Different Kinds Of Errors Research Articles

Multiple Types of Developmental Dyslexias in a Shallow Orthography: Principles for Diagnostic Screening in Italian.

A new dyslexia screening test for Italian, Tiltan-IT, is presented. The test was developed based on an integrated dual-route model of reading, which describes in detail specific mechanisms underpinning early visual processes as well as the lexical and the sublexical routes. The principle according to which the test was developed is that each dyslexia type is manifested in different kinds of errors and in different kinds of stimuli, and we therefore included stimuli sensitive to each dyslexia type in the test. Tiltan-IT is a reading aloud test that includes word, nonword, and word pair lists. The test was administered to 618 Italian-speaking children (2nd-8th grade). Each error produced by the children was classified through the coding scheme developed to detect the different types of dyslexias described by the reading model. The Tiltan-IT was able to identify 110 children with dyslexia. The identified dyslexia types included letter position dyslexia, attentional dyslexia, letter identity dyslexia, surface dyslexia, vowel dyslexia, consonant conversion dyslexia, multi-letter phonological dyslexia, voicing dyslexia. The results confirm that the selection of items in the Tiltan-IT enabled the detection of the wide variety of dyslexias in Italian, some of them for the first time, adding evidence for the cross-linguistic validity of multiple types of developmental dyslexias and for the dual-route model of reading.

Polynomial Equivalence of Complexity Geometries

This paper proves the polynomial equivalence of a broad class of definitions of quantum computational complexity. We study right-invariant metrics on the unitary group—often called `complexity geometries' following the definition of quantum complexity proposed by Nielsen—and delineate the equivalence class of metrics that have the same computational power as quantum circuits. Within this universality class, any unitary that can be reached in one metric can be approximated in any other metric in the class with a slowdown that is at-worst polynomial in the length and number of qubits and inverse-polynomial in the permitted error. We describe the equivalence classes for two different kinds of error we might tolerate: Killing-distance error, and operator-norm error. All metrics in both equivalence classes are shown to have exponential diameter; all metrics in the operator-norm equivalence class are also shown to give an alternative definition of the quantum complexity class BQP. My results extend those of Nielsen et al., who in 2006 proved that one particular metric is polynomially equivalent to quantum circuits. The Nielsen et al. metric is incredibly highly curved. I show that the greatly enlarged equivalence class established in this paper also includes metrics that have modest curvature. I argue that the modest curvature makes these metrics more amenable to the tools of differential geometry, and therefore makes them more promising starting points for Nielsen's program of using differential geometry to prove complexity lowerbounds. In a previous paper my collaborators and I—inspired by the UV/IR decoupling that happens in the phenomenon of renormalization—conjectured that high- dimensional metrics that look very different at short scales will often nevertheless give rise at long scales to the same emergent effective geometry. The results of this paper provide evidence for those conjectures, since many complexity metrics that have radically different penalty factors and therefore radically different short- distance properties are shown to belong to the same long-distance equivalence class.

Effective Denoising Algorithms for Converting Indoor Blueprints Using a 3D Laser Scanner

This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity.

Grammatical versus Spelling Error Correction: An Investigation into the Responsiveness of Transformer-Based Language Models Using BART and MarianMT

Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through the conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR and speech recognition are utilised to transform the images and speech signals into text content. All these variety of mechanisms of text generation also introduce errors into the captured text. This project aims at analysing different kinds of errors that occur in text documents. The work employs two of the advanced deep neural network-based language models, namely, BART and MarianMT, to rectify the anomalies present in the text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both models can bring down the erroneous sentences by 20+%, BART can handle spelling errors far better (24.6%) than grammatical errors (8.8%).

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

Benchmarking and Analysis of Quantum Error Correction Codes

Quantum Computing is a computing framework that takes advantage of unique quantum mechanical properties (such as superposition and entanglement) to perform calculations and implement algorithms that could offer exponential speed-ups over classical computing. However, in physical implementations of such quantum computers, qubits – the fundamental components of these systems – can accumulate errors that must be accounted for. In order to mitigate these errors, various quantum error correction (QEC) codes have been developed, including the repetition code and surface codes. In this experiment I implement and evaluate three types of QEC codes on the Qiskit simulator to compare their efficacy and applicability in correcting for different kinds of errors. I hypothesize that surface codes, with their more effective design and range of correction methods, should perform the best with much lower error thresholds and resultant logical error rates. The results support the hypothesis and suggest that surface codes are a viable method of implementing scalable error correction in quantum computers.

Genre–Linguistic Analysis of Errata Texts in Arabic

News accuracy has been a significant issue for media organizations. However, Arabic errata texts have been neglected in academic research. The purpose of the present study is to identify the generic structural components of errata published in three different types of Arabic texts. We attempt to analyse the generic structure of these texts and determine the linguistic signals used to indicate these components. The paper also sheds light on the different kinds of errors found in Arabic texts and the functions they communicate to readers. For this, we collected and analysed 120 errata from Arabic daily newspapers, academic journals, and popular magazines. For our data analysis, we drew on the genre analysis framework pioneered by Swales (1990) and further adapted by other researchers such as Al-Ali (2005). We additionally referred to Bugeja and Peterson (2007) for classifying the types of errors. The study results show that the analysed Arabic errata texts followed a generic structure comprising eight component moves. Further, objective errors were found to be more frequent than subjective errors in Arabic newspapers and academic journals. We hope that similar future studies further the current understanding of genre identification and construction, especially in Arabic errata texts.

AN ERROR ANALYSIS IN WRITING THESIS ABSTRACT OF ENGLISH DEPARTMENT

The goal of this study was to identify the different kinds of errors that were made by English Education Department students at KH. A. Wahab Hasbullah University of Jombang when they were writing their thesis abstracts. It also sought to understand why these errors could happen. This study concentrated on the thesis abstracts written by English education department undergrads. This study employed a descriptive qualitative methodology. Thirty undergraduate thesis abstract texts released in 2022 served as the research's source of data. Collection, identification, description, explanation, and evaluation of errors were the steps in the error analysis process. The method of data analysis was developed based on Corder's work, which was cited in (Nurdin & Sri Hartati, 2019). According to the results, undergrad students' mistakes included adding words, using the wrong terms, capitalizing, omitting words, spelling, punctuating, using the wrong tenses, and misusing word forms. These errors can be made in a variety of ways depending on their kind, and they can also result from carelessness, inadequate grasp of English grammar and vocabulary, and inter- and intra-lingual interference.

Descartes on the source of error: the Fourth Meditation and the Correspondence with Elisabeth

ABSTRACT In the Fourth Meditation, Descartes famously treats the indifference of the will (roughly, ambivalence of reasons) as the source of error, which many read as oddly suggesting that the will judges arbitrarily. In his letter to Elisabeth dated 1st September 1645, however, he expressly takes passions to be the source of error, saying that passions move the will to judge erroneously by misrepresenting the value of objects. Although these two accounts focus on different kinds of error – theoretical and practical error, respectively – I argue that Descartes is best understood as extending the second account also to the source of theoretical error. On my reading, the first account does not imply that erroneous judgements are simply arbitrary, but it leaves out an explanation why we judge at the time we do, when we could (and should) continue to inquire insofar as we have not yet gathered sufficient evidence. The second account fills in this lacuna by giving an explanation in terms of passions. I further argue that the schematic nature of the first account is due partly to the structure of the Meditations, but mainly to the fact that Descartes has not yet systematically examined the nature of passions there.

There Is no Theory-Free Measure of “Swaps” in Visual Working Memory Experiments

Visual working memory is highly limited, and its capacity is tied to many indices of cognitive function. For this reason, there is much interest in understanding its architecture and the sources of its limited capacity. As part of this research effort, researchers often attempt to decompose visual working memory errors into different kinds of errors, with different origins. One of the most common kinds of memory error is referred to as a “swap,” where people report a value that closely resembles an item that was not probed (e.g., an incorrect, non-target item). This is typically assumed to reflect confusions, like location binding errors, which result in the wrong item being reported. Capturing swap rates reliably and validly is of great importance because it permits researchers to accurately decompose different sources of memory errors and elucidate the processes that give rise to them. Here, we ask whether different visual working memory models yield robust and consistent estimates of swap rates. This is a major gap in the literature because in both empirical and modeling work, researchers measure swaps without motivating their choice of swap model. Therefore, we use extensive parameter recovery simulations with three mainstream swap models to demonstrate how the choice of measurement model can result in very large differences in estimated swap rates. We find that these choices can have major implications for how swap rates are estimated to change across conditions. In particular, each of the three models we consider can lead to differential quantitative and qualitative interpretations of the data. Our work serves as a cautionary note to researchers as well as a guide for model-based measurement of visual working memory processes.

Disentangling task-selection failures from task-execution failures in task switching: an assessment of different paradigms

Differentiating errors on the basis of the distinct cognitive mechanisms that may have generated them has provided neuropsychologists with useful diagnostic tools. For example, perseverative errors arising from the inability of the patient to set a new criterion for responding are considered one of the hallmarks of cognitive inflexibility. Similarly, in the task-switching paradigm it is possible to distinguish between task-confusion errors, produced by a failure in task selection, and response-confusion errors, arising when the correct task is selected, but the wrong response is given. Nonetheless, only a few studies so far have exploited the existence of different kinds of errors in multitasking situations to inform theories of cognitive flexibility. In the present study, we set out to use a variety of methodologies employed so far in the literature for disentangling errors due to task-selection failure from errors due to task-execution failure. In three experiments, we assessed the capacity of each method to produce error categories that can be mapped as clearly as possible to the cognitive mechanism(s) underlying them using multinomial processing tree modelling. Subsequently, the distinction between task- and response-confusion errors was used to test their differential impact on inhibitory mechanisms in task switching as measured by N-2 repetition costs. Our results are encouraging regarding the possibility of correctly detecting response- and task-selection failures, thus allowing us to assess their differential impact on N-2 repetition costs.

Novel computational mathematical algorithms for structural optimization using graph-theoretical methods

PurposeThe purpose is to reduce round-off errors in numerical simulations. In the numerical simulation, different kinds of errors may be created during analysis. Round-off error is one of the sources of errors. In numerical analysis, sometimes handling numerical errors is challenging. However, by applying appropriate algorithms, these errors are manageable and can be reduced. In this study, five novel topological algorithms were proposed in setting up a structural flexibility matrix, and five different examples were used in applying the proposed algorithms. In doing so round-off errors were reduced remarkably.Design/methodology/approachFive new algorithms were proposed in order to optimize the conditioning of structural matrices. Along with decreasing the size and duration of analyses, minimizing analytical errors is a critical factor in the optimal computer analysis of skeletal structures. Appropriate matrices with a greater number of zeros (sparse), a well structure and a well condition are advantageous for this objective. As a result, a problem of optimization with various goals will be addressed. This study seeks to minimize analytical errors such as rounding errors in skeletal structural flexibility matrixes via the use of more consistent and appropriate mathematical methods. These errors become more pronounced in particular designs with ill-suited flexibility matrixes; structures with varying stiffness are a frequent example of this. Due to the usage of weak elements, the flexibility matrix has a large number of non-diagonal terms, resulting in analytical errors. In numerical analysis, the ill-condition of a matrix may be resolved by moving or substituting rows; this study examined the definition and execution of these modifications prior to creating the flexibility matrix. Simple topological and algebraic features have been mostly utilized in this study to find fundamental cycle bases with particular characteristics. In conclusion, appropriately conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Findings(1) Five new algorithms were proposed in order to optimize the conditioning of structural flexibility matrices. (2) A JAVA programming language was written for all five algorithms and a friendly GUI software tool is developed to visualize sub-optimal cycle bases. (3) Topological and algebraic features of the structures were utilized in this study.Research limitations/implicationsThis is a multi-objective optimization problem which means that sparsity and well conditioning of a matrix cannot be optimized simultaneously. In conclusion, well-conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Practical implicationsEngineers always finding mathematical modeling of real-world problems and make them as simple as possible. In doing so, lots of errors will be created and these errors could cause the mathematical models useless. Applying decent algorithms could make the mathematical model as precise as possible.Social implicationsErrors in numerical simulations should reduce due to the fact that they are toxic for real-world applications and problems.Originality/valueThis is an original research. This paper proposes five novel topological mathematical algorithms in order to optimize the structural flexibility matrix.

SoundexGR: An algorithm for phonetic matching for the Greek language

AbstractText usually suffers from typos which can negatively affect various Information Retrieval and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language, this is not the case for other languages. For the Greek language, most of the existing phonetic algorithms provide rather insufficient support. For this reason, in this paper, we introduce an algorithm for phonetic matching designed for the Greek language: we start from the original Soundex and we redesign and extend it for accommodating the Greek language’s phonetic rules, ending up to a family of algorithms, that we call ${\tt Soundex}_{GR}$ . Then, we report various experimental results showcasing how the algorithm behaves in different scenarios, and we provide comparative results for various parameters of the algorithm for revealing the trade-off between precision and recall in datasets with different kinds of errors. We also provide comparative results with matching using stemming, full phonemic transcription, and edit distance, that demonstrate that ${\tt Soundex}_{GR}$ performs better (indicatively, it achieves F-Score over 95% in collections of similar-sounded words). The simplicity, efficiency, and effectiveness of the proposed algorithm make it applicable and adaptable to a wide range of tasks.

Presidential Address 2020 Suboptimal Climate Policy

Abstract There is a scientific consensus that human activities, in the form of emissions of carbon dioxide into the atmosphere, cause global warming. These emissions mostly occur in the marketplace, that is, they are undertaken by private individuals and firms. Governments seeking to curb emissions thus need to design policies that influence market behavior in the direction of their goals. Economists refer to Pigou taxation as “the” solution here, since the case of global warming can be seen as a pure (negative) externality. We agree. However, given the reluctance of policymakers to agree with us, there is an urgent need to consider, and compare, suboptimal policies. In this paper, we look at one such instance: setting a global tax on carbon at the wrong level. How costly are different errors? Since there is much uncertainty about how much climate change there will be, and how damaging it is when it occurs, ex-post errors will most likely be made. We compare different kinds of errors qualitatively and quantitatively and find that policy errors based on over-pessimistic views on climate change are much less costly than those made based on over-optimism. This finding is an inherent feature of standard integrated assessment models, even though these models do not feature tipping points or strong linearities.

A priori error estimates for finite element approximations of regularized level set flows in higher norms

This paper proves error estimates for H2 conforming finite elements for elliptic equations which model the flow of surfaces by different powers of the mean curvature (this includes mean curvature flow). The scheme is based on a known regularization procedure and produces different kinds of errors, a regularization error, a finite element discretization error for the regularized problems, and a full error. While in the literature and own previous work different aspects of the aforementioned error types are treated, here, we solely and for the first time focus on the finite element discretization error in higher norms for the regularized equation and additionally analyze also the dependencies from the regularization parameter.

Malaysian Tertiary ESL Students' Writing Errors and Their Implications on English Language Teaching

Abstract: Errors, either in writing or speaking, are common in second language production. Researchers have long been investigating English as Second Language (ESL) learners' errors because they may produce different kinds of errors than native speakers. The purpose of this study was to identify errors made by adult ESL learners and to examine the L1 interference in writing errors. Writing samples were collected from 30 students enrolled in the academic writing class in Universiti Putra Malaysia, and the errors were identified, recorded, and analyzed to determine whether they were intralingual or interlingual ones. The study results showed that most of the errors were in subject-verb agreement and verb tenses, which could be caused by first language (L1) influence. The findings of this study would shed light on the types of errors these ESL learners make and their weaknesses in using the English language. Besides, the findings could support instructors, curricula creators, and textbook writers to create and offer materials that could help learners enhance their command of the English language.
 
 Keywords: Error Analysis, Intralingual, Interlingual, writing problems, writing errors

Determining optical mapping errors by simulations.

Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system. The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework's main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis. Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/. Supplementary data are available at Bioinformatics online.

Error analysis and optimization for Risley-prism imaging distortion correction

The reverse ray-tracing method has become a well-known technique to correct the dynamic imaging distortion caused by the Risley-prism imaging system due to its precision and computational efficiency. However, the reverse ray-tracing method is sensitive to equipment error, which seriously degrades the quality of distortion correction when using a prism with a large wedge angle or a camera with a large field of view. We optimize the distortion correction method utilizing reverse ray tracing. In addition, we propose a distortion correction model with error parameters to investigate the influence of prism orientation error, prism tilt error, prism parameter error, and model simplification errors on the correction accuracy. The work on the optimized model clearly indicates the obvious image distortion introduced by different kinds of errors, including model error and systematic error. Furthermore, we propose an error parameter identification method to eliminate the negative results of error on the image correction. The simulation results show that the boresight pointing error and distortion correction error are reduced to about 1% of the initial value after 10 iterations, thus achieving high-precision imaging distortion correction and providing better data support for other subsequent applications.

Models, Markets, and the Forecasting of Elections

We examine probabilistic forecasts for battleground states in the 2020 US presidential election, using daily data from two sources over seven months: a model published by the Economist, and prices from the PredictIt exchange. We find systematic differences in accuracy over time, with markets performing better several months before the election, and the model performing better as the election approached. A simple average of the two forecasts performs better than either one of them overall, even though no average can outperform both component forecasts for any given state-date pair. This effect arises because the model and the market make different kinds of errors in different states: the model was confidently wrong in some cases, while the market was excessively uncertain in others. We conclude that there is value in using hybrid forecasting methods, and propose a market design that incorporates model forecasts via a trading bot to generate synthetic predictions. We also propose and conduct a profitability test that can be used as a novel criterion for the evaluation of forecasting performance.

OCR error correction using correction patterns and self-organizing migrating algorithm

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition.