern donation contexts.

Given the increasing popularity of crowdsourced fundraisers, understanding how characteristics of funding initiatives and donors influence donations has critical real-world implications. Across two studies, we identified potential situational factors most conducive to successful crowdsourcing while also determining whether individual differences in various personality factors predicted differing levels of donation. Participants in Study 1 (MAge = 19.99; 309 women, 75 men) viewed descriptions that manipulated donation type (organizer donation, anonymous donation, no donation) and type of fundraiser (self-organized, other-organized), and reported their willingness to donate to an individual’s medical treatment and completed inventories assessing Big Five personality traits. In Study 2 participants (MAge = 20.22; 322 women, 102 men) viewed vignettes describing fundraisers for an individual’s vacation fun and completed inventories assessing participants levels of narcissism using the Pathological Narcissism Inventory. Higher agreeableness in men predicted heightened donation interest, regardless of type of cause, particularly when someone else has already donated (Study 1). Unexpectedly, narcissistic men and women both reported heightened donation interest (Study 2). We frame these findings through a framework assessing the adaptive utility of altruism as a function of personality in modern donation contexts. [ABSTRACT FROM AUTHOR]

Background: The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore., Objective: This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats., Methods: This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities., Results: Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore., Conclusions: As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events., (©Nomita Divi, Jaś Mantero, Marlo Libel, Onicio Leal Neto, Marinanicole Schultheiss, Kara Sewalk, John Brownstein, Mark Smolinski. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 15.03.2024.)

The SARS-CoV-2 pandemic has rapidly saturated healthcare resources across the globe and has led to a restricted screening process, hindering efforts at comprehensive case detection. This has not only facilitated community spread but has also resulted in an underestimation of the true incidence of disease, a statistic which is useful for policy making aimed at controlling the current pandemic and in preparing for future outbreaks. In this perspective, we present a crowdsourced platform developed by us for the true estimation of all SARS-CoV-2 infections in the community, through active self-reporting and layering other authentic datasets. The granularity of data captured by this system could prove to be useful in assisting governments to identify SARS-CoV-2 hotspots in the community facilitating lifting of restrictions in a controlled fashion.

Growing attention on the benefits of non-motorized travel has increased the demand for accurate and timely pedestrian and bicycle travel data. Advancements in technologies and the proliferation of smartphones have created new data sources that can help eliminate limitations related to small sample size and infrequent updates due to limited resources. This study reviews the emerging data sources and their current use, focusing on non-motorized travel monitoring. In this study, the emerging data are categorized into mode-unspecified and mode-specified data based on whether the mode used can be detected with no or little effort. While mode-unspecified data are collected without sorting out non-motorized travelers, mode-specified data at least know who (which mode) is being monitored. So far, commercial vendors provide a vast volume of mode-unspecified data, but their products have been mainly used for motorized trips or are in initial stages of development. Meanwhile, readily available data sources and their applications are more concentrated on mode-specified data, which have enabled varying non-motorized travel studies—including travel pattern identification, route-choice modeling, crash/air pollution exposure estimation, and new facility provision evaluation—but are mostly focused on bicycling. Despite the potential of emerging data, their use also has several challenges, such as limited mode inference, sample bias, and lack of detailed trip/traveler information due to privacy issues. More efforts are needed, such as improving data accuracy and developing robust data fusion techniques, to be able to fully utilize the emerging data sources.

The history of women's exclusion and invisibility in cities is well charted, yet young women's experience of sexual harassment and assault has been difficult to quantify. This article discusses the Free to Be project initiated by Plan International in 2018. In partnership with Monash University's XYX Lab and CrowdSpot, the crowdmapping web app enabled young women in Delhi, Kampala, Lima, Madrid, and Sydney to identify and share their experiences of public spaces. It is believed to be the most ambitious global crowdsourced data collection project on street harassment ever undertaken. By using coding and visual data techniques, the data demonstrated the scale of the problem and the urgent need for city-level decision makers, as well as other members of society, to take action. This article outlines the findings from Free to Be, and explores the ways data and activism led by girls and young women are a powerful method for creating change. [ABSTRACT FROM AUTHOR]

Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes. [ABSTRACT FROM AUTHOR]

Vehicular crowd sensing systems are designed to achieve large spatio-temporal sensing coverage with low-cost in deployment and maintenance. For example, taxi platforms can be utilized for sensing city-wide air quality. However, the goals of vehicle agents are often inconsistent with the goal of the crowdsourcer. Vehicle agents like taxis prioritize searching for passenger ride requests (defined as task requests), which leads them to gather in busy regions. In contrast, sensing systems often need to sample data over the entire city with a desired distribution (e.g., Uniform distribution, Gaussian Mixture distribution, etc.) to ensure sufficient spatio-temporal information for further analysis. This inconsistency decreases the sensing coverage quality and thus impairs the quality of the collected information. A simple approach to reduce the inconsistency is to greedily incentivize the vehicle agents to different regions. However, incentivization brings challenges, including the heterogeneity of desired target distributions, limited budget to incentivize more vehicle agents, and the high computational complexity of optimizing incentivizing strategies. To this end, we present a vehicular crowd sensing system to efficiently incentivize the vehicle agents to match the sensing distribution of the sampled data to the desired target distribution with a limited budget. To make the system flexible to various desired target distributions, we formulate the incentivizing problem as a new type of non-linear multiple-choice knapsack problem, with the dissimilarity between the collected data distribution and the desired distribution as the objective function. To utilize the budget efficiently, we design a customized incentive by combining monetary incentives and potential task (ride) requests at the destination. Meanwhile, an efficient optimization algorithm, iLOCuS, is presented to plan the incentivizing policy for vehicle agents to decompose the sensing distribution into two distinct levels: time-location level and vehicle level, to approximate the optimal solution iteratively and reduce the dissimilarity objective. Our experimental results based on real-world data show that our system can reduce up to 26.99 percent of the dissimilarity between the sensed and target distributions compared to benchmark methods. [ABSTRACT FROM AUTHOR]

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a "crowdsourcing-change detection-classification-expert knowledge" vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China's terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

This second article (in a series of six), presents an examination of the existing conditions that limit access to Peirce's corpus, and offers some detailed account of what reasoning technology could be used to improve these conditions. Peirce's theory of recursive inquiry, relying on his pragmatism, gives us the basis for designing technology to support effective access to his archive, and to pursue future improvement by developing a crowdsourced community of inquiry for continuing semiosis.

Crowdsensing is more and more used nowadays for indoor localization based on Received Signal Strength (RSS) fingerprinting. It is a fast and efficient solution to maintain fingerprinting databases and to keep them up-to-date. There are however several challenges involved in crowdsensing RSS fingerprinting data, and these have been little investigated so far in the current literature. Our goal is to analyse the impact of various error sources in the crowdsensing process for the purpose of indoor localization. We rely our findings on a heavy measurement campaign involving 21 measurement devices and more than 6800 fingerprints. We show that crowdsensed databases are more robust to erroneous RSS reports than to malicious fingerprint position reports. We also evaluate the positioning accuracy achievable with crowdsensed databases in the absence of any available calibration.

Complex and costly discharge measurements are usually required to calibrate hydrological models. In contrast, water level measurements are straightforward, and practitioners can collect them using a crowdsourcing approach. Here we report how crowdsourced water levels were used to calibrate a lumped hydrological model. Using six different calibration schemes based on discharge or crowdsourced water levels, we assessed the value of crowdsourced data for hydrological modeling. As a benchmark, we used estimated discharge from automatically measured water levels and identified 2,500 parameter sets that resulted in the highest Nash‐Sutcliffe‐Efficiencies in a Monte Carlo‐based uncertainty framework (Q‐NSE). Spearman‐Rank‐Coefficients between crowdsourced water levels and modeled discharge (CS‐SR) or observed discharge and modeled discharge (Q‐SR) were used as an alternative way to calibrate the model. Additionally, we applied a filtering scheme (F), where we removed parameter sets, which resulted in a runoff that did not agree with the water balance derived from measured precipitation and publicly available remotely sensed evapotranspiration data. For the Q‐NSE scheme, we achieved a mean NSE of 0.88, while NSEs of 0.43 and 0.36 were found for Q‐SR and CS‐SR, respectively. Within the filter schemes, NSEs approached the values achieved with the discharge calibrated model (Q‐SRF 0.7, CS‐SRF 0.69). Similar results were found for the validation period with slightly better efficiencies. With this study we demonstrate how crowdsourced water levels can be effectively used to calibrate a rainfall‐runoff model, making this modeling approach a potential tool for ungauged catchments. Key Points: Crowdsourced water levels were used to calibrate a conceptual rainfall‐runoff modelCombining crowdsourced water levels with a water balance derived from remotely sensed evapotranspiration increases the model efficiencyTime‐variable‐collected water levels can be converted into a continuous discharge time series using a simple model approach [ABSTRACT FROM AUTHOR]

Precision agriculture is highly dependent on the collection of high quality ground truth data to validate the algorithms used in prescription maps. However, the process of collecting ground truth data is labor-intensive and costly. One solution to increasing the collection of ground truth data is by recruiting citizen scientists through a crowdsourcing platform. In this study, a crowdsourcing platform application was built using a human-centered design process. The primary goals were to gauge users’ perceptions of the platform, evaluate how well the system satisfies their needs, and observe whether the classification rate of lambsquarters by the users would match that of an expert. Previous work demonstrated a need for ground truth data on lambsquarters in the D.C., Maryland, Virginia (DMV) area. Previous social interviews revealed users who would want a citizen science platform to expand their skills and give them access to educational resources. Using a human-centered design protocol, design iterations of a mobile application were created in Kinvey Studio. The application, Mission LQ, taught people how to classify certain characteristics of lambsquarters in the DMV and allowed them to submit ground truth data. The final design of Mission LQ received a median system usability scale (SUS) score of 80.13, which indicates a good design. The classification rate of lambsquarters was 72%, which is comparable to expert classification. This demonstrates that a crowdsourcing mobile application can be used to collect high quality ground truth data for use in precision agriculture.

Growing demand for software has attracted the attention of the software development industry. Crowdsourced software development has provided a new method for the software industry to produce quality software based on an open-call format. Selecting an appropriate task to develop (developer-end) or evaluate (platform-end) is one of the primary problems in this type of open-call format. Receiving or assigning an improper task to an improper crowdsource (CS) developer does not only decrease the quality of the software deliverables, but also causes overburden on both the platform and the developers. To solve this problem, sorting the tasks based on the developers' human characteristics may increase task relevancy for developers, which can accelerate efficiency and lessen complexity. Thus, this paper has conducted an empirical experiment to measure the influence of personality on task selection based on the important characteristics of a task: money, time, and type. A total of 83 students from the University of Sindh voluntarily participated in four different short-duration rounds of task development using the developed CS platform. The personality types of the participants were measured based on the Myers-Briggs type indicator. In addition, a complex network technique called weighted degree centrality was applied to identify the most suitable personality for task sorting based on money or complexity attractions (i.e., time or type). Based on the results, it can be observed that personality has a significant relationship with task selection. For instance, developers with intuitive (N) and feeling (F) personality traits are primarily focused on the time duration of a project.