Error Classification Research Articles

Error patterns in student translations: A study of grammatical issues in higher education

The importance of learning English has grown significantly in response to its role as a global means of communication. Mastery of English, particularly its grammatical aspects, is essential for effective communication and is often developed through formal education, such as university programs. This study examines the grammatical competence of undergraduate English Literature students during the 2022/2023 academic year, as reflected in their translation texts. The study analysed 11 translation texts selected from the population using Corder’s superficial basis error classification framework, encompassing four types of errors: omission, addition, selection, and ordering. The classified data were further analysed using general English grammar theories. The findings reveal significant grammatical errors in the students' translations: 49 errors of omission (48%), 7 errors of addition (7%), 40 errors of selection (30%), and 5 errors of ordering (5%). These results highlight specific areas of grammatical difficulty faced by students, offering insights into the need for targeted instructional strategies to improve their grammatical proficiency. This research underscores the importance of error analysis in enhancing the quality of English language education in higher learning institutions.

Classification of Scanning Errors of Digital Scans Recorded by Using Intraoral Scanners.

The different scanning errors that can be caused by the operator handling an intraoral scanner (IOS) or the intraoral conditions of the patient being scanned have not been described. The purpose of this review was to describe and classify the scanning errors that can be identified in digital scans recorded by using IOSs. The identification of scanning errors in an intraoral scan and understanding the cause of these scanning errors are fundamental procedures for successfully handling an IOS and integrating these digital data acquisition technologies in dental practices. There are two main types of scanning errors: the ones created by the operator and the ones caused by the intraoral conditions of the patient. There are seven operator-related scanning errors: mesh hole, stitching, tissue, reliability, umbrella, implant scan body geometry, and scanning noise errors. Additionally, there are four patient-related scanning errors: humidity, bridge, fuzzy finish line, and scanability noise errors. The identification of scanning errors is fundamental for assessing the quality of an intraoral digital scan. The comprehensive reading of these scanning errors allows the dental professional to understand if the scanning error can be corrected or if it is related to hardware/software limitations of IOSs.

Impact of Pharmacological Competencies on the Reduction of Medication Errors in Critical Care Units

The use of drugs to treat disease and relieve pain has historical roots that evolved significantly over time, especially in the 20th century, when pharmacology became a central discipline in healthcare and nursing. This practice, although essential for patient recovery, especially in high-intervention settings such as intensive care units, is prone to errors that can have serious consequences for patients. The National Coordinating Council for Medication Error Prevention (NCC MERP) has developed error classification and analysis tools, emphasizing that errors are often preventable and multifactorial, highlighting the importance of nursing staff pharmacological knowledge.Several studies have identified the causes and consequences of medication errors. In Ecuador, errors were related to high patient demand, non-compliance with schedules and poor administration technique. In Cuba, a general mastery of medications was observed, but a deficiency in the recognition of adverse reactions. In Spain, pharmacovigilance showed low rates of spontaneous reporting of adverse events, attributable to insufficient training in pharmacology. In critical care units, the most common errors included administration, dilution and dosing techniques, exacerbated by pressure of care and communication problems.In this context, nursing plays a crucial role as it is at the end of the pharmacotherapeutic process, acting as the last barrier to prevent errors from reaching the patient. This study in an Intensive Care Unit in Rosario seeks to delve into this problem, highlighting its impact on patients and on the professional development of nursing staff, with the aim of providing practical and theoretical solutions

Stylistic Errors in Arabic Sentences Writing and Their Factors Among Arab Students Outside the Arab Countries "Malaysia as a Model" Language Acquisition

This study aimed to identify stylistic errors among students of the Global Modern International School (GMIS) school, and to reveal the factors and causes that led to their occurrence. The researcher conducted this study in the second semester of the academic year 2022 AD, using the descriptive analytical approach to analyze errors, describe them and explain their causes, and applied it to a sample of (32) male and female sixth grade primary school students based on a free writing test, and the choice of writing included one topic out of three topics; the first is a trip to the beach, the second is to describe your life in Malaysia, and the third is the importance of the Internet. The most important results reached by this study are: The percentage of the size of the linguistic repertoire among the sample students reached (78%), and the percentage of stylistic errors reached (53%), combined. The total grammatical and morphological errors reached (32%). The total number of spelling errors reached (51%). The most important factors and causes of these errors were psychological factors such as: tension and haste, in addition to the effect of the English language as the language of instruction, and the effect of the Arabic dialects of the sample students, in addition to the lack of reading materials such as books, magazines, and Arabic newspapers, and the lack of time allocated to learning the Arabic language because the Arabic language is just an additional subject in the international Global Modern International School (GMIS) curriculum. Keywords: Stylistic errors, error analysis, Arabic sentence writing, error classification, Arab students

LEXICAL COLLOCATIONAL ERRORS MADE BY EFL HIGH SCHOOL STUDENTS

This research paper addresses various lexical collocational errors made by EFL private school students, aiming to investigate their writings (essays and paragraphs) through detailed analysis. The primary objectives of this study are to identify various lexical collocation errors in the written compositions of students at the school level and to provide the correct forms of these lexical collocations to enhance their writing accuracy. The study utilizes both qualitative and quantitative methods, focusing on a sample of 50 randomly selected students (males and females), who participated voluntarily. By using qualitative and quantitative methods and adopting the classification of lexical collocational errors by Benson et al. (1997), for data analysis and to check the acceptance of word combinations, these errors were manually identified in the target written tasks. Then, the researchers used the HASK collocation database (www.pelcra.pl/hask_en/), a reliable online resource containing a large number of collocations. The extracted errors were analyzed and interpreted, with their frequencies and percentages tabulated using Excel bar charts. The findings of the study conclude that EFL students make diverse errors in using lexical collocations in their writing when they try to express their thoughts and ideas. In the analysis of 50 academic pieces of writing, 58 lexical collocation errors were found. It was also found that two specific patterns, (verb + noun) and (verb + adverb) were rarely or almost never used by the participants of the study. The study also found that literal translation from the native language to the target language and limited knowledge of collocations were the main reasons behind these errors. The current study is valuable since collocations play an essential role in improving writing skills. Particularly, the identification of common lexical collocation errors helps educators target specific instructional areas and understand their impact on students' long-term language development. The findings also aid curriculum developers in addressing lexical challenges faced by EFL high school students.

Assessing Chinese-to-English Translation Quality – A Systemic Functional Linguistics Perspective

Translation quality assessment is an important issue in translation teaching and learning, but it has been under-researched in translation studies. Whether a translation is good or not depends largely on a translator’s ability of text analysis. Taking the translation task of TEM8 (Test for English Majors Band 8) in 2023 as an example, this paper presents a pilot project aimed at exploring a systematic way of analyzing translation errors by referring to systemic functional linguistics (SFL). In particular, the paper investigates how SFL-based text analysis of ideational meaning, interpersonal meaning and textual meaning can be used for translation teaching and learning, through comparative analyses of a set of texts, including a Chinese source text, two translation texts from TEM8 in 2023, and an AI-generated literal translation as back translation. The study finds that it is possible to identify, describe and classify translation errors in the translated texts, and more significantly, the resulting error description and classification allows translation teachers a more precise expression of the nature of poor translation or translation errors that would otherwise be simply put as “inadequate” or “awkward” translation, and students a more tangible understanding of what counts as an “excellent” translation. Following the analyses, the paper discusses the pedagogical effects of SFL-based text analysis by conducting a survey and semi-structured interviews with students. The quantitative data show that overall, students held a positive attitude towards translation, and the qualitative data analysis uncovers specific benefits and challenges experienced by the students.

MACHINE LEARNING IN ADAPTATION OF EDUCATIONAL TEXTS: LEXICAL ASPECT

This paper presents the results of approbation of machine learning methods for adapting educational texts for the purpose of teaching non-native learners in Russian. The testing was carried out on the text of the textbook on the history of Russia for the 11th grade, in which synonymic substitutions of a number of lexemes were made for words included in the lexical minimum of the A1 level. The preparatory stage of adaptation is described and the classification of errors made in the text as a result of adaptation using machine learning methods is presented.

Optimized AI-MPM: Application of PSO for tuning the hyperparameters of SVM and RF algorithms

Modern computational techniques, particularly Support Vector Machines (SVM) and Random Forest (RF) models, are revolutionizing predictive mineral prospectivity mapping. These advanced systems excel at identifying prime resource locations but require meticulous fine-tuning of their internal settings to achieve peak performance. Careful calibration of these configurations during the learning phase significantly enhances their ability to detect promising deposits. The main goal of this study is to introduce a hybrid model called PSO -SVM and PSO-RF, which aim to combine particle swarm optimization (PSO) with SVM (with RBF kernel) and RF models. This hybrid model automatically adjusts the optimized hyperparameters of SVM and RF, resulting in highly accurate predictions and a wide range of applicability. The PSO algorithm has been applied to fine-tune two main parameters (C and λ) for SVM-RBF and three main parameters (NT, NS, and d) for RF, creating efficient models for both. The proposed hybrid model as well as the traditional versions of SVM and RF models, were tested using a geo-spatial dataset related to Cu mineralization in Kerman belt, SE Iran. Forecasting algorithms were developed by integrating diverse datasets: multi-element concentrations from stream samples, bedrock and fault line maps, indicators of hot fluid interaction, aeromagnetic survey results, coordinates of previously identified copper-rich igneous intrusions, and verified ore body positions as reference points. The models' performance was evaluated using four validation methods: Multi-round data partitioning (K-fold), error classification tables (confusion matrix), true-positive vs. false-positive graphical analysis ((ROC) curve), and P-A plot were used to assess algorithms and models performance. Tests revealed that the PSO-SVM surpassed all competitors. Impressively, this fine-tuned classifier identified prime target zones in merely one-seventh of the region (14%), yet these areas encompassed nearly all verified resource sites (97%).

High frequency radar error classification and prediction based on K-means methods

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation.

Evaluating Text-to-Image Generated Photorealistic Images of Human Anatomy.

Generative artificial intelligence (AI) models that can produce photorealistic images from text descriptions have many applications in medicine, including medical education and the generation of synthetic data. However, it can be challenging to evaluate their heterogeneous outputs and to compare between different models. There is a need for a systematic approach enabling image and model comparisons. To address this gap, we developed an error classification system for annotating errors in AI-generated photorealistic images of humans and applied our method to a corpus of 240 images generated with three different models (DALL-E 3, Stable Diffusion XL, and Stable Cascade) using 10 prompts with eight images per prompt. The error classification system identifies five different error types with three different severities across five anatomical regions and specifies an associated quantitative scoring method based on aggregated proportions of errors per expected count of anatomical components for the generated image. We assessed inter-rater agreement by double-annotating 25% of the images and calculating Krippendorf's alpha and compared results across the three models and 10 prompts quantitatively using a cumulative score per image. The error classification system, accompanying training manual, generated image collection, annotations, and all associated scripts, is available from our GitHub repository at https://github.com/hastingslab-org/ai-human-images. Inter-rater agreement was relatively poor, reflecting the subjectivity of the error classification task. Model comparisons revealed that DALL-E 3 performed consistently better than Stable Diffusion; however, the latter generated images reflecting more diversity in personal attributes. Images with groups of people were more challenging for all the models than individuals or pairs; some prompts were challenging for all models. Our method enables systematic comparison of AI-generated photorealistic images of humans; our results can serve to catalyse improvements in these models for medical applications.

You cannot have both – Two different ways of change implementation and their effects on adaptive performance and frustration

The dynamics and frequency of change in today's workplaces force organizations to address the question of how to create optimal conditions for facilitating adaptation to change. Identifying critical factors that affect performance and well-being in change situations can help answer this question. Given the need for applied research in this area, the aim of this paper is to investigate whether the way of change implementation has an impact on performance and well-being. Additionally, retentivity and specific self-efficacy as individual factors are exploratorily investigated. In an experimental study, seventy-one participants learned and trained specific manufacturing steps in a simulated production environment. Subsequently, they had to adapt the trained task execution as some of the previously learned manufacturing steps were subject to change. The changes were implemented a) either in a stepwise manner or b) all at once. The executed manufacturing steps were recorded, enabling the calculation of task execution time and classification of adaptation errors. The results of Mann-Whitney-U-tests showed that the stepwise introduction of changes led to significantly faster task execution time than the change introduction all at once. Additionally, descriptive statistics indicated fewer adaptation errors but higher frustration. Our exploratory analysis showed a significant negative relationship between retentivity and task execution time, as well as between specific self-efficacy and frustration. Stepwise change introduction seems to be beneficial for adaptation, even though emotional responses and individual factors must be considered.

Customer Value Classification Model Based on Improved TOPSIS Method and BP-Adaboost Algorithm

Identifying customer groups with different consumption values can help enterprises understand and analyze customers' purchasing behaviors and preferences, so as to effectively improve their operating profits. In this paper, the customer value evaluation index system is constructed on the basis of existing research on customer value classification using entropy weight method and improved TOPSIS method, and different grades are classified according to the comprehensive score of customers. Subsequently, the customer comprehensive score is used as the output layer, and the BP-Adaboost algorithm is used to construct the customer value classification model. The empirical results show that the improved TOPSIS method is more advantageous in terms of error control and classification accuracy, and the BP-Adaboost algorithm outperforms the traditional BP neural network in the customer value classification model, which improves the classification precision and accuracy. This study provides a powerful tool for dealing with the customer value classification problem, which helps enterprises make more accurate decisions in customer relationship management and resource optimization allocation.

CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study

ObjectiveThis study aims to assess the consistency of various CT-FFR software, to determine the reliability of current CT-FFR software, and to measure relevant influence factors. The goal is to build a solid foundation of enhanced workflow and technical principles that will ultimately improve the accuracy of measurements of coronary blood flow reserve fractions. This improvement is critical for assessing the level of ischemia in patients with coronary heart disease.Methods103 participants were chosen for a prospective research using coronary computed tomography angiography (CCTA) assessment. Heart rate, heart rate variability, subjective picture quality, objective image quality, vascular shifting length, and other factors were assessed. CT-FFR software including K software and S software are used for CT-FFR calculations. The consistency of the two software is assessed using paired-sample t-tests and Bland-Altman plots. The error classification effect is used to construct the receiver operating characteristic curve.ResultsThe CT-FFR measurements differed significantly between the K and S software, with a statistical significance of P < 0.05. In the Bland-Altman plot, 6% of the points (14 out of 216) fell outside the 95% consistency level. Single-factor analysis revealed that heart rate variability, vascular dislocation offset distance, subjective image quality, and lumen diameter significantly influenced the discrepancies in CT-FFR measurements between two software programs (P < 0.05). The ROC curve shows the highest AUC for the vessel shifting length, with an optimal cut-off of 0.85 mm.ConclusionCT-FFR measurements vary among software from different manufacturers, leading to potential misclassification of qualitative diagnostics. Vessel shifting length, subjective image quality score, HRv, and lumen diameter impacted the measurement stability of various software.

The effect of using AI and Google Translate on translating BBC media texts into Arabic

Recent post-human translation studies increasingly explore the intricate relationship between artificial intelligence (AI) and translation. This paper examines the complexities of AI, with a focus on machine translation (MT), and its transformative effect on translation in light of technological advancements. Such transformation has led to new roles and skills for translators. Our research primarily scrutinizes the style and messaging quality of English-Arabic media text translations. We aim to illuminate how AI and MT challenge the translation industry, discussing both potential benefits and risks for language professionals, and presenting a theoretical framework for the changing roles of human translators. This study specifically identifies common errors in MT for Arabic-English news texts and vice versa, assessing translation quality, fluency, semantic accuracy, and the need for human intervention in corrections. We selected purposeful examples from online newspapers and used a qualitative method for data analysis, identifying errors based on Hsu's (2014) machine translation error classification.

Adaptive Cybersecurity Neural Networks: An Evolutionary Approach for Enhanced Attack Detection and Classification

The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP’s weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. Its performance was compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, and HHO. The results demonstrated that the proposed trainer consistently outperformed the other algorithms, achieving the lowest Mean Square Error (MSE) and highest classification accuracy across all datasets. Notably, the trainer reached a classification rate of 99.5% on the Bot-IoT dataset and 98.8% on the CSE-CIC-IDS2018 dataset, underscoring its effectiveness in detecting and classifying diverse cyber threats.

Языковое образование онлайн: принципы создания размеченного корпуса ошибок в специализированном английском языке русскоязычных работников IT-сферы

Today, academic corpora are a matter of great interest to many linguists. The article examines the importance of academic corpora in modern linguistics and pedagogy as a tool for identifying typical errors in the speech of foreign language students. They reveal problems in foreign language acquisition and create new teaching methods. The paper reviews the existing academic corpora and error classifications. The authors developed a corpus of oral speech errors typical of Russian-speaking IT students that study English for IT purposes. The corpus relied on 50 video recordings of in-class activities in which students communicated with English-speaking IT specialists and made up dialogues in English. The corpus made it possible to identify the main difficulties experienced by adult learners of English for IT purposes. It involves a tagged classification of typical errors divided by language level into morphological, syntactic, lexical, and phonetic ones. The research demonstrated good prospects for developing other academic speech corpora with automated error tagging based on the speech of English learners.

The static voltage stability analysis of photovoltaic energy storage systems based on NPU algorithm

Although the data-driven static voltage stability problems have been widely studied, most of the classical algorithms focus more on improving the accuracy of the system prediction, ignoring the error classification errors generated during the prediction process. Furthermore, current research ignores the utilization of data-driven voltage stability assessment of energy storage systems. Therefore, this paper proposes a static voltage stability assessment method for photovoltaic energy storage systems based on considering the error classification constraint algorithm using Neyman-Pearson umbrella algorithms. Firstly, the Spearman Correlation Coefficient is employed in the feature selection phase. Secondly, an updated voltage stability assessment (VSA) model is proposed. Compared with the existing data-driven prediction of system static voltage stability in the literature, it can realize voltage stability assessment more quickly. Furthermore, on the basis of rapid voltage stability assessment, the umbrella NP classifier can also effectively limit the first-class error and attenuate the effect of error classification by mirroring the control of the number of cycle splits and the type I classification error threshold. Finally, the simulation and experimental results show that the effectiveness and robustness of the scheme proposed in this paper in grid-connected photovoltaic energy farms.

Fast Classification of Error Correcting Codes based on Syndrome a Posteriori Probability Spectrum for Intelligent Communication

Abstract Classification of channel codes has drawn widespread concern for a long time. In this letter, we propose a posteriori probability based approach to classify cyclic redundancy check codes and convolutional codes. We defined the posteriori probability spectrum to clarify the parity-check relationship between the intercepted codewords and the candidate parity-check vector generated by traversing. We theoretically explained that the posteriori probability spectrum of cyclic redundancy check codes and convolutional codes show clear distinctions. We further specify the feature parameter generated from the posteriori probability spectrum to do classification. Simulation results show that the posteriori probability spectrum for cyclic redundancy check codes and convolutional codes are significantly different. The proposed approach outperforms traditional Gauss-Jordan elimination through pivoting approach in classification accuracy.

Emergence of Paper-Digital Systems: A Usability Evaluation of Single-Page Versus Multipage Digital Instructions in a Ballot Mailing Task

Findings from previous research that assessed the usability of single-page and multipage digital interfaces in purely digital interactions indicated that the single-page format is more efficient than its multipage counterpart. This research expands on previous work by applying the findings from these digital-only interactions to a paper-digital interaction. Specifically, this study assessed the usability of single-page and multipage instructional interfaces to guide voters through the paper-based ballot mailing process embedded in the prototype of an electronic voting system designed for overseas military voters. A detailed classification of errors and requests for assistance revealed that the multipage format had fewer occurrences of both than the single-page format. Statistical analyses revealed no statistically significant differences between the single-page and multipage interfaces in efficiency, contrary to previous research, as well as no differences in effectiveness, satisfaction, and workload. To conclude, we provide arguments in favor of utilizing the multipage format for the digital display of the ballot mailing instructions on electronic voting systems moving forward. These findings can reveal best practices for the design of digital instructions in emerging paper-digital systems.

Turning the attention to time-resolved EPID-images: treatment error classification with transformer multiple instance learning

Objective. The aim of this work was to develop a novel artificial intelligence-assisted in vivo dosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy. Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI) in vivo EPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence on γ-maps during dynamic treatments. To address this limitation, simulated TR γ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TR γ-maps. Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively. Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.