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Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language. Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion. -- Back cover.

Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data‐scarce regions are limited to only a few sparse locations at pre‐existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river‐gauge observations in Uganda and Kenya. The flood impact data contains semi‐quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river‐gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data‐scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning.

Position verification is an emerging field of quantum cryptography. Its goal is to verify whether a distant communicating party is telling the truth about where they are. However, the problem is usually formulated in a way that the position is the only credential of that party, which cannot guarantee uniqueness. In this study, the authors show how a practically secure position verification algorithm – assuming it exists – might be used to track (i.e. repeatedly verify the position) of some unique key or cipher text. To achieve this, they rely on pre-prepared position verification data called trackers. They also propose three algorithms that implement their general tracking scheme and examine some questions related to their security. These implementations include shuffling trackers into valuable data and hiding their memory address through a random permutation; using CNOT operations to entangle valuable data and trackers; and using random qubit strings from which either trackers or secret keys can be produced at will. These methods may be used to track a diplomatic package or reveal the location of a malicious party during a denial of service attack.

TransCelerate has developed a risk-based monitoring methodology that transforms clinical trial monitoring from a model rooted in source data verification (SDV) to a comprehensive approach leveraging cross-functional risk assessment, technology, and adaptive on-site, off-site, and central monitoring activities to ensure data quality and subject safety. Evidence suggests that monitoring methods that concentrate on what is critical for a study and a site may produce better outcomes than do conventional SDV-driven models. This article assesses the value of SDV in clinical trial monitoring via a literature review, a retrospective analysis of data from clinical trials, and an assessment of major and critical findings from TransCelerate member company internal audits. The results support the hypothesis that generalized SDV has limited value as a quality control measure and reinforce the value of other risk-based monitoring activities.

Most existing data integrity auditing protocols in cloud storage rely on proof of probabilistic data possession. Consequently, the sampling rate of data integrity verification is low to prevent expensive costs to the auditor. However, in the case of a multi-cloud environment, the amount of stored data will be huge. As a result, a higher sampling rate is needed. It will also have an increased cost for the auditor as a consequence. Therefore, this paper proposes a blockchain-based distributed data integrity verification protocol in multi-cloud environments that enables data verification using multi-verifiers. The proposed scheme aims to increase the sampling rate of data verification without increasing the costs significantly. The performance analysis shows that this protocol achieved a lower time consumption required for verification tasks using multi-verifiers than a single verifier. Furthermore, utilizing multi-verifiers also decreases each verifier’s computation and communication costs.

Concerns and limitations relating to data quality, reliability and accuracy hamper the use of citizen science initiatives in research and conservation. Valued for their cost‐effective and large data acquisition potential, citizen science platforms such as iNaturalist have been highlighted as beneficial tools to supplement monitoring using traditional data sources. However, intrinsic uncertainties in unverified observations stem from the nature of species being identified, the quality of uploaded media and georeferencing; these factors can limit the value of the data as they can result in inaccurate records. Verification of data prior to use is critical. This process can, however, be laborious and time‐consuming, with bias associated with the individual responsible for the task. To address this challenge this study developed a protocol for assigning confidence in iNaturalist observations, using marine alien and cryptogenic species observations from South Africa as a case study. A positive relationship was found between the accuracy of observations and confidence score. The inherent data quality assessment in iNaturalist, termed quality grade, was found to be an inadequate proxy for accuracy. The results of this study highlight the importance of the expert verification phase when using citizen science data. The confidence score facilitates a streamlined approach to the verification process by reducing the time taken to validate records, while assessing the three levels of uncertainty within observations and reducing researcher bias. It is recommended that this confidence score be used as an essential tool when using citizen science derived data. Data quality, reliability and accuracy have long been recognised as barriers to unlocking the full potential of citizen science derived data. This study developed a score to assign confidence in iNaturalist observations and streamline the verification process.

OBJECTIVEThe Halcyon comes with a reference beam data (RBD) set including percentage depth dose (PDD) curves, profiles, and output factors. Varian generates a pre-configured beam model in Eclipse treatment planning system (TPS) using this RBD. The aim of the current study is to validate RBD set of Halcyon 3.0, newly installed in our institute.METHODSThe PDD and lateral dose profiles were measured for open fields with sizes from 4×4 cm2 to 28×28 cm2in a water tank using a semiflex ionization chamber. The PDD and profiles were calculated with the AAA v17.1 in Eclipse for the same field sizes as described in ion chamber measurements. The depth of maximum dose (dmax), PDD value at depth of 10 cm, penumbra, field size, and lateral distance from the central axis at 90%, 75%, and 60% dose points of profile were analyzed.RESULTSThe difference of dmax values was 0.08 cm for all PDDs. A good agreement was obtained between calculated and measured PDD10 with a maximum difference of 0.35%. The measured field size and penumbra values indicated an excellent agreement with calculated values with a maximal discrepancy of 0.17 cm and 0.50 mm for all field sizes, respectively. The discrepancies between calculated and measured lateral distances for all field sizes were within 0.20 mm.CONCLUSIONThe TPS-calculated data using pre-configured beam model for Halcyon 3.0 were in good agreement with the measurements.

The encryption of user data is crucial when employing electronic health record services to guarantee the security of the data stored on cloud servers. Attribute-based encryption (ABE) scheme is considered a powerful encryption technique that offers flexible and fine-grained access control capabilities. Further, the multi-user collaborative access ABE scheme additionally supports users to acquire access authorization through collaborative works. However, the existing multi-user collaborative access ABE schemes do not consider the different weights of collaboration users. Therefore, using these schemes for weighted multi-user collaborative access results in redundant attributes, which inevitably reduces the efficiency of the ABE scheme. This paper proposes a revocable and verifiable weighted attribute-based encryption with collaborative access scheme (RVWABE-CA), which can provide efficient weighted multi-user collaborative access, user revocation, and data integrity verification, as the fundamental cornerstone for establishing a robust framework to facilitate secure sharing of electronic health records in a public cloud environment. In detail, this scheme employs a novel weighted access tree to eliminate redundant attributes, utilizes encryption version information to control user revocation, and establishes Merkle Hash Tree for data integrity verification. We prove that our scheme is resistant against chosen plaintext attack. The experimental results demonstrate that our scheme has significant computational efficiency advantages compared to related works, without increasing storage or communication overhead. Therefore, the RVWABE-CA scheme can provide an efficient and flexible weighted collaborative access control and user revocation mechanism as well as data integrity verification for electronic health record systems.

Citizen science includes a suite of research approaches that involves participation by citizens, who are not usually trained scientists, in scientific projects. Citizen science projects have the capacity to record observations of species with high precision and accuracy, offering the potential for collection of biological data to support a diversity of research investigations. Moreover, via the involvement of project participants, these projects have the potential to engage the public on scientific issues and to possibly contribute to changes in community knowledge, attitudes and behaviours. However, there are considerable challenges in ensuring that large-scale collection and verification of species data by the untrained public is a robust and useful long-term endeavor, and that project participants are indeed engaged and acquiring knowledge. Here, we describe approaches taken to overcome challenges in creation and maintenance of a website-based national citizen science initiative where fishers, divers, and other coastal users submit opportunistic photographic observations of ‘out-of-range’ species. The Range Extension Database and Mapping Project (Redmap Australia) has two objectives, 1/ ecological monitoring for the early detection of species that may be extending their geographic distribution due to environmental change, and 2/ engaging the public on the ecological impacts of climate change, using the public’s own data. Semi-automated ‘managed crowd-sourcing’ of an Australia-wide network of scientists with taxonomic expertise is used to verify every photographic observation. This unique system is supported by efficient workflows that ensures the rigor of data submitted. Moreover, ease of involvement for participants and prompt personal feedback has contributed to generating and maintaining ongoing interest. The design of Redmap Australia allows co-creation of knowledge with the community - without participants requiring formal training - providing an opportunity to engage sectors of the community that may not necessarily be willing to undergo training or otherwise be formally involved or engaged in citizen science. Given that capturing changes in our natural environment requires many observations spread over time and space, identifying factors and processes that support large-scale citizen science monitoring projects is increasingly critical.

Research on the challenge of enhancing information credibility in citizen journalism has shown that integrating GNSS satellite data with blockchain technology significantly increases the authenticity and transparency of reported data. The analysis of GNSS data, such as geographical coordinates and GNSS time, combined with blockchain verification methods, confirmed the high effectiveness of information verification. The publication presents the complete logic and architecture of the solution, which protects the rights of content creators, preventing manipulation and modification, and increases user trust in the platform. The research findings indicate a significant potential for further development of such solutions in information systems.