Kinds Of Errors Research Articles

Code review in digital humanities

Abstract Software and computational methods offer tremendous possibilities for digital humanities research, both accelerating existing work and opening up entirely new questions. However, software also has the potential to introduce new kinds of errors into the research workflow. How do we know that the software developed for a digital humanities project is error free and does what we think it does? Code review is a widespread technique to improve software quality and reduce the number of flaws, where a programmer other than the author reviews and comments on the source code. However, given that many digital humanities developers work in developer teams of one, code review is often not possible. In this article, we share progress and insights from an effort to establish a community code review process for digital humanities, and provide background to help understand the need and potential impacts of this work.

An Investigation into the Feedback of Automated Writing Evaluation System on EFL Learners’ Writing

Computer technology has developed at a rapid pace in recent decades. The combination of artificial intelligence and writing teaching has become an important development trend. For better utilization of Automated Writing Evaluation System in teaching, the quality of its feedback on various types of errors in compositions is one of the issues worth exploring. In order to investigate this issue, this study compares the diagnosis of EFL learners' compositions by Automated Writing Evaluation System with the actual errors in the compositions, and calculates the Precision and Recall of the iWrite system's feedback. Unlike previous studies, this study synthesizes macro discourse elements such as propositional logic, across-sentence logic, and discourse structure, and categorizes the actual errors in learners' compositions into seven kinds. The results show that the iWrite system performs excellent in detecting three kinds of errors, which are lexical errors, grammatical errors, and other detail-type errors, and is moderately effective in detecting pragmatic errors, while it fails to provide feedback on propositional logic, discourse cohesion and coherence, and paragraph structure, which are the high-level error types, indicating that iWrite system is completely unable to capture errors in terms of macro-discourse elements. These findings provide a strong basis for improving Automated Writing Evaluation System, and at the same time provide English teachers with a reference for writing teaching, which helps to better serve the field of foreign language education.

Antenna Integration for Millimeter-Wave RF Sensing and Millimeter-Wave Communication Mountable on a Platform.

An array antenna for millimeter-wave communication and an array antenna for millimeter-wave sensing are designed and put together into one structure. Because millimeter-wave signals become weaker fast with the increasing distance and any kind of error in the required functions of the antenna has to be minimized, pointing error from the target direction should be prevented. The device is a millimeter-wave sensing antenna with high directivity to check the straight link between the TX and RX sides of wireless communication. A 24 GHz 8-by-16 array antenna which generates stronger signals for sensing resolves the drawback of a 28 GHz 1-by-4 array antenna that is commonly seen in 5G wireless terminals. The sensing and communication antennas are integrated as a planar structure mountable on platforms, which is investigated with regard to forming wireless links over a distance of several meters with an input power of less than 0 dBm. Additionally, in the event of a reflecting surface disturbing the straight path and worsening the pointing error in RF signal transfer, the dual-capability of the combination is presented on the basis of intuitive electromagnetic experiments.

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.

Accuracy, thoroughness, and quality of outpatient primary care documentation in the U.S. Department of Veterans Affairs

BackgroundElectronic health records (EHRs) can accelerate documentation and may enhance details of notes, or complicate documentation and introduce errors. Comprehensive assessment of documentation quality requires comparing documentation to what transpires during the clinical encounter itself. We assessed outpatient primary care notes and corresponding recorded encounters to determine accuracy, thoroughness, and several additional key measures of documentation quality.MethodsPatients and primary care clinicians across five midwestern primary care clinics of the US Department of Veterans Affairs were recruited into a prospective observational study. Clinical encounters were video-recorded and transcribed verbatim. Using the Physician Documentation Quality Instrument (PDQI-9) added to other measures, reviewers scored quality of the documentation by comparing transcripts to corresponding encounter notes. PDQI-9 items were scored from 1 to 5, with higher scores indicating higher quality.ResultsEncounters (N = 49) among 11 clinicians were analyzed. Most issues that patients initiated in discussion were omitted from notes, and nearly half of notes referred to information or observations that could not be verified. Four notes lacked concluding assessments and plans; nine lacked information about when patients should return. Except for thoroughness, PDQI-9 items that were assessed achieved quality scores exceeding 4 of 5 points.ConclusionsAmong outpatient primary care electronic records examined, most issues that patients initiated in discussion were absent from notes, and nearly half of notes referred to information or observations absent from transcripts. EHRs may contribute to certain kinds of errors. Approaches to improving documentation should consider the roles of the EHR, patient, and clinician together.

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.

Majorana subsystem qubit codes that also correct odd-weight errors

A potential platform for topological quantum computation is the Majorana-based tetron architecture. Its building blocks are superconducting islands called tetrons, which host four Majorana zero modes. Existing error correcting codes can correct even-weight errors on tetrons. In a previous proposal by us, we had shown that incorporating tetrons in the stabilizer group allows us to correct a combination of odd-weight errors and even-weight errors on tetrons. In this work, we show that inclusion of tetrons in the gauge group lets us create subsystem codes from conventional Pauli stabilizer codes, which can correct both kinds of errors. Compared to the previous approach, the current approach lets us construct codes with fewer stabilizer generators. This leads to shorter fault-tolerant sequence length, and improves the fault-tolerant pseudothreshold by as much as 84%.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models.

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Saastamoinen and Marini-Murray's Study of the Tropospheric Delay Model in Shijiazhuang

In addition to the non-dispertic medium, the tropospheric medium changes with time, and it is difficult to use a simple and unified mathematical model to determine the degree of change, especially in the long baseline relative positioning, the tropospheric delay between two stations often shows a weak spatial correlation. In this paper, this study will briefly introduce the main methods to reduce or eliminate various kinds of errors in order to build a real-time tropospheric delay model. The main body of this paper is to introduce and discuss the existing direct calculation model and mapping function of tropospheric delay. Based on the meteorological data of Shijiazhuang meteorological station provided by the official website of China Meteorological Administration, the appropriate tropospheric delay correction model and projection function (Saastamoinen +Marini-Murray model) are selected. The hourly tropospheric delay correction model along the propagation path direction in Shijiazhuang city on April 1, 2023 is established, and the accuracy and reliability of the model are analyzed.

Impact Analysis and Compensation Methods of Frequency Synchronization Errors in Distributed Geosynchronous Synthetic Aperture Radar

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares.

Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér-Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.

It is not what you say but how you say it: Evidence from Russian shows robust effects of the structural prior on noisy channel inferences.

Under the noisy-channel framework of language comprehension, comprehenders infer the speaker's intended meaning by integrating the perceived utterance with their knowledge of the language, the world, and the kinds of errors that can occur in communication. Previous research has shown that, when sentences are improbable under the meaning prior (implausible sentences), participants often interpret them nonliterally. The rate of nonliteral interpretation is higher when the errors that could have transformed the intended utterance into the perceived utterance are more likely. However, previous experiments on noisy channel processing mostly relied on implausible sentences, and it is unclear whether participants' nonliteral interpretations were evidence of noisy channel processing or the result of trying to conform to the experimenter's expectations in an experiment with nonsensical sentences. In the current study, we used the unique properties of Russian, an understudied language in the psycholinguistics literature, to test noisy-channel comprehension using only simple plausible sentences. The prior plausibility of sentences was tied only to their word order; subject-verb-object (SVO) sentences were more probable under the structural prior than object-verb-subject (OVS) sentences. In two experiments, we show that participants often interpret OVS sentences nonliterally, and the probability of nonliteral interpretations depended on the Levenshtein distance between the perceived sentence and the (potentially intended) SVO version of the sentence. The results show that the structural prior guides people's final interpretation, independent of the presence of semantic implausibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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%).

A Quantitative Impact Assessment of English Translation Errors on the Authenticity of News Dissemination

Abstract To improve the accuracy of news communication between readers and audiences, it is necessary to avoid the errors generated in the process of news translation so as to make the English translation of news content expression more in line with the actual situation, thus ensuring the authenticity of news dissemination. In this paper, the MapReduce multiple regression model is constructed by optimizing the classical multiple linear regression model in terms of maximum likelihood estimation and gradient descent algorithm with the aim of reducing the errors in the process of English translation of news. This model is used to analyze the errors generated by the English translation process and finally proposes a method for improving translation accuracy based on the factors causing the errors. After testing, the F-value, P-value, and R² of the degree of fit of the MapReduce model are 54.39, 0.005, and 0.893, respectively. Furthermore, the model passed the significance test. The Pearson coefficients of the 4 types of translation errors in the MapReduce multiple linear regression model were −0.673, −0.622, −0.584, and −0.736, respectively, all of which were significantly negatively correlated with the truthfulness of news dissemination at the 1% level. The model analyzes that the three kinds of translation errors are basically between 2%-4%, 7%-20%, and 2%-4%, and puts forward reasonable opinions on the avoidance of the four kinds of errors, namely, “pragmatic translation, cultural translation, linguistic translation and text-specific translation”, which generate errors in the process of the English translation of news.

عبا دات کا میقاتی تصور :سامی ادیان کے تناظر میں ایک مطالعہ

Worship is an integral part of every religion. Now a days there is not any region in world where worship is not practiced in one way or another. Islam is a natural religion. Everything comes under the cover of Islam including Human Rights, Commands of Allah, human relations and interactions with each other etc. All commands, rights and actions are as per path of nature. However, other religions are lacking such kind of delicate balance of nature in every aspect of life. On one side excess and other side deviation is observed. Some religions consider material things core of life while others completely deny it. Islam kept balance between spirit and body. It commands as per requirement of each part. It has not refused one part and encouraged other part of it. Such kind of access and deviations are possible in secular systems but not under the Divine Religion of Islam where everything is written on “Tablet of Heaven”. Therefore there are no chances of such kind of error in this divine religion of Islam. He has been given as much importance and priority as he has the right and demand. It is possible for the human mind and free thinking to be completely inclined towards the other side by denying one aspect, but it is absolutely not possible in the divine system of Islam. Because here are the commands of the Creator of the slaves instead of the slaves and like His essence, His commands are also free from all defects.

Two-Stage Optimal Hypotheses Testing for a Model of Stegosystem with an Active Adversary

We study the information-theoretic model of stegosystem with an active adversary, where unlike a passive adversary he can not only read but also write. The legitimate sender as well as the adversary can embed or not a message in the sending data. The receiver’s first task is to decide whether the communication is a covertext, data with no hidden message, or a stegotext, modified data with a hidden secret message. In case of stegotext, the receiver’s second task is to decide whether the message was sent by a legitimate sender or from an adversary. For this purpose an authenticated encryption from the legitimate sender is considered. In this paper we suggest two-stage statistical hypothesis testing approach from the receivers point of view. We propose the logarithmically asymptotically optimal testing for this model. As a result the functional dependence of reliabilities of the first and second kind of errors in both stages is constructed. A comparison of overall error probabilities with the situation of one stage hypotheses testing is discussed and the behaviour of functional dependences of reliabilities are illustrated.

A Lévy Walk Model not Affected by the Memory Error

In this paper, we investigate the influence of memory errors on a random walk model (the direction-comparison (DC) model), which was developed by the authors. In the DC model, a walker sometimes alters its directional rule, which is dependent on the directional inconsistency between the current direction and the past movement direction. Here, we introduced two different types of memory errors into the DC model: the recall error and the direction error. We found that the DC model was not influenced by a former type of error, suggesting that our model could be robust against some kinds of errors.

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.

Study on the vibration characteristics of worm helical gear drive

The application of worm helical gear in smart car, smart home and other fields can significantly reduce the weight of products. Accordingly, the requirements for vibration and noise are also increased. In this paper, the lumped mass method is used to establish the dynamic model of point contact bending-twisting shaft meshing coupling worm helical gear drive. In this model, the contact ellipse is transformed into an equivalent rectangle, and the nonlinear factors such as time-varying meshing stiffness, tooth meshing error and meshing damping are considered. The vibration characteristics of worm helical gear drive in three directions and at meshing point are obtained for the first time. Machining accuracy, tooth surface modification and assembly errors are transformed into the error in the dynamic model respectively, and their effects on the vibration characteristics are analyzed. The closed vibration and noise performance test bench of worm helical gear drive is built, and the influence of three kind of errors on the vibration characteristics of worm helical gear drive are tested to verify the correctness of the dynamic model and its numerical analysis results. Both the theoretical analysis and the experimental results show that machining accuracy, tooth surface modification and the assembly error have a great influence on the vibration characteristics.

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.