Inconsistency Problem Research Articles

Fixing the inconsistencies of continuous changing operations in fuzzy spatiotemporal RDF graph

Fuzzy spatiotemporal data models have been used to support spatial and temporal knowledge representation and reasoning in the presence of fuzziness. In the meantime, Resource Description Framework (RDF), the most relevant data representation and exchange standard on Semantic Web, has become a trend for representing fuzzy spatiotemporal data. However, due to the dynamic changes of fuzzy spatiotemporal data, fuzzy spatiotemporal RDF graphs are more likely to violate predefined spatial and temporal constraints than the general RDF graph, which causes data inconsistencies. At present, consistency problems in RDF graphs have been widely studied, but their studies consider only discrete changing operations of fuzzy spatiotemporal data, while researches on consistencies of continuous changing operations are still open issues. In this paper, we study the types of inconsistency caused by continuous changing operations and propose the corresponding fixing rules to the problems of inconsistencies. Then, we propose the corresponding fixing approaches to checking and fixing fuzzy spatiotemporal RDF graphs according to the inconsistent states. Finally, the experimental results show that our proposed algorithms can significantly fix inconsistencies of continuous changing operations in fuzzy spatiotemporal RDF graphs.

Clinically Consistent Prostate Cancer Outcome Prediction Models with Machine Learning

<h3>Purpose/Objective(s)</h3> State-of-the-art prediction modeling relies on machine learning (ML). To provide accurate out-of-sample predictions, such as when the number of available covariates is large, these models trade-off variance by adding bias. However, these artificial biases frequently create anomalies that are clinically inconsistent, hindering their use in clinical practice. We illustrate such biases in outcome prediction ML models using the National Cancer Data Base (NCDB) and propose a solution that leverages clinical expertise. We hypothesize that integrating clinical expertise into outcome prediction ML models will correct significant problems present in ML modeling without compromising the model accuracy, enabling models to be more clinically intuitive. <h3>Materials/Methods</h3> We use data from 92,990 prostate cancer patients in the NCDB (death rate: 28%) to build a gradient boosted machine (GBM) model to predict 10-year survival post-treatment. We then investigate how the model output depends on each covariate and identify inconsistencies with clinical knowledge. To correct for these biases, we collect clinical intuition from five oncologists through a survey that asks how increasing a covariate impacts (increase/decrease/variable effect) a patient's survival. We then encode the survey results as clinically informed monotonic constraints into a LASSO model and compare the AUC with and without constraints. <h3>Results</h3> We find that while the GBM model has a relatively high out-of-sample AUC of 0.79 (compared to similar studies, with AUC between 0.65-0.83, although patient cohort, methodology, and outcomes may differ), the way some covariates are used is not clinically consistent. For example, keeping other covariates equal, the probability of survival should consistently be higher for patients with lower prostate specific antigen (PSA) levels compared to patients at higher levels. But the model predicts (with high confidence) a sporadic, non-monotonic pattern. This is because the GBM model biases its predictions for patients that comprise a small fraction of the patient cohort (i.e., patients with very low PSA values) to avoid high variance, but the artificially injected bias is towards lower survival which is not clinically consistent. Covariates named by oncologists to have a monotonic effect on the probability of survival include PSA and age. We find that by adding clinically informed constraints to a LASSO model, the problem of clinical inconsistency is addressed without compromising model performance (AUC is 0.77 with and without constraints). <h3>Conclusion</h3> We illustrate that even in a large-scale regime, state-of-the-art clinical risk prediction models can suffer from biases. Our work shows that innovative integration of clinical intuition can make models clinically consistent and verifies that this method is promising research to resolve critical challenges in ML-based decision support.

A two-stage deep counting for bacterial colonies from multi-sources

Automatic bacterial colony counting (BCC) from Petri dish images is full of challenges due to commonly exhibited overlapping, occlusion, and inconsistency problems. Modern deep learning methods require sufficient annotated data to identify colonies from a single source and yet usually perform badly on new sources, which makes it difficult to be extended. In this paper, a scalable two-stage deep learning framework is proposed to count bacterial colonies from multi-sources with both generic and specific features. In addition, a novel blending based strategy is proposed to augment the data and raise the robustness by increasing the sample complexity. We also suggest a refinement of the coarse results from existing BCC programmes could save a lot of time and effort for annotation. Four state-of-the-art methods are selected to compare with ours. Our method outperforms others in terms of mean average precision, averaged root-mean-square error and mean absolute percentage error. Hyper-parameters and other settings are also investigated in the experiments, which proved the scalability, robustness and accuracy of the proposed method.

Imbalance multi-label data learning with label specific features

A class of machine learning problem where each instance may either belong to one or more than one class simultaneously is known as Multi-label classification problem. A well-known paradigm for multi-label classification is binary relevance (BR) where multi-label classification is decomposed into binary (one-vs-rest) classification subproblems, one for each label. However, it has a number of drawbacks. First, each binary classifier can be affected by the class-imbalance issue. Second, it does not take label correlations into account. Third, it has an inconsistency problem i.e. training instances with multi-label characteristics are considered both as positive as well as negative instance simultaneously. We attempt to resolve these problems in our proposed formulation. The problem of class imbalance is addressed by applying different weights to positive and negative examples depending on the class distribution, the inconsistency issue is addressed by selecting label-specific features. We have also considered the associations that exist among the set of possible labels. We have used hinge loss function which ensures less sensitivity towards outliers and the accelerated proximal gradient method (APG) to efficiently solve the underlying optimization problem. Our proposed approach is competitive with other state-of-the-art approaches according to experimental findings on several benchmark data sets.

A Study on the Usefulness of Stochastic Simulation Algorithms for Teaching and Learning in College Physical Education Classrooms

In order to address the problem of the absolute nature of the evaluation of superiority and inferiority in the evaluation of physical education classroom in universities and the problem of inconsistency in the conclusion of multiple evaluations, we develop an “autonomous advantage evaluation method to highlight one's own advantages,” which uses a probabilistic stochastic simulation algorithm to evaluate the advantages of the evaluated objects by calculating the degree of superiority among them. The method is based on an innovative “base to top” approach, with a high degree of independence. The method was validated by means of an algorithm, and the conclusions were obtained with probabilistic information.

We cannot agree to disagree: ensuring consistency, transparency and fairness across bachelor thesis writing, supervision and evaluation

Writing, supervision and evaluation of students’ dissertations has received a fair share of attention in academic literature, with a focus on problems of marking, information processing mechanisms, worldviews and more. In most texts, problems of inconsistency, transparency and fairness are identified, leading to frustration among supervisors, assessors and students. This article shares positive experience on the creation and application of an instrument which would benefit educators and contribute practical solutions on how the issues of inconsistency, transparency and fairness within the bachelor thesis process could be tackled. Using design science research, which utilizes gained knowledge to solve problems, create change or improve existing solutions, we developed the bachelor thesis writing and assessing instrument. Following three iterations of the instrument among writers, supervisors and assessors of bachelor theses at the Estonian Business School, we conclude that the use of the instrument has greatly improved consistency, transparency and fairness of the thesis-process, thus benefitting all three parties in particular and the university in general.

Consistency of the boundary value problem of an elastic body involving surface tension in small deformations

Surface tension has a significant influence on the mechanical response of solids at small scales to external loadings. Conventional small deformation theories remain effective in capturing such an influence provided the corresponding boundary condition incorporates some measure of the deformed configuration of the solid’s surface in evaluating the surface tension–induced traction acting on the solid. Since the governing equations arising from conventional small deformation theories for the bulk of the solid are defined strictly in the undeformed configuration, they may be sometimes incompatible with the surface tension–related boundary condition incorporating certain measure of the deformed configuration, leading to an inconsistent boundary value problem. To clarify how to pose a self-consistent boundary value problem involving surface tension, we consider the plane deformation of a perforated medium with its internal boundary endowed with surface tension and surface elasticity and its external boundary subjected to a mechanical loading. We first review three kinds of related boundary conditions on the internal boundary of the medium: the Gurtin–Murdoch-type boundary condition and two modified boundary conditions based on the change-in-curvature formula. We then derive the resultant force and moment of the traction caused by surface tension and surface elasticity for each of the boundary conditions. We show that for the Gurtin–Murdoch-type boundary condition, the resultant force vanishes unconditionally but the resultant moment depends on the rigid-body rotation of the medium, while for the two modified boundary conditions, both the resultant force and resultant moment (about a certain point) rely on local deformation gradients and the curvature of the internal boundary of the medium. Consequently, for the Gurtin–Murdoch-type boundary condition, one may easily establish a self-consistent boundary value problem by specifying an appropriate mechanical loading, while for the two modified boundary conditions, the boundary value problem is only occasionally self-consistent for some special and compatible combinations of the geometry of the internal boundary and the mechanical loading. Nevertheless, if the medium becomes infinitely large with its external boundary extremely distant from the internal boundary, then the boundary value problem corresponding to either of the three boundary conditions is always self-consistent for an arbitrary configuration of the internal boundary and an arbitrary far-field loading.

A novel consensus reaching method for MCGDM in social network with intuitionistic grey linguistic numbers

PurposeThe existing consensus reaching mechanisms ignore the influence of social triangle structure on the decision-makers’ (DMs') weights, and the consensus reaching process (CRP) fails to fully reflect the DMs' subjectivity and can be time consuming and costly. To solve these issues, a novel CRP for multi-criteria group decision-making (MCGDM) problems with intuitionistic grey linguistic numbers (IGLNs) is proposed in this paper.Design/methodology/approachFirst, a weight calculation method is proposed by analysing the triangle structure of DMs' social network and scale of adjacent nodes. Then, a consensus degree index based on three-level polygon area is defined and applied to identify the inconsistent DMs. Finally, the feedback mechanism based on particle swarm optimisation (PSO) algorithm under grey linguistic environment is developed, where subjective trust relationships in social network is utilised to determine the adjustment coefficient.FindingsThe advantages of the proposed method are highlighted by two practical applications of the evaluation of tunnel construction method and the selection of a hotel for the centralised isolation. Comparision analysis and numerical simulation are performed to reveal the effectiveness and applicability of the method.Practical implicationsThe proposed model can not only reflect the effect of triangle structure in social network on DMs' weights, but also reduce the time and cost of decision-making.Originality/valueThe main contribution of this paper is to propose a new MCGDM model based on intuitionistic grey linguistic numbers, which can handle the problem of inconsistency of information more effectively.

Investigating the effect of classroom-based feedback on speaking assessment: a multifaceted Rasch analysis

Due to subjectivity in oral assessment, much concentration has been put on obtaining a satisfactory measure of consistency among raters. However, the process for obtaining more consistency might not result in valid decisions. One matter that is at the core of both reliability and validity in oral assessment is rater training. Recently, multifaceted Rasch measurement (MFRM) has been adopted to address the problem of rater bias and inconsistency in scoring; however, no research has incorporated the facets of test takers’ ability, raters’ severity, task difficulty, group expertise, scale criterion category, and test version together in a piece of research along with their two-sided impacts. Moreover, little research has investigated how long rater training effects last. Consequently, this study explored the influence of the training program and feedback by having 20 raters score the oral production produced by 300 test-takers in three phases. The results indicated that training can lead to more degrees of interrater reliability and diminished measures of severity/leniency, and biasedness. However, it will not lead the raters into total unanimity, except for making them more self-consistent. Even though rater training might result in higher internal consistency among raters, it cannot simply eradicate individual differences related to their characteristics. That is, experienced raters, due to their idiosyncratic characteristics, did not benefit as much as inexperienced ones. This study also showed that the outcome of training might not endure in long term after training; thus, it requires ongoing training throughout the rating period letting raters regain consistency.

Specific emitter identification based on the multi‐discrepancy deep adaptation network

Specific emitter identification task often encounters the problem of channel inconsistency between the collected signal and the signal to be recognised, that is, different channel environments would cause inconsistency in the sample distribution, which would result in a significant decrease in recognition accuracy; the same problem is encountered in transfer learning. The authors focus on an important category of methods in transfer learning called domain adaptation to solve the above problems. Motivated by the idea of the Fisher discriminant and metric learning, a novel domain adaptation method called the multi-discrepancy deep adaptation network is proposed. In contrast to the current approach of reducing the discrepancies between the source and target domain distributions without considering category information, the basic idea of the proposed method is to increase interclass discrepancies between the source and target domains while decreasing interclass discrepancies. To adjust the weights of interclass and interclass discrepancies, a dynamic balancing factor that adaptively adjusts the weights between the interclass variance and interclass variance according to the latest trends of both variances is introduced, which is effective in improving performance. Dynamic balancing and learning weight factors, which are used to adjust the training weights of interclass and interclass discrepancies during the training process are introduced. The authors applied the network to a specific emitter identification problem under different channels on the ORACLE radio frequency Fingerprinting Data set and demonstrated a maximum domain adaptability increase of 15.4%, which outperformed existing domain adaptation methods.

Geographical and population diversity of African honeybees, Apismellifera L. (Hymenoptera: Apidae): A review

This review was prepared to summarize the information on geographic and genetic diversity of Apismellifera L. of African honeybees and the progresses made through time and applications of various advanced tools in their taxonomy at subspecies level. African honeybees have developed distinct morphology, behavior and physiology as they evolved in tropical favorable climate. The variations among the African honeybee populations have helped to produce geographic subspecies based on the morphological characteristics. However, the introduction of molecular taxonomic tools has come up with discrepancies in the number of subspecies and lineages, and subspecies composition of evolutionary lineages in Africa and other parts of the world. It has also brought up controversies in the evolutionary history of A. mellifera reporting different location as the center of origin of the species at different times: Asia, Africa and Europe. It is not only the tools that are used, but also the genetic variations within subspecies, within colonies in an apiary site and within individuals of the same colony temporally are the factors for continually appearing discrepancies. Natural hybridization between populations of adjacent subspecies along the geographic transition zones and introgression of genes through introduction of colonies that is mainly driven by human demand for desirable traits of honeybees have been among the challenges for discrimination of honeybee subspecies. Therefore, in future genetic diversity and taxonomic investigations, considering these natural variations at subspecies level and developing standardized and integrated procedures from sampling to analysis methods would help to produce generally recognized data base that is critically important to resolve the problem of inconsistencies in taxonomy and nomenclature of honeybee subspecies of Arica and elsewhere.&#x0D;

BRefine: Achieving High-Quality Instance Segmentation.

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.

An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data.

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.

Commitment versus flexibility and sticky prices: Evidence from life insurance

Life insurance premiums display significant rigidity in the data, on average adjusting once every 3 years by more than 10%. This contrasts with the underlying marginal cost which exhibits considerable volatility due to the movements in interest and mortality rates. We build a dynamic model where policyholders are held-up by long-term insurance contracts, resulting in a time inconsistency problem for the insurer. The optimal contract balances commitment and flexibility and takes the form of a simple cutoff rule: premiums are rigid for cost realizations smaller than the threshold, while adjustments must be large and are only possible when cost realizations exceed it. We use a calibrated version of the model to show that it matches the data and captures several aspects of premium rigidity in the cross-section and over time.

S2Net: Shadow Mask-Based Semantic-Aware Network for Single-Image Shadow Removal

Existing shadow removal methods often struggle with two problems: color inconsistencies in shadow areas and artifacts along shadow boundaries. To address these two problems, we propose a novel shadow mask-based semantic-aware network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) that uses shadow masks as guidance for shadow removal. The color inconsistency problem is solved in two steps. First, we use a series of semantic-guided dilated residual (SDR) blocks to transfer statistical information from non-shadow areas to shadow areas. The shadow mask-based semantic transformation (SST) operation in SDR enables the network to remove shadows while keeping non-shadow areas intact. Then, we design a refinement block by incorporating semantic knowledge of shadow masks and applying the learned modulated convolution kernels to get traceless and consistent output. To remove artifacts along shadow boundaries, we propose a newly designed boundary loss. The boundary loss encourages spatial coherence around shadow boundaries. By including the boundary loss as part of the loss function, a significant portion of artifacts along shadow boundaries can be removed. Extensive experiments on the ISTD, ISTD+, SRD and SBU datasets show our S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net outperforms existing shadow removal methods.

The role of working memory updating, inhibition, fluid intelligence, and reading comprehension in explaining differences between consistent and inconsistent arithmetic word-problem-solving performance

Children’s performance in arithmetic word problems (AWPs) predicts their academic success and their future employment and earnings in adulthood. Understanding the nature and difficulties of interpreting and solving AWPs is important for theoretical, educational, and social reasons. We investigated the relation between primary school children’s performance in different types of AWPs and their basic cognitive abilities (reading comprehension, fluid intelligence, inhibition, and updating processes). The study involved 182 fourth- and fifth-graders. Participants were administered an AWP-solving task and other tasks assessing fluid intelligence, reading comprehension, inhibition, and updating. The AWP-solving task included comparison problems incorporating either the adverb more than or the adverb less than, which demand consistent or inconsistent operations of addition or subtraction. The results showed that consistent problems were easier than inconsistent problems. Efficiency in solving inconsistent problems is related to inhibition and updating. Moreover, our results seem to indicate that the consistency effect is related to updating processes’ efficiency. Path analyses showed that reading comprehension was the most important predictor of AWP-solving accuracy. Moreover, both executive functions—updating and inhibition—had a distinct and significant effect on AWP accuracy. Fluid intelligence had both direct and indirect effects, mediated by reading comprehension, on the overall measure of AWP performance. These domain-general factors are important factors in explaining children’s performance in solving consistent and inconsistent AWPs.

Organizational Culture as a Development Resource in the Conditions of Changing Institutional Functions of Libraries

In the context of technological and socio-cultural transformations, libraries are in search for landmarks and new formats of activity. The vector of transformation of library functions is from the preservation and transmission of information to agentive: structuring communities, managing information, communicative functions. Libraries are becoming conductors of social innovation. The article considers organizational culture as a catalyst for the development of libraries in the future. The authors pose the problem of inconsistency of organizational culture with new demands and needs, as well as justify the need for the formation of an innovative type of culture. The paper presents the results of quantitative and qualitative research in the Municipal Information Library System (MILS) of Tomsk in 2021. Questionnaires and interviews were conducted with employees of three libraries of Tomsk MILS, one of which took part in the Federal project “Cultural Environment” of the National project “Culture” on the creation of model municipal libraries. The authors study organizational culture at the formal and informal levels. The analysis showed that each of the libraries demonstrated insufficient level of compliance with the context of social transformations. Barriers of organizational culture in public libraries are conservative mission, management attitudes to support performing competencies, features of clan culture (orientation to isolation, lack of openness for cooperation on the part of staff both within the library network and with external communities, underestimation of the role of partnership), technological focus of activities, etc. Reserves for the development of organizational culture are the willingness of staff to take initiatives, focus on support and building positive relationships in the team. As an opportunity to manage organizational culture, the authors note that transformations should begin with a change of emphasis in the libraryʼs mission based on a new spatial approach.

Discriminative error prediction network for semi-supervised colon gland segmentation.

Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation directly affects the accuracy on the test set and the subsequent self-training with the error-corrected pseudo labels. In this paper, we propose a novel label rectification method based on error correction, namely ECLR, which can be directly added after the fully-supervised segmentation framework. Moreover, it can be used to guide the semi-supervised learning (SSL) process, constituting an error correction guided SSL framework, called ECGSSL. Specifically, we analyze the types and causes of segmentation error, and divide it into intra-class error and inter-class error caused by intra-class inconsistency and inter-class similarity problems in segmentation, respectively. Further, we propose a collaborative multi-task discriminative error prediction network (DEP-Net) to highlight two error types. For better training of DEP-Net, we propose specific mask degradation methods representing typical segmentation errors. Under the fully-supervised regime, the pre-trained DEP-Net is used to directly rectify the initial segmentation results of the test set. While, under the semi-supervised regime, a dual error correction method is proposed for unlabeled data to obtain more reliable network re-training. Our method is easy to apply to different segmentation models. Extensive experiments on gland segmentation verify that ECLR yields substantial improvements based on initial segmentation predictions. ECGSSL shows consistent improvements over a supervised baseline learned only from labeled data and achieves competitive performance compared with other popular semi-supervised methods.

Reliability evaluation of balanced systems with common bus performance sharing considering balance degree base on battery storage systems

To solve the problem of inconsistency of batteries, many people have studied the the battery system with the automatic balancing mechanism which can be regarded as a balanced system. In this paper, a reliability evaluation model for balanced systems with common bus performance sharing considering the degree of balance is proposed. The balance degree of the system is represented by the difference in performance between the components. If the performance difference between components exceeds the balance degree threshold, the system is unbalanced and performance sharing is required to redistribute performance through a common bus. This paper also takes into account transfer capacity limits. When the output performance exceeds the maximum transmission capacity, the performance transmission is performed according to the maximum limit to achieve the best balance. The criterion for system failure is that the balance is still out of balance after performance sharing or the total system performance is lower than the specified requirements. Firstly, by solving the Markov equation, the steady-state probability of component performance levels under the long-term operation of the system is obtained, and then the reliability of the system is evaluated by UGF (Universal generating function) method. Finally, a numerical example is carried out to illustrate the effectiveness of the method.

Thyroid nodule segmentation and classification in ultrasound images through intra- and inter-task consistent learning.

Thyroid nodule segmentation and classification in ultrasound images are two essential but challenging tasks for computer-aided diagnosis of thyroid nodules. Since these two tasks are inherently related to each other and sharing some common features, solving them jointly with multi-task leaning is a promising direction. However, both previous studies and our experimental results confirm the problem of inconsistent predictions among these related tasks. In this paper, we summarize two types of task inconsistency according to the relationship among different tasks: intra-task inconsistency between homogeneous tasks (e.g., both tasks are pixel-wise segmentation tasks); and inter-task inconsistency between heterogeneous tasks (e.g., pixel-wise segmentation task and categorical classification task). To address the task inconsistency problems, we propose intra- and inter-task consistent learning on top of the designed multi-stage and multi-task learning network to enforce the network learn consistent predictions for all the tasks during network training. Our experimental results based on a large clinical thyroid ultrasound image dataset indicate that the proposed intra- and inter-task consistent learning can effectively eliminate both types of task inconsistency and thus improve the performance of all tasks for thyroid nodule segmentation and classification.