Reconciliation Problem Research Articles

A Rate-Compatible Solution to the Set Reconciliation Problem

A Rate-Compatible Solution to the Set Reconciliation Problem

An efficient methodology to select high-performance M-estimators for robust data reconciliation

Within the framework of Chemical Engineering, many exponential M-estimators have been proposed and tested. As usual, extensive simulation procedures are run to compare their performances. This work presents a deterministic methodology that reduces the computational endeavor of the comparative analysis. It becomes an effective technique for proposing and selecting high-performance M-estimators. The strategy uses the weight functions’ standardization to provide a common basis for the posterior application of Equivalent M-estimators and Tails Discrepancy definitions. In this way, identical M-estimators are figured out and those that behave likewise are identified. Whichever robust estimation procedure applied to estimate process variables and parameters can take advantage of the methodology results. For validation purposes, its conclusions are contrasted to the traditional performance measures obtained solving robust data reconciliation problems for two chemical processes.

Between the Placement Problem and the Reconciliation Problem. Philosophical Naturalism Today

Scientific naturalism—the conception according to which the natural sciences, and possibly physics alone, set the limits of ontology and epistemology—is characterized by a strong monistic tendency. For this reason, all versions of scientific naturalism face the so-called “placement problem”, which concerns the features of the ordinary view of the world that, at least prima facie, do not fit into the scientific view of the world (think of consciousness, moral properties, free will, and intentionality). To address this problem, scientific naturalists use three strategies: reductionism, eliminativism, and mysterianism—none of which, it is argued, produces satisfying results. Liberal naturalism opts instead for a pluralistic attitude in both ontology and epistemology but accepts a constraint according to which one should accept no entity or explanation that is incompatible with the scientific worldview. Liberal naturalism faces the “reconciliation problem”, which concerns the relationship between the scientific and the ordinary views of the world once one denies ontological and epistemological priority to either of them. Three strategies for addressing this problem are presented: according to the first, the ordinary worldview and the scientific world are categorically distinct; according to the second, the former emerges from the latter; according to the third, between them there is a relation of global supervenience. Other objections to liberal naturalism are finally presented and addressed.

The Creation of One Truth: Single-Ledger Entries for Multiple Stakeholders Using Blockchain Technology to Address the Reconciliation Problem

ABSTRACT Reconciliation of transactions between multiple parties is a time-consuming process. This paper presents a new conceptual framework that may lower costs and shorten the reconciliation time. The proposed framework recommends recording transactions on the blockchain before populating the transacting parties’ respective enterprise resource planning (ERP) systems, which will be the source of a single truth. This information flow will result in all involved parties having access to the same transaction details. Further, the paper presents a technical feasibility demonstration with two examples, where all involved parties are executing transaction details on the blockchain after meeting the governing smart contract constraints using their digital wallets. The new conceptual framework has been specifically designed to create one truth for all parties involved in a transaction that in turn may eliminate what is perceived to be unnecessary redundancy in current accounting systems.

Prescribing errors in children: what is the impact of a computerized physician order entry?

Prescribing errors represent a safety risk for hospitalized patients, especially in pediatrics. Computerized physician order entry (CPOE) might reduce prescribing errors, although its effect has not yet been thoroughly studied on pediatric general wards. This study investigated the impact of a CPOE on prescribing errors in children on general wards at the University Children’s Hospital Zurich. We performed medication reviews on a total of 1000 patients before and after the implementation of a CPOE. The CPOE included limited clinical decision support (CDS) such as drug-drug interaction check and checks for duplicates. Prescribing errors, their type according to the PCNE classification, their severity (adapted NCC MERP index), as well as the interrater reliability (Cohen’s kappa), were analyzed. Potentially harmful errors were significantly reduced from 18 errors/100 prescriptions (95% CI: 17–20) to 11 errors/100 prescriptions (95% CI: 9–12) after CPOE implementation. A large number of errors with low potential for harm (e.g., “missing information”) was reduced after the introduction of the CPOE, and consequently, the overall severity of potential harm increased post-CPOE. Despite general error rate reduction, medication reconciliation problems (PCNE error 8), such as drugs prescribed on paper as well as electronically, significantly increased after the introduction of the CPOE. The most common pediatric prescribing errors, the dosing errors (PCNE errors 3), were not altered on a statistically significant level after the introduction of the CPOE. Interrater reliability showed moderate agreement (Κ = 0.48). Conclusion: Patient safety increased by reducing the rate of prescribing errors after CPOE implementation. The reason for the observed increase in medication reconciliation problems might be the hybrid system with remaining paper prescriptions for special medication. The lacking effect on dosing errors could be explained by the fact that a web application CDS covering dosing recommendations (PEDeDose) was already in use before the implementation of the CPOE. Further investigations should focus on eliminating hybrid systems, interventions to increase the usability of the CPOE, and full integration of CDS tools such as automated dose checks into the CPOE.What is Known:• Prescribing errors, especially dosing errors, are a common safety threat for pediatric inpatients.•The introduction of a CPOE may reduce prescribing errors, though pediatric general wards are poorly studied.What is New:•To our knowledge, this is the first study on prescribing errors in pediatric general wards in Switzerland investigating the impact of a CPOE.•We found that the overall error rate was significantly reduced after the implementation of the CPOE. The severity of potential harm was higher in the post-CPOE period, which implies that low-severity errors were substantially reduced after CPOE implementation. Dosing errors were not reduced, but missing information errors and drug selection errors were reduced. On the other hand, medication reconciliation problems increased.

Algebra of Data Reconciliation

With distributed computing and mobile applications becoming ever more prevalent, synchronizing diverging replicas of the same data is a common problem. Reconciliation – bringing two replicas of the same data structure as close as possible without overriding local changes – is investigated in an algebraic model. Our approach is to consider two sequences of simple commands that describe the changes in the replicas compared to the original structure, and then determine the maximal subsequences of each that can be propagated to the other. The proposed command set is shown to be functionally complete, and an update detection algorithm is presented which produces a command sequence transforming the original data structure into the replica while traversing both simultaneously. Syntactical characterization is provided in terms of a rewriting system for semantically equivalent command sequences. Algebraic properties of sequence pairs that are applicable to the same data structure are investigated. Based on these results the reconciliation problem is shown to have a unique maximal solution. In addition, syntactical properties of the maximal solution allow for an efficient algorithm that produces it.

GenSync: A New Framework for Benchmarking and Optimizing Reconciliation of Data

In the set reconciliation problem, remote parties seek to reconcile similar sets of data according to an efficiency objective, such as minimizing communication or computation. Though investigated for many individual distributed applications, this problem still lacks a holistic treatment, and this is the aim of this work. Specifically, we design and analyze GenSync, a unified set reconciliation framework that incorporates several state-of-the-art set reconciliation protocols with an integrated testbed. We compare and analyze the various protocols and offer general guidelines for selecting a good protocol for a given application. Through extensive experiments, we demonstrate that the optimal choice of protocol is highly sensitive to several parameters, including network properties (e.g., bandwidth and latency) and computing power. Notably, none of our framework’s protocols are universally dominant under diverse conditions, and a poor protocol choice may lead to a 5x hit in performance. To demonstrate our framework, we measure the effects of protocol choice in reconciling memory pools of adjacent Bitcoin nodes.

MUL-tree pruning for consistency and optimal reconciliation - complexity and algorithms

A multi-labeled tree (or MUL-tree) is a rooted tree in which every leaf is labeled by an element from some set X, but in which more than one leaf may be labeled by the same element of X. MUL-trees have applications in many fields. In phylogenetics, they can represent the evolution of gene families, where genes are represented by the species they belong to, the non-uniqueness of leaf-labels coming from the fact that a given genome may contain many paralogous genes (genes related through duplication). In this paper, we consider two problems related to the leaf-pruning (leaf removal) of MUL-trees leading to single-labeled trees. First, given a set of MUL-trees, the MUL-tree Set Pruning for Consistency (MULSETPC) Problem asks for a pruning of each tree leading to a set of consistent trees, i.e. a collection of label-isomorphic single-labeled trees. Second, processing one tree at a time, the MUL-tree Pruning for Reconciliation (MULPR) Problem asks for a pruning that minimizes a “reconciliation” cost, i.e. an evolutionary cost of the gene family through duplication, given a known species tree. We show that MULSETPC is NP-hard and that MULPR is W[2]-hard when parameterized by the duplication cost, NP-hard when the number of occurrences of each label in a MUL-tree is bounded by two and that the optimization version of MULPR is hard to approximate within factor (1−o(1))ln⁡|X|. We then develop a polynomial-time heuristic for MULPR and show its accuracy by comparing it to a brute-force exact method on a set of gene trees from the Ensembl Genome Browser.

On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?

The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning. A solution to a MRP is an explanation for the differences between the models of the human and the planning agent (robot). Most approaches to solving MRPs assume that the robot, who needs to provide explanations, knows the human model. This assumption is not always realistic in several situations (e.g., the human might decide to update her model and the robot is unaware of the updates). In this paper, we propose a dialog-based approach for computing explanations of MRPs under the assumptions that (i) the robot does not know the human model; (ii) the human and the robot share the set of predicates of the planning domain and their exchanges are about action descriptions and fluents' values; (iii) communication between the parties is perfect; and (iv) the parties are truthful. A solution of a MRP is computed through a dialog, defined as a sequence of rounds of exchanges, between the robot and the human. In each round, the robot sends a potential explanation, called proposal, to the human who replies with her evaluation of the proposal, called response. We develop algorithms for computing proposals by the robot and responses by the human and implement these algorithms in a system that combines imperative means with answer set programming using the multi-shot feature of clingo.

VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems

Advancements in explanation generation for automated planning algorithms have moved us a step closer towards realizing the full potential of human-AI collaboration in real-world planning applications. Within this context, a framework called model reconciliation has gained a lot of traction, mostly due to its deep connection with a popular theory in human psychology, known as the theory of mind. Existing literature in this setting, however, has mostly been constrained to algorithmic contributions for generating explanations. To the best of our knowledge, there has been very little work on how to effectively convey such explanations to human users, a critical component in human-AI collaboration systems. In this paper, we set out to explore to what extent visualizations are an effective candidate for conveying explanations in a way that can be easily understood. Particularly, by drawing inspiration from work done in visualization systems for classical planning, we propose a visualization framework for visualizing explanations generated from model reconciliation algorithms. We demonstrate the efficacy of our proposed system in a comprehensive user study, where we compare our framework against a text-based baseline for two types of explanations – domain-based and problem-based explanations. Results from the user study show that users, on average, understood explanations better when they are conveyed via our visualization system compared to when they are conveyed via a text-based baseline.

Cross-social networks analysis: building me-edge centered BUNet dataset based on implicit bridge users

PurposeThe intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.Design/methodology/approachWe propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.FindingsUsing the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.Originality/valueThis paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only.
 In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems.
 Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

The effects of a pharmacist-led medication review in a nursing home: A randomized controlled trial.

Background:In this study, an intensive review of pharmaceutical care for elderly patients was conducted in a Veterans Administration nursing home in Taiwan and its effects were evaluated.Methods:One hundred participants were enrolled in this randomized controlled study with even distribution. The inclusion criteria were age 65 years or older, prescriptions for at least 5 oral medicines daily, and ≥2 chronic diseases, for the period May 2013 to October 2014. Subjects were excluded if they had previously been included in an intensive medication review conducted by a pharmacist. The primary outcomes were numbers of drugs prescribed, potential inappropriate medications, and numbers of drug-related problems. The secondary outcomes were self-reported medical usages, measurements of quality of life, results of a satisfaction survey, and health status.Results:A total of 80 cases (42 in the intervention group with medication reconciliation and 38 in the control group without medication reconciliation) completed the study. Baseline characteristics were not statistically different between the 2 groups. The overall prevalence of potential inappropriate medication was 74.3%. There were no differences between the 2 groups, with the exception of “medical problems,” which showed a significantly higher prevalence in the intervention group (P < .05). The intervention group reported greater satisfaction regarding pharmacist visits and medication compliance (P < .01). The mean number of drug-related problems was significantly lower after the intervention (P < .01).Conclusion:In this study, the intensive review of the elderly patients’ medications revealed that the only significant effect of pharmaceutical care was on “all outcomes.” A possible reason for this is the rather advanced ages of some patients who needed a considerable number of medications to treat several chronic diseases. Another reason may be the small sample size. However, participants who received the pharmacist intervention did have higher satisfaction with medication reconciliation and fewer drug-related problems.

The most ignored error in the history of mining sampling – extension variance: a case study applied to coal

ABSTRACT Apart from the sampling errors related to the sample preparation protocol in a laboratory to determine the ash and sulfur content of the coal, the delimitation/extraction errors (recovery, among other geological factors) and the “extension” error must be considered. As the collection time interval increases, the extension error also increases, so that even automatic sampling has an error that is often completely ignored. The extension error was determined via a variographic test, which was specifically designed for this study. This test was proposed to be carried out at the mining company to check the uncertainties (bias and reproducibility errors), regarding the sampling at the client. In the latter, sampling is done in piles with manual samplers. At the mining company, there are automatic collectors, but the sample interval is not considered when calculating the total error. To redeem reconciliation problems between the mining company and the client, as well as to avoid contractual fines, it was decided to compare the results of the variographic analysis carried out in the mining company with those obtained by the client. These showed considerable bias and extension errors according to the collection time intervals in the mining company, especially regarding sulfur. In shorter time intervals, 30 minutes, extension errors have resulted in a range of [0.18% to 1.52%] and [29% to 59%] for sulfur and ashes respectively (CI = 95%). For longer intervals, there would be a doubt about accepting or not the lot: maximum limits are 1.5% for sulfur and 55% for ashes. Also, as extension errors are elevated and possibly higher for “true mass flows,” which exceed 2,000 t/h, it was recommended to adapt the automatic samplers of the mining company at shorter intervals than it was used to do (2/2 hours). Then, it will improve the processes control, including blending, avoiding reconciliation problems, and subsequent contractual fines. The client was alerted to the problem of bias which occurs when the coal is sampled manually in piles. For both, it is important to know the intrinsic variance of the material/lot and the errors related to the sampling.

Foundations of explanations as model reconciliation

Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where users have domain and task models that differ from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a “model reconciliation problem” (MRP), where the AI system in effect suggests changes to the user's mental model so as to make its plan be optimal with respect to that changed user model. We will study the properties of such explanations, present algorithms for automatically computing them, discuss relevant extensions to the basic framework, and evaluate the performance of the proposed algorithms both empirically and through controlled user studies.

Reconciliation of scrum and the project management process of the ISO/IEC 29110 standard-Entry profile—an experimental evaluation through usability measures

Software process standards and models are used in large- and medium-sized organizations to reach the Iron Triangle. In contrast, small and very small entities either ignore them or cannot apply them because these standards and models are technically and economically not affordable. Consequently, agile software development practices are usually used by small and very small organizations. The ISO/IEC 29110 series of standards and guides are now available for very small organizations, but their utilization with agile practices represents an agility-rigor reconciliation problem. In this research, we report the experimental evaluation of Scrum + EPG (a reconciled agile-rigorous software Project Management process from Scrum, and the Project Management process of the ISO/IEC 29110 series-Entry profile, documented in an Electronic Process Guide). Scrum + EPG was compared to Scrum EPG (a non-modified Scrum process also documented in an Electronic Process Guide). Thirty-two international academicians and practitioners, including experts and novices on agile practices, from Latin America, North America, and Asia–Pacific regions, evaluated six metrics of usability. A within-subjects design and Wilcoxon matched-pairs signed-rank tests were applied for collecting and analyzing the experimental data. The statistical results support the claim that the Scrum + EPG was considered a high-quality conciliated agile-rigorous software Project Management process for the Entry profile. Given the scarcity of similar studies and the need for reconciling agile-rigorous software development practices, this study contributes to a plausible solution for very small organizations. Finally, further empirical research is encouraged to confirm, update, and extend the results reported in this investigation.

Understanding forecast reconciliation

A series of recent papers introduce the concept of Forecast Reconciliation, a process by which independently generated forecasts of a collection of linearly related time series are reconciled via the introduction of accounting aggregations that naturally apply to the data. Aside from its clear presentational and operational virtues, the reconciliation approach generally improves the accuracy of the combined forecasts. In this paper, we examine the mechanisms by which this improvement is generated by re-formulating the reconciliation problem as a combination of direct forecasts of each time series with additional indirect forecasts derived from the linear constraints. Our work establishes a direct link between the nascent Forecast Reconciliation literature and the extensive work on Forecast Combination. In the original hierarchical setting, our approach clarifies for the first time how unbiased forecasts for the entire collection can be generated from base forecasts made at any level of the hierarchy, and we illustrate more generally how simple robust combined forecasts can be generated in any multivariate setting subject to linear constraints. In an empirical example, we show that simple combinations of such forecasts generate significant improvements in forecast accuracy where it matters most: where noise levels are highest and the forecasting task is at its most challenging.

Benchmarking and reconciliation with time-varying cross-coefficients

In this paper, the authors propose a method to obtain explicit solutions for simultaneous benchmarking and reconciliation problems for a system of variables when the cross-restrictions use time-varying coefficients. The method is based on a hierarchical Bayesian model with a normal-gamma specification for the prior distributions. The proposed solution provides explicit (not sequential) feasible estimations, including measurements for its statistical accuracy. One interesting feature of the proposed procedure is that it allows users to include one or several performance indicators and to estimate disaggregated values for incomplete years. The method is applied to obtain Quarterly Regional Accounts for the Spanish economy.

Exact median-tree inference for unrooted reconciliation costs

BackgroundSolving median tree problems under tree reconciliation costs is a classic and well-studied approach for inferring species trees from collections of discordant gene trees. These problems are NP-hard, and therefore are, in practice, typically addressed by local search heuristics. So far, however, such heuristics lack any provable correctness or precision. Further, even for small phylogenetic studies, it has been demonstrated that local search heuristics may only provide sub-optimal solutions. Obviating such heuristic uncertainties are exact dynamic programming solutions that allow solving tree reconciliation problems for smaller phylogenetic studies. Despite these promises, such exact solutions are only suitable for credibly rooted input gene trees, which constitute only a tiny fraction of the readily available gene trees. Standard gene tree inference approaches provide only unrooted gene trees and accurately rooting such trees is often difficult, if not impossible.ResultsHere, we describe complex dynamic programming solutions that represent the first nonnaïve exact solutions for solving the tree reconciliation problems for unrooted input gene trees. Further, we show that the asymptotic runtime of the proposed solutions does not increase when compared to the most time-efficient dynamic programming solutions for rooted input trees.ConclusionsIn an experimental evaluation, we demonstrate that the described solutions for unrooted gene trees are, like the solutions for rooted input gene trees, suitable for smaller phylogenetic studies. Finally, for the first time, we study the accuracy of classic local search heuristics for unrooted tree reconciliation problems.

Linear Time Reconciliation With Bounded Transfers of Genes.

Tree reconciliation is a general framework for investigating the mutual influence between gene and species trees according to the parsimony principle, that is, to each evolutionary event a cost is assigned and the goal is to find a reconciliation of minimum total cost. The resulting optimization problem is known as the reconciliation problem. Usually, the considered events are: co-divergence, gene Duplication, horizontal gene Transfer, and gene Loss (DTL model), while in a more conservative setting, gene transfers are not allowed (DL model). The reconciliation problem requires, in the DL model, time linear in the dimension of the two trees and at least quadratic time in the DTL model. Hence, it is reasonable to argue that the introduction of horizontal gene transfers increases the complexity of the problem. Instead, we introduce horizontal gene transfers with some constraints and prove that the problem is still linear in the dimension of the trees. Namely, we allow gene transfers of length bounded by k=2, on the basis of the observation that transfers are more likely to occur between closely related species than between distantly related ones. Then we extend the same reasonings to the case in which under additional constrains. In this paper we study also another problem related to the reconciliation one, that is optimally rooting one of the two trees when it is not, and also for it we prove similar results. The relevance of this contribution lies in showing that, in the transit from the DL to the DTL model, the computational time does not increase suddenly to quadratic but remains linear in the case when gene transfers are very short (i.e., happening between very close genes).