Types Of Errors Research Articles

“Although But...” A Study of Bias in the Narrative Discourse of Thai Overseas Students

This paper takes the Thai students in Guangxi University of Foreign Languages as the research object to investigate their application of Chinese transitional related words "although... But..." And systematically summarize their use of breaking class connective words "although... But..." The types of errors appear in time, and explore the causes of their formation. Finally, based on the research results, targeted teaching suggestions are proposed to help Thai students more accurately grasp the "although... But..." In order to provide reference for teaching transition related words in teaching Chinese as a foreign language, the correct use of these words in narrative texts can improve the writing quality and Chinese expression ability.

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.

Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets

Abstract Recent machine translation (MT) metrics calibrate their effectiveness by correlating with human judgement. However, these results are often obtained by averaging predictions across large test sets without any insights into the strengths and weaknesses of these metrics across different error types. Challenge sets are used to probe specific dimensions of metric behaviour but there are very few such datasets and they either focus on a limited number of phenomena or a limited number of language pairs. We introduce ACES, a contrastive challenge set spanning 146 language pairs, aimed at discovering whether metrics can identify 68 translation accuracy errors. These phenomena range from basic alterations at the word/character level to more intricate errors based on discourse and real-world knowledge. We conducted a large-scale study by benchmarking ACES on 47 metrics submitted to the WMT 2022 and WMT 2023 metrics shared tasks. We also measure their sensitivity to a range of linguistic phenomena. We further investigate claims that Large Language Models (LLMs) are effective as MT evaluators, addressing the limitations of previous studies by using a dataset that covers a range of linguistic phenomena and language pairs and includes both low- and medium-resource languages. Our results demonstrate that different metric families struggle with different phenomena and that LLM-based methods are unreliable. We expose a number of major flaws with existing methods: most metrics ignore the source sentence; metrics tend to prefer surface level overlap; and over-reliance on language-agnostic representations leads to confusion when the target language is similar to the source language. To further encourage detailed evaluation beyond singular scores, we expand ACES to include error span annotations, denoted as SPAN-ACES and we use this dataset to evaluate span-based error metrics, showing that these metrics also need considerable improvement. Based on our observations, we provide a set of recommendations for building better MT metrics, including focusing on error labels instead of scores, ensembling, designing metrics to explicitly focus on the source sentence, focusing on semantic content rather than relying on the lexical overlap, and choosing the right pre-trained model for obtaining representations.

Personalized assessment of eating disorder cognitions during treatment: A new measure of cognitive pathology change

ObjectiveModifying cognitive distortions, or thinking errors, is crucial in eating disorders (ED) treatment. To address the lack of a personalized measure for ED cognitions, the Thought Inventory was developed. The study aimed to establish its feasibility and validity, identify thinking error contents and types, examine changes in belief of irrational thoughts, and investigate associations with change in ED symptoms. Hypotheses, procedure, and planned analyses were pre-registered to ensure transparency. MethodsParticipants (N = 55) completed the Thought Inventory, the Eating Disorder Examination Questionnaire, the Eating Pathology Symptom Inventory, the Frost Multidimensional Perfectionism Scale, the Beck Depression Inventory, and the Penn State Worry Questionnaire at pre-and post-ten weeks of treatment. Using the Thought Inventory, participants collaborated with study therapists to identify ED-related thinking errors and rate the degree of belief in these thoughts on a scale of 0 to 100 %. ResultsCognitions primarily contained self-judgments, food rules, and concern over shape, while catastrophizing/fortune telling, emotional reasoning, and should/must statements were the most common types of thinking errors. Belief in cognitions significantly decreased over treatment and change in thought belief was positively associated with change in ED symptoms. ConclusionThe Thought Inventory shows promise as a personalized measure. Future research should explore whether ED cognitions, assessed in this manner, are a mechanism of change in ED treatment.

Exploring Sources of Errors in L3 English Production of Natives of Moroccan Arabic

Language learners’ writing products can provide insights into how these learners acquire the target language through the analysis of the committed errors. Indeed, research has revealed that error analysis in writing essays is crucial for understanding and improving language acquisition by identifying common errors, and their sources, and informing teaching strategies to address these issues. The current paper aims to identify, describe and analyze errors committed by Moroccan secondary school students in writing. The study also attempts to determine some of the possible causes of the errors to offer an understanding of how linguistic interference influences the foreign language learning process. To this end, a sample of tasks written in English by a group of 20 secondary school students was collected. Then, an analysis procedure was conducted to analyze errors and explain their sources. Hence, this study adopts a mixed-method approach design to generate qualitative as well as quantitative data by identifying error categories and calculating the total number of error types and sources, respectively. The findings show that the errors are committed both at the word as well as the sentential level including lexical, grammatical, substance, and syntactic errors. Besides, the results indicate that learners commit errors in their English writings partly due to interference of Moroccan or Standard Arabic and French, but mostly due to other developmental sources within the scope of the target language. These findings offer valuable insights into how learners acquire a second or foreign language and what obstacles they encounter in the learning process and, thus, could suggest some implications to reduce the errors caused by negative language transfer and improve learners’ language proficiency.

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying consequences. To address this asymmetry issue, two popular paradigms have been developed: the Neyman-Pearson (NP) paradigm and the cost-sensitive (CS) paradigm. Previous studies on the NP paradigm have primarily focused on the binary case, while the multi-class NP problem poses a greater challenge due to its unknown feasibility. In this work, we tackle the multi-class NP problem by establishing a connection with the CS problem via strong duality and propose two algorithms. We extend the concept of NP oracle inequalities, crucial in binary classifications, to NP oracle properties in the multi-class context. Our algorithms satisfy these NP oracle properties under certain conditions. Furthermore, we develop practical algorithms to assess the feasibility and strong duality in multi-class NP problems, which can offer practitioners the landscape of a multi-class NP problem with various target error levels. Simulations and real data studies validate the effectiveness of our algorithms. To our knowledge, this is the first study to address the multi-class NP problem with theoretical guarantees. The proposed algorithms have been implemented in the R package npcs, which is available on CRAN.

온라인 동영상 수업 후 서술형 평가상의 쓰기에서 나타나는 오류 분석: 공과대학 1학년 기초미적분학 강좌를 중심으로

Objectives This study attempted to qualitatively examine the errors that appear in the descriptive type assessment after online video classes. Methods For this purpose, we qualitatively analyzed the errors appearing in 5 descriptive type questions in the mid-term and final exams for first-year engineering students at a local college. They are taking basic precalculus in the first semester of 2022. The 5 questions were divided into drawing a graph(3 questions) and descriptive type writing(2 questions), and the types of errors that emerged as a result are as follows. Results First, in the drawing a graph questions, there were errors in conceptual errors, errors regarding the domain, asymptote notation errors, coordinate notation errors, writing explanation errors, omitting convexity mentions, omitting inflection points mentions, and errors of omitting algebraic expressions for determining graph shape). Second, in the descriptive type writing questions, errors in problem recognition, incorrect use of problem conditions, substitution errors, rationalization errors, technical errors, omission of the solution process, logically inappropriate reasoning, and errors due to mistakes or carelessness appeared. Conclusions In addition, as a result of a detailed analysis according to the type of error shown in each question, it was emerged that college students also had errors similar to those made by high school students in drawing graphs and solving descriptive type problems.

Assessment of pediatric visual acuity

The VA assessment of children within the different age groups is a very tough task somewhat due to of cooperation of children. However, the method of estimation of pediatric VA is based on different types of concern according to age. VA is a compound function of the minimum visibility, resolution, recognition and minimum discriminability. Sometimes the VA is hampered due to the types of refractive error and the initial correction of the error is also an important issue. The 5 to 6 years of age children with normal growth respond easily and they can be observed with the adult method but the youngest school-age children can be estimated with nonverbal methods. The growth of social and educational development of the children can retard due to acuity impairment but early detection and visual correction enhances the issue.The pathological disorders in childhood like corneal and lenticular disorders, macular degeneration, tumours, multiple sclerosis etc., turning of the eye from the standard called strabismus and it happens due to the reduction of the VA and the loss of binocular vision. Thus visual acuity is an approach for the assessment of ocular health along with the visual brain and its pathway.

LINGPASS: An approach for multilingual passphrase generation by integrating English and Tamil

AbstractIn today's world of ever‐increasing cyber threats, safeguarding digital accounts is paramount. Traditional passwords are often vulnerable to brute force attacks and can be easily guessed, leading to a shift towards passphrases. However, using monolingual passphrases can pose several challenges, such as lack of diversity, predictability, memorability, and accessibility. To address this issue, this paper presents a model called LINGPASS, short for Language INtegrated Graph‐based PASSphrase, which generates multilingual passphrases by combining English and Tamil languages. The proposed approach is implemented as a web application for user convenience, and the entropy of the passphrase is evaluated using Shannon entropy. The LINGPASS model generates higher entropy passphrases than existing multilingual passphrase generators, increasing the security of digital accounts. By integrating two languages, LINGPASS increases the entropy of the passphrase by approximately 72%, making it less vulnerable to dictionary attacks. Additionally, the copy/paste functionality reduces typing errors, making it more user‐friendly.

Perceptual inference corrects function word errors in reading: Errors that are not noticed do not disrupt eye movements

Both everyday experience and laboratory research demonstrate that readers often fail to notice errors such as an omitted or repeated function word. This phenomenon challenges central tenets of reading and sentence processing models, according to which each word is lexically processed and incrementally integrated into a syntactic representation. One solution would propose that apparent failure to notice such errors reflects post-perceptual inference; the reader does initially perceive the error, but then unconsciously ’corrects’ the perceived string. Such a post-perceptual account predicts that when readers fail to explicitly notice an error, the error will nevertheless disrupt reading, at least fleetingly. We present a large-scale eyetracking experiment investigating whether disruption is detectable in the eye movement record when readers fail to notice an omitted or repeated two-letter function word in naturalistic sentences. Readers failed to notice both omission and repetition errors over 36% of the time. In an analysis that included all trials, both omission and repetition resulted in pronounced eye movement disruption, compared to reading of grammatical control sentences. But in an analysis including only trials on which readers failed to notice the errors, neither type of error disrupted eye movements on any measure. Indeed, there was evidence in some measures that reading was relatively fast on the trials on which errors were missed. It does not appear that when an error is not consciously noticed, it is initially perceived, and then later corrected; rather, linguistic knowledge influences what the reader perceives.

Mental effort during mindless reading? Pupil fluctuations indicate internal processing during levels of inattention.

Mind wandering, an experience characterized by a reduced external focus of attention and an increased internal focus, has seen significant theoretical advancement in understanding its underlying cognitive processes. The levels-of-inattention hypothesis posits that in mind wandering, external attention is reduced in a graded fashion, reflecting different levels of weak versus deep attentional decoupling. However, it has remained unclear whether internal processing during mind wandering, and mindless reading in particular, requires effort and, if so, whether it is graded or distinct. To address this, we analyzed pupil size as a measure of cognitive load in the sustained-attention-to-stimulus task during text reading. We examined whether decoupled external attention is linked to an overall reduction in workload and whether internal focus of attention is graded or represents a distinct cognitive process. Overall, overlooking errors in the text was associated with a small pupil size, indicating reduced effortful processing. However, this effect varied with error type: overlooking high- or medium-level errors (weak decoupling) resulted in reduced pupil size, while overlooking low-level errors (deep decoupling) had no effect on pupil size. Moreover, detecting an error (at any processing level) elicited a task-evoked pupillary response, which was absent when it was overlooked. These findings suggest that weak decoupling reduces internal resource-demanding processing and are in line with the hypothesis that large pupils during deep decoupling may be associated with distinct states of effortful internal processing. They further support both the levels-of-inattention hypothesis and the notion that internal focus is a distinct mode of deeply decoupled processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integrating error correction and detection techniques in RISC-V processor microarchitecture for enhanced reliability

An essential consideration in processor design is ensuring reliability, particularly in demanding environments such as outer space and nuclear plants. To mitigate the effects of errors and enable error recovery, processors need to incorporate fault tolerance techniques. One common type of error is SEU (Single Event Upset), which affects various microelectronic devices including microprocessors, microcontrollers, and semiconductor memory devices. While error mitigation techniques have been developed for processors based on architectures like ARM (Advanced RISC Machine) and MIPS (Million Instructions Per Second), there is a gap in research for open-source ISAs (Instruction Set Architecture) like RISC-V, which this paper aims to address. This paper focuses on designing a fault-tolerant microarchitecture for a RISC-V processor that can correct one-bit errors, detect up to two-bit errors, and integrate lockstep and pipeline rollback features at a lower LUTs (Look Up Tables) consumption by re-using the same hardware pipeline for error mitigation and recovery through instruction mimicking. By incorporating these features, the proposed approach enhances the system’s fault tolerance by detecting and correcting errors caused by transient events and achieves a lower effective die size upon realization compared to contemporary works. The proposed microarchitecture design was simulated and synthesized using the Vivado Design Suite 2023.1 and implemented on a Zynq 7000 SoC ZC702 Evaluation Kit.

Measuring error in the Demographic Index – steps towards a future population statistics system in England and Wales

ObjectiveWe are developing methods to measure the quality of the Demographic Index at a National Statistics Institute. The Demographic Index is the foundational dataset for a future, transformed, population statistics system for England and Wales. It clusters records for individuals across many years of health, education, and tax data, and assigns them a unique ID. Previous research has helped us to conceptualise three types of error in the Demographic Index: clustering error, coverage error, and data measurement error. We are currently working on estimating one type of clustering error, whereby records for two individuals are mistakenly assigned the same ID (“false positive clusters”). ApproachPrevious work has allowed us to identify variables associated with false positive clusters. We are now working on a stratification method, whereby each ID in the Demographic Index is scored according to how likely it is to contain this error. We plan to measure uncertainty using bootstrapping. Results and ConclusionsThis work is ongoing, and we hope to obtain results by summer 2024. ImplicationsIt is vital for us to understand and measure error in the Demographic Index because this is necessary for measuring error in any statistics derived from the Index. And it is crucial that population statistics made by any future system should have measures of error. Assuming that our work shows promise, we will continue by measuring other types of error, and by creating use cases to test how these measures can be fed forward into secondary analyses (e.g. population estimation).

ANALISIS ATURAN ASOSIASI KESALAHAN STATISTIKA SISWA KELAS X-11 SMA NEGERI 10 SAMARINDA BERDASARKAN KRITERIA WATSON

Mathematics plays an important role in forming systematic thinking patterns, logic, and financial management skills. However, many students have difficulty in understanding mathematical concepts due to various factors, which affect the success of learning. This study aims to identify and classify common errors of class X-11 students of SMA Negeri 10 Samarinda in solving statistics problems using mixed quantitative and qualitative methods. solving statistics problems using mixed methods, namely quantitative and qualitative. Using the apriori algorithm with a minimum support of 20% and 80% confidence, four types of errors were found that were often made by students, namely incorrect data errors, procedural errors, missing conclusions, and skill hierarchy problems. Data analysis and interviews showed a strong correlation between these errors, where errors in data collection often lead to data manipulation and incorrect conclusions. To overcome this problem, the school has implemented more effective learning strategies, such as additional guidance, material adjustments, and contextual approaches. These strategies are expected to improve students' conceptual understanding, reduce errors, and improve academic achievement in statistics.

Misses or False Alarms? How Error Type Affects Confidence In Automation Reliability Estimates

Prior research has shown that automation errors—false alarms and misses—differentially impact operator behavior, yet it is not clear why this difference exists. This study examined how the type of automation error, the automation’s reliability, and the number of experiences a person has with an automated system impacts their perception of the system. Participants responded to the correctness of an automation recommendation about the match between pairs of Mega Block shapes while automation reliability, number of trials, and error type varied. At the end of each block, participants provided their estimates of the automation’s reliability, ratings of their confidence in their reliability estimate, and their trust in the system. Our findings indicated that the type of automation error does not impact operator perceptions of the system. Instead, factors such as working memory limitations and pre-existing biases impact these perceptions.

Translating Arabic Free Poetry Texts into English by ChatGPT: Success and Failure

The purpose of this study is to examine the success and failure of ChatGPT in translating some Arabic free poetry texts selected from Wahib's 'Hulm 'ala Nasiat Aljabin 'A Dream on the Corners of Nostalgia.’ In this study, we examined ChatGPT’s ability in the translation field. In particular, the focus is on translating in Arabic context. The free poetry texts were first translated by the authors and then by ChatGPT. Both translations were then analyzed and compared to find out to what extent ChatGPT translation deviates from human translation. A qualitative approach is adopted with the help of comparative and analytical methods. The results show that ChatGPT is, to some extent, successful in producing a natural English translation of the Arabic poems in this study. In contrast, humans adopt translation strategies like paraphrasing, adapting, recreating, and transliteration. ChatGPT adopts translation strategies like paraphrasing addition, word-for-word, and literal translation. However, there is no care for the aesthetic values in ChatGPT’s translation, and many types of translation errors result from applying these strategies.

Analyzing the Impact of CALL Tools on English Learners' Writing Skills: A Comparative Study of Errors Correction

The study seeks to compare the effectiveness of Computer-Assisted Language Learning Tools (ChatGPT, Grammarly, and Google Translate) in correcting the occurrences of common errors in English as a second/foreign language (ESL/EFL) learners’ writing. An experimental design offered instructions for Class A with ChatGPT, Class B with Grammarly, Class C with Google Translate, and Class D was the control group with no exposure to any of these tools. Data were collected from texts created by the students based on pictures, both before and after the intervention. It was found that ESL/EFL learners frequently made eight distinct types of errors in their writing: spelling, punctuation, capitalization, possessive words, verb forms, subject-verb agreement, articles, and prepositions. The interventions across different classes showed that ChatGPT in Class A significantly corrected spelling, verb form, and subject-verb agreement-related errors, Grammarly in Class B excelled in correcting prepositional errors, and Google Translate in Class C effectively addressed errors in article use, capitalization, and possessives. In contrast, the control group in Class D surpassed the others in enhancing punctuation. In Class A, ChatGPT, and in Class B, Google Translate displayed enhanced effectiveness in rectifying common writing errors of ESL learners compared to Grammarly in Class B and the control group. The findings suggest that employing targeted digital tools for distinct grammatical challenges can substantially enhance learning outcomes in a language learning context.

Clinical Decision Support as a Prevention Tool for Medication Errors in the Operating Room: A Retrospective Cross-Sectional Study.

Medication errors in the operating room have high potential for patient harm. While electronic clinical decision support (CDS) software has been effective in preventing medication errors in many nonoperating room patient care areas, it is not yet widely used in operating rooms. The purpose of this study was to determine the percentage of self-reported intraoperative medication errors that could be prevented by CDS algorithms. In this retrospective cross-sectional study, we obtained safety reports involving medication errors documented by anesthesia clinicians between August 2020 and August 2022 at a 1046-bed tertiary care academic medical center. Reviewers classified each medication error by its stage in the medication use process, error type, presence of an adverse medication event, and its associated severity and preventability by CDS. Informational gaps were corroborated by retrospective chart review and disagreements between reviewers were resolved by consensus. The primary outcome was the percentage of errors that were preventable by CDS. Secondary outcomes were preventability by CDS stratified by medication error type and severity. We received 127 safety reports involving 80 medication errors, and 76/80 (95%) of the errors were classified as preventable by CDS. Certain error types were more likely to be preventable by CDS than others ( P < .001). The most likely error types to be preventable by CDS were wrong medication (N = 36, 100% rated as preventable), wrong dose (N = 30, 100% rated as preventable), and documentation errors (N = 3, 100% rated as preventable). The least likely error type to be preventable by CDS was inadvertent bolus (N = 3, none rated as preventable). Ninety-five percent of self-reported medication errors in the operating room were classified as preventable by CDS. Future research should include a randomized controlled trial to assess medication error rates and types with and without the use of CDS.

Prescribing Errors in an Ambulatory Care Setting: Mitigating Risks in Outpatient Medication Orders, Cross-Sectional Review

ABSTRACT Introduction Prescribing errors (PEs) are the most common type of medication error, which may occur by prescribing the wrong medication, improper dose, dosage, and/or even prescribing a drug to the wrong patient. The present study aims to compile PEs that were generated in an ambulatory care setting at a tertiary-care hospital in Saudi Arabia. Methods A retrospective cross-sectional review was conducted for all reported PEs in ambulatory care clinics for 3 years. The potential hazardous outcomes of these PEs were classified according to the medication error index. Results A total of 897 records containing 1199 PEs were retrieved. More than a third of prescribers had frequently committed PEs—ranging from 2 to 39 times. The most encountered errors were prescribing incorrect doses, medication duplication, incorrect dosing frequency, and inappropriate duration (34.5%, 14.1%, 11.6%, and 9.8%, respectively). The most frequent mistakes were when prescribing antibiotics (22.9%) and drugs for cardiovascular conditions (18.5%). Most errors were of mild to moderate severity, mostly type-B near-miss errors and did not reach patients. Only two prescription events (0.17%) had severe consequences that required intervention to avoid any subsequent harm or damage. Conclusion The current investigation has revealed a substantial percentage of PEs, mostly in internal medicine and cardiology departments. Although PEs are undoubtedly not easy to avoid, monitoring and recognizing these inaccuracies is pivotal to preventing potential harm and promoting patient safety.

The Dispensing Error Rate in an App-Based, Semaglutide-Supported Weight-Loss Service: A Retrospective Cohort Study.

Digital weight-loss services (DWLSs) combining pharmacotherapy and health coaching have the potential to make a major contribution to the global struggle against obesity. However, the degree to which DWLSs compromise patient safety through the dispensation of Glucagon-like peptide-1 receptor agonist (GLP-1 RA) medications is unknown. This study retrospectively analysed the rate at which patients reported GLP-1 RA dispensing errors from patient-selected and partner pharmacies of Australia's largest DWLS provider over a six-month period. The analysis found that 99 (0.35%) of the 28,165 dispensed semaglutide orders contained an error. Incorrect dose (58.6%) and unreasonable medication expiry window (21.2%) were the two most common error types. Most errors (84.9%) were deemed to have been of medium urgency, with 11.1% being considered high-urgency errors. Incorrect doses (45.5%) and supplies of the wrong medication (36.3%) comprised most errors reported in high-urgency cases. Female patients reported more dispensing errors than male patients (0.41% vs. 0.12%, p < 0.001). Similarly, reported dispensing error rates were highest among patients aged 18 to 29 years (0.6%) and 30 to 39 years (0.5%). This research provides preliminary evidence that GLP-1 RA dispensing errors within comprehensive Australian DWLSs are relatively low.