Aggregation Error Research Articles

A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data.

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.

Distributed Event-Triggered Adaptive Coordinated Trajectory Tracking Control of Multi-USVs Based on the Aggregate Tracking Error

Aiming to solving the nonlinear coordinated trajectory tracking control problem of multi-USVs under the influence of slowly changing and uncertain environmental disturbances, reducing energy consumption and ensuring tracking performance and stability, a distributed event-triggered adaptive coordinated trajectory tracking controller (DET-ACTTC) for multiple unmanned surface vessels (multi-USVs) is proposed based on the undirected communication topology. By introducing a virtual leader, a leader-follower formation is used to generate the coordinated tracking errors. Based on the expected formation and states, the aggregate tracking errors are defined, and event-triggered measurement errors are constructed to design the event-triggered conditions. An adaptive term is used to compensate for external environment disturbances, and the DET-ACTTC is deduced for each USV in the group. The tracking errors of the event-triggered coordinated trajectory tracking control (ETCTTC) system gradually converge to zero, and Zeno behavior is avoided. The theoretical results are verified by simulations.

Comparison between Different Hybrid Life Cycle Assessment Methodologies: A Review and Case Study of Biomass-based p-Xylene Production

Two common life cycle assessment (LCA) methods are process LCA and economic input–output LCA, which suffer from truncation error and aggregation error, respectively. Many hybridization techniques a...

Group-Based General Epidemic Modeling for Spreading Processes on Networks: GroupGEM

We develop a group-based continuous-time Markov general epidemic modeling (GroupGEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups. Aggregating nodes by groups, the state-space becomes smaller than the one of individual-based approach at the cost of an aggregation error, which is bounded by the well-known isoperimetric inequality. We also develop a mean-field approximation of this framework to reduce the state-space size further. Finally, we extend the GroupGEM to multilayer networks. Individual-based frameworks are in general not computationally efficient. However, the individual-based approach is essential when the objective is to study the local dynamics at the individual level. Therefore, we propose a group-based framework to reduce the computational time of the Individual-based generalized epidemic modeling framework (GEMF) but retain its advantages.

Minimizing aggregation errors when measuring potential access to services for social groups at the city scale

This research explores how to minimize aggregation errors when measuring potential access to services for social groups at the city scale. It develops a cadastral and address-based population weighting technique, the Household Space Weighting, to reduce aggregation errors caused by using population weighted centroids when applying the Have Their Centre In criterion (the Population Weighted Centroid technique). The Household Space Weighting technique is formally tested in a case study of General Practitioner practices in Newcastle upon Tyne, UK. The findings suggest that the Population Weighted Centroid technique produces inaccurate population estimates for 267 out of 910 output areas (29%) in the city. When applying the two techniques to measure access for social groups at the city scale, the absolute difference in the percentage of each social group with potential accessibility is 9–10% and the relative difference in the percentage of each social group with potential access is 18–20%, taking into account the overlay of service areas at the city scale. This suggests that if service planners or policy makers want to measure potential accessibility or potential access of social groups to services for cities, it would be useful to apply a more accurate technique, or at least be aware of the implications of applying the Population Weighted Centroid technique.

An Adaptive Bayesian Melding Method for Reliability Evaluation Via Limited Failure Data: An Application to the Servo Turret

In the early stage of product development, reliability evaluation is an indispensable step before launching a product onto the market. It is not realistic to evaluate the reliability of a new product by a host of reliability tests due to the limiting factors of time and test costs. Evaluating the reliability of products in a short time is a challenging problem. In this paper, an approach is proposed that combines a group of experts’ judgments and limited failure data. Novel features of this approach are that it can reflect various kinds of information without considering the individual weight and reduces aggregation error in the uncertainty quantification of multiple inconsistent pieces of information. First, an expert system is established by the Bayesian best–worst method and fuzzy logic inference, which collects and aggregates a group of expert opinions to estimate the reliability improvement factor. Then, an adaptive Bayesian melding method is investigated to generate a posterior by inaccurate prior knowledge and limited test data; this method is made more computationally efficient by implementing an improved sampling importance resampling algorithm. Finally, an application for the reliability evaluation of a subsystem of a CNC lathe is discussed to illustrate the framework, which is shown to validate the reasonability and robustness of our proposal.

Why do latent fingerprint examiners differ in their conclusions?

Forensic latent print examiners usually but do not always reproduce each other’s conclusions. Using data from tests of experts conducting fingerprint comparisons, we show the extent to which differing conclusions can be explained in terms of the images, and in terms of the examiners. Some images are particularly prone to disagreements or erroneous conclusions; the highest and lowest quality images generally result in unanimous conclusions. The variability among examiners can be seen as the effect of implicit individual decision thresholds, which we demonstrate are measurable and differ substantially among examiners; this variation may reflect differences in skill, risk tolerance, or bias. Much of the remaining variability relates to inconsistency of the examiners themselves: borderline conclusions (i.e., close to individual decision thresholds) often were not repeated by the examiners themselves, and tended to be completed more slowly and rated difficult. A few examiners have significantly higher error rates than most: aggregate error rates of many examiners are not necessarily representative of individual examiners. The use of a three-level conclusion scale does not precisely represent the underlying agreements and disagreements among examiners. We propose a new method of quantifying examiner skill that would be appropriate for use in proficiency tests. These findings are operationally relevant to staffing, quality assurance, and disagreements among experts in court.

Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

Intelligent Internet of Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence." This shall unleash the full potential of intelligent IoT in a plethora of exciting applications, such as self-driving cars, unmanned aerial vehicles, healthcare, robotics, and supply chain finance. These applications drive the need to develop revolutionary computation, communication, and artificial intelligence technologies that can make low-latency decisions with massive realtime data. To this end, federated machine learning, as a disruptive technology, has emerged to distill intelligence from the data at the network edge, while guaranteeing device privacy and data security. However, the limited communication bandwidth is a key bottleneck of model aggregation for federated machine learning over radio channels. In this article, we shall develop an overthe- air computation-based communication-efficient federated machine learning framework for intelligent IoT networks via exploiting the waveform superposition property of a multi-access channel. Reconfigurable intelligent surface is further leveraged to reduce the model aggregation error via enhancing the signal strength by reconfiguring the wireless propagation environments.

Development of a Mesoscale Inversion System for Estimating Continental‐Scale CO2 Fluxes

AbstractComputational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical‐twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental‐scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high‐temporal‐resolution information from the North American tower network, to extract high‐resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high‐resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles.

An assessment of poverty determinants in U.S. census tracts, 1970–2010

AbstractThis paper deals with the determinants of poverty in the U.S. at the census tract level. By proceeding in this way, potential aggregation errors that may occur when using larger areas as the unit of observation are avoided. Our empirical analysis exploits a geographically consistent data set covering the period 1970–2010 and all metropolitan statistical areas. The estimations have been carried out using a system generalized method of moments framework for dynamic panels. Our results show that poverty is a persistent phenomenon, especially in tracts with higher levels. Those variables with a more robust relationship with poverty reflect labor market conditions. We also obtain evidence of a higher vulnerability of women to poverty. These findings have been framed within the debate about the optimal design of local development strategies. The main conclusion drawn is that place‐based and person‐centered policies can be considered as complementary rather than as substitutes.

Advancing Community Pharmacy Practice – A Technician Product Verification Pilot to Optimize Care

Elevating the technical role of pharmacy technicians to perform Technician Product Verification (TPV) is one strategy that has shown promise to optimize pharmacy practice models. This is done by better positioning pharmacists to provide clinical care, in line with their education and expertise. TPV permits a Validated Pharmacy Technician, as defined by the Wisconsin Pharmacy Examining Board, to verify the accuracy of a product filled by another technician. The pharmacist maintains responsibility for assessing the clinical appropriateness of the prescription, including drug utilization review, data entry, and patient counseling. During the study period, 12,891 pharmacist-verified prescriptions (baseline) and 27,447 Validated Pharmacy Technician-verified prescriptions were audited for accuracy. The aggregate verification error rate for pharmacist-verified prescriptions was 0.16% and 0.01% for Validated Pharmacy Technician-verified prescriptions. The mean error rate was significantly less for Validated Pharmacy Technician-verified prescriptions than for pharmacist-verified prescriptions (0.19 ± 0.174 % vs 0.03 ± 0.089 %, p=0.020) (Figure 3). This suggests TPV in the community pharmacy setting maintained patient safety. In this study, Validated Pharmacy Technicians were shown to be more accurate than pharmacists at performing product verification. The ability to delegate the product verification task holds the potential to free up pharmacist time for increased direct patient care. Increasing direct patient care by pharmacists in community pharmacies may have significant implications for improving patient outcomes and pharmacy quality.

Fund accounting and government-wide financial reporting during the Pre-IPSAS implementation era from a Nigerian experience

PurposeThis paper assessed accuracy level in accounting for government funds in Nigeria's federal treasury and their faithful presentation in government financial reporting. It aimed to determine whether the reported annual balances in Nigeria's financial reporting were reliable or otherwise. Data used in analysis were obtained from secondary sources from federal treasury.Design/methodology/approachEx-post “facto” analysis method was adopted in the study involving the use of statistical techniques of absolute or aggregate mean percentage error derived from differences between recomputed and published fund balances and was employed. This was augmented with interactive review meetings of the initial case research report with the management of Nigeria's audit agency.FindingsResults distilled from the consolidated revenue fund (CRF), development fund and public debt show that recomputed values were greater than the fund balances in the gazetted financial statements. Results for contingency fund (CTF), federation account fund (FAF), special trust fund (STF) and sundry deposit fund yield equal figures and accurate. The paper concludes that there were serial understatements of the core public fund balances in the financial statements over the years. This trend of reporting incorrect in three core public funds in financial statements rendered Nigeria's financial position unreliable in the affected years for decisions. It also facilitated frauds, mismanagement of funds and corrupt practices.Research limitations/implicationsThe scope of the research is restricted to assessment of degree of accuracy in fund accounting, faithful representation of the respective fund balance in the liabilities side of FGN balance sheet and the reliability of the financial position. But, it did not consider or cover the implementation of International Public Sector Accounting Standards (IPSASs) in federal treasury since FGN had not issued any full IPSAS–oriented financial statements as on 2015.Practical implicationsIdentification of deficiencies in fund account balances, structural defects in fund accounting and acts of understatement of carrying balances in CRF and capital development fund (CDF) implies that the aggregate core fund liabilities reported in financial statement of government entities without corresponding assets do not actually reflect a true and fair financial position in some countries. It reveals remarkable degree of financial information asymmetry in government financial reporting. Illusionary fund accounting has direct linkage to poor fiscal governance in many sovereign with associated sub-optimal delivery of public goods and service level distress syndrome in many economies; lead to poverty, unemployment, crisis and macroeconomic disturbances.Social implicationsThe study contributes to the development of fund accounting system; strengthening government financial reporting architecture and practices. It provides framework for tracking financial information asymmetry in government financial reporting and mismanagement of public funds. It provides platform to effect necessary adjustment (correction) during the “first time 3-year adoption” adjustment window in Nigeria. Flowing from the findings, it advocates for institutionalization of government fund accounting standards and provides evidence for migration to accrual accounting system in countries that have not already implemented it. Evaluation system developed herein will improve fund management in federal treasury and contribute to efficient public financial management, good governance and enhance development of public accounting practice.Originality/valueThis exploratory empirical research is the one to ever evaluate accuracy level of fund accounting in sovereign entities and faithful representation in government's financial position prior to implementation of accrual accounting and financial reporting. The study established substantial level of illusionary accounting for public funds and information asymmetry in published government's financial reporting. It is necessary to rectify these discrepancies in fund accounting and financial reporting prior to and or during the first three years of the IPSAS transition implementation programme. These research deliverables provide adopters with relevant data for adjustment accounting during the transition period in strengthening public financial reporting in order to realize the benefit of full IPSAS accrual accounting.

Application of surrogate models in estimation of storm surge:A comparative assessment

Coastal storm surge hazard assessment has received increased attention due to major hurricane events in the last two decades. Robust hazard assessment requires accurate and efficient storm surge prediction models; however, existing numerical models are either high-fidelity but computationally demanding or low-fidelity but with real-time forecasting application. This dichotomy has prompted the development of surrogate models that leverage available synthetic/historical storm databases to build prediction models intended to better balance efficiency and accuracy. Despite numerous studies that have examined the application of various surrogate modeling methods in coastal response prediction, no study is available that compares all of the frequently used methods for storm surge prediction. Furthermore, in most of these studies, the discussion of the performance of these models is bounded to aggregated error metrics (e.g., RMSE, R). This study aims to provide a comprehensive framework for comparison and assessment of the performance of surrogate models based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Support Vector Regression (SVR) for predicting storm surge. The United States Army Corp of Engineers’ North Atlantic Coast Comprehensive Study (NACCS) database is used for developing the models at representative coastal locations. In this study, the performance of the models is explored by investigating the stability of performance across training sample sizes, identifying systematic trends in errors, assessing performance in predicting large target response quantities, and characterizing the distribution of error. The results indicate that the performance of surrogate models may be improved by use of physically-motivated parameter scaling and that the selection of a surrogate modeling method should be informed by factors such as consistency in performance under a range of target surge elevations. In particular, the results suggest that accuracy of the tested surrogate models may be a function of the target surge elevation. Furthermore, results suggest that model performance should be assessed using factors beyond aggregate error metrics because such measures (particularly when used without modification to focus on risk-significant events) may give incomplete information about performance of surrogate models.

Optimal spatial aggregation of space–time models and applications

Cancers are serious health concerns for every country. In the U.S. various cancer data are collected, monitored, and studied by the American Cancer Society (ACS). Since the data involves both spatial and temporal components, space–time models are useful for their analyses. Often these data (such as cancer rates) from varying geographical or political areas will be aggregated spatially to correspond to larger regions for analysis at that spatial scale. Methods to compare spatial aggregation schemes and to identify the optimal spatial aggregation are introduced. Specifically, some useful theorems and algorithms to determine the aggregation scheme that results in the minimum aggregate model error will be given, and they are demonstrated using the U.S. ovarian cancer incidence.

Investor Protection, Aggregate Accounting Earnings and Forecasts of Growth in GDP: International Evidence.

This study draws on the investor protection literature to examine differences in a country’s information environment that are likely to explain cross country variation in the extent to which macroeconomic forecasters take account of current earnings when forecasting future growth in GDP. Using a unique sample of analysts’ GDP forecasts from thirty countries, we find that economies with stronger levels of disclosure and enforcement regulation have a lower association between changes in aggregate earnings and errors in forecasts of GDP growth. The results are consistent with macroeconomic forecasters in countries with stronger investor protections taking greater account of changes in aggregate earnings when forecasting growth in GDP than analysts in countries with lower levels of investor protection.

DREAM: Online Control Mechanisms for Data Aggregation Error Minimization in Privacy-Preserving Crowdsensing

Nowadays, by integrating the smart devices carried by users with existing communication infrastructures to provide large-scale, fine-grained and complex sensing services, crowdsensing as a novel sensing paradigm has significantly enriched the applications of smart city and promoted the development of Internet of Things (IoT). However, privacy has become skyrocketing concern for crowdsensing and gravely affected the deployment of crowdsensing. In this paper, we present a framework to make the tradeoff between minimizing data aggregation error and guaranteeing system stability by jointly considering the privacy of participants, the randomness of sensing task arrival and the cost of platform. We propose an online control mechanism by exploiting Lyapunov stochastic optimization technique. Additionally, considering that, in reality, it always take different time for different tasks to make sensing decisions, we extend standard Lyapunov stochastic optimization technique to make separate decisions for different types of sensing tasks in consecutive time. Through rigorous theoretical analysis, we prove that our time-average data aggregation error is approximately optimal while still maintaining system stability. By carrying out extensive simulations, we demonstrate the superiority of our proposed mechanisms.

Spatiotemporal reconstructions of global CO2‐fluxes using Gaussian Markov random fields

AbstractAtmospheric inverse modeling is a method for reconstructing historical fluxes of green‐house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill‐conditioned, and assumptions on the fluxes are needed to constrain the solution. A common practice is to model the fluxes using latent Gaussian fields with a mean structure based on estimated fluxes from combinations of process modeling (natural fluxes) and statistical bookkeeping (anthropogenic emissions). Here, we reconstruct global CO2 flux fields by modeling fluxes using Gaussian Markov random fields (GMRFs), resulting in a flexible and computational beneficial model with a Matérn‐like spatial covariance and a temporal covariance arriving from an autoregressive model in time domain.In contrast to previous inversions, the flux is defined on a spatially continuous domain, and the traditionally discrete flux representation is replaced by integrated fluxes at the resolution specified by the transport model. This formulation removes aggregation errors in the flux covariance, due to the traditional representation of area integrals by fluxes at discrete points, and provides a model closer resembling real‐life space–time continuous fluxes.

Extended Energy Return on Investment of multiproduct energy systems

Energy Return on Investment (EROI) is an indicator of how efficient is an energy supply system. In the present study, the conventional approach of EROI assessment is extended to include the equivalent energy investment needed for offsetting the life cycle environmental impacts. Moreover, the issue of allocation of the invested energy among different by-products is addressed. The EROI of multiple products has been specified using different benchmarks of price, energy content, exergy content, and exergy costs. The application of the concept is demonstrated through a case study of an Iranian oil production unit. The overall conventional and environmentally-extended EROI values of the produced oil in Iran is estimated to be 26.8:1, 23.3:1, respectively. Also, when taking the downstream environmental emissions into account, the EROI will be as low as 6.8:1. This shows that the EROI may be overestimated by 75% if the embodied costs are not taken into account. The comparison of the aggregated EROI estimates based on state-properties (price, energy, and exergy) and disaggregated process-property (exergy cost), gives a measure of the error of aggregation. It is shown that this error may be as high as 11.5% in the case of the multi-product energy system of the studied case.

Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference (rAD¯) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a rAD¯ of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The rAD¯ of soil data was higher (5.6–26.3%) than rAD¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed (rAD¯ = 14.5%) than irrigated conditions (rAD¯ = 3.0%). The rAD¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Boundary Detection Using a Bayesian Hierarchical Model for Multiscale Spatial Data

Spatial boundary analysis has attained considerable attention in several disciplines including engineering, shape analysis, spatial statistics, and computer science. The inferential question of interest is often to identify rapid surface change of an unobserved latent process. Curvilinear wombling and crisp wombling (or fuzzy) are two major approaches that have emerged in Bayesian spatial statistics literature. These methods are limited to a single spatial scale even though data with multiple spatial scales are often accessible. Thus, we propose a multiscale representation of the directional derivative Karhunen–Loéve expansion to perform directionally based boundary detection. Taking a multiscale spatial perspective allows us, for the first time, to consider the concept of curvilinear boundary fallacy (CBF) error, which is a boundary detection analog to the ecological fallacy that is often studied in spatial change of support literature. Furthermore, we propose a directionally based multiscale curvilinear boundary error criterion to quantify CBF. We refer to this metric as the criterion for boundary aggregation error (BAGE), and use it to perform boundary detection. Several theoretical results are derived to motivate BAGE. In particular, we show that no BAGE exists when the directional derivatives of eigenfunctions of a KL expansion are constant across spatial scales. We illustrate the use of our model through a simulated example and an analysis of Mediterranean wind measurements data. Supplementary materials for this article are available online.