•The paper provides a quantitative analysis of bibliometric-database errors in the databases Scopus and Web of Science.•A large corpus of errors in the two databases are collected using an automated ...procedure.•Errors are divided in the two macro-categories: (A) pre-existing errors and (B) database mapping errors.•The analysis reveals lack of correlation between databases, regarding the error classification.•The description is supported by practical examples concerning a variety of errors in the two databases.
In the last decade, a growing number of studies focused on the qualitative/quantitative analysis of bibliometric-database errors. Most of these studies relied on the identification and (manual) examination of relatively limited samples of errors.
Using an automated procedure, we collected a large corpus of more than 10,000 errors in the two multidisciplinary databases Scopus and Web of Science (WoS), mainly including articles in the Engineering-Manufacturing field. Based on the manual examination of a portion (of about 10%) of these errors, this paper provides a preliminary analysis and classification, identifying similarities and differences between Scopus and WoS.
The analysis reveals interesting results, such as: (i) although Scopus seems more accurate than WoS, it tends to forget to index more papers, causing the loss of the relevant citations given/obtained, (ii) both databases have relatively serious problems in managing the so-called Online-First articles, and (iii) lack of correlation between databases, regarding the distribution of the errors in several error categories.
The description is supported by practical examples concerning a variety of errors in the Scopus and WoS databases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
As an approach to granular computing, the sequential three-way decision (S3WD) model has been widely studied in practical applications. In terms of improving the accuracy of the S3WD model, existing ...studies have achieved fruitful results. However, the two types of classification errors and two types of uncertain classifications caused by a probabilistic rough set model have received less consideration, which will result in a higher error classification rate (ECR) in the decision process. In this paper, from the perspective of the subdivision of granules, a new sequential three-way decision model with autonomous error correction (S3WD-AEC) is proposed to reduce the ECR. First, two types of errors correction and two types of effective classifications in the S3WD model are defined. Next, according to the process of information granulation, four subdivisions of equivalence classes are discussed in detail. Subsequently, the total ECR composed of the positive and negative regions in each granularity layer is proved to gradually decrease with the subdivision of the equivalence classes. Then, during the S3WD process, four commonly used clustering algorithms are introduced to select a portion of the equivalence classes near the boundary region for further subdivision, implementing an error correction for some misclassified objects. Finally, the experimental results show that the S3WD-AEC model has a smaller ECR compared with the S3WD model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Erroneous gesture execution is at the core of motor cognition difficulties in apraxia. While a taxonomy of errors may provide important information about the nature of the disorder, classifications ...are currently often inconsistent. This study aims to identify the error categories which distinguish apraxic from non-apraxic patients. Method. Two groups of mixed (bucco-facial and limb) and bucco-facial apraxic patients suffering from stroke were compared to non-apraxic, left and right hemisphere damaged patients in tasks tapping the ability to perform limb and bucco-facial actions. The errors were analysed and classified into 6 categories relating to content, configuration or movement, spatial or temporal parameters and unrecognisable actions. Furthermore, an anatomical investigation (VLMS) was conducted in the whole group of left hemisphere damaged patients to investigate potential correlates of the various error categories. Results. Although all the above error typologies may be observed, the most indicative of mixed apraxia is the content-related one in all the typologies of actions (transitive and intransitive), and configuration errors in transitive ones. Configuration and content errors in mouth actions seem to be typical of bucco-facial apraxia. Spatial errors are similar in both apraxic and right brain damaged, non-apraxic patients. A lesion mapping analysis of left-brain damaged patients demonstrates that all but the spatial error category are associated with the fronto-parietal network. Moreover, content errors are also associated with fronto-insular lesions and movement errors with damage to the paracentral territory (precentral and postcentral gyri). Spatial errors are often associated to ventral frontal lesions. Conclusions. Bucco-facial and mixed apraxic patients make different types of errors in different types of actions. Not all errors are equally indicative of apraxia. In addition, the various error categories are associated with at least partially different neural correlates.
•Not all gesture errors identify apraxia.•Content and configuration errors are specific to apraxia.•Spatial errors are not specific to apraxia.•Different Neural substrates underpins different gesture errors.•Gesture errors are different in apraxic and non-apraxic patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Human error associated with medical device use may lead to devastating consequences for end-users. Identifying post-market associations between instances of use error can inform human factors design ...decisions and guide regulatory action. The US Food and Drug Administration (FDA) requires medical device manufacturers, importers, and user facilities to track and report instances of adverse events. These reports are available in the Manufacturer and User Facility Experience (MAUDE) database. MAUDE exists to support post-market surveillance and to aid the discovery of adverse event-medical device associations. Each event contained in MAUDE contains an event narrative: a free-text description of the event. These event narratives are coded with a “device problem code” that describes the nature of each event that can aid in identifying trends, how,ever codes related to human factors are limited in detail. In the authors’ prior work, new use error categories for MAUDE entries were proposed tprovideides decomposition based on primary and secondary use error. In this work, these use error categories were used to structure entries based on narrative content. Topic modeling was performed for automatic extraction of narrative themes for use error MAUDE data from 2010 – 2019. Latent Dirichlet Allocation, an unsupervised generative model, was used to provide a descriptive analysis of this textual data and identify thematic topics. Notable outcomes included the categorization of narratives into six distinct topics; the first five primarily involved rule-based errors during the operation of glucose self-management devices, and the sixth involved knowledge-based errors during inpatient surgical procedures. Distinct divides between error narratives for healthcare providers and patients, as well as for different device types were observed, demonstrating an alignment with proposed use error categories. These categories can be used to monitor trends for specific medical device user segments and can inform device manufacturers of usability design requirements that must be addressed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Block programming has been suggested as a way of engaging young learners with the foundations of programming and computational thinking in a syntax-free manner. Indeed, syntax errors—which form one ...of two broad categories of errors in programming, the other one being logic errors—are omitted while block programming. However, this does not mean that errors are omitted at large in such environments. In this exploratory case study of a learning environment for early programming (Kodetu), we explored errors in block programming of middle school students (N = 123), using log files drawn from a block-based online. Analyzing 1033 failed executions, we found that errors may be driven by either learners’ knowledge and behavior, or by the learning environment design. The rate of error types was not associated with the learners’ and contextual variables examined, with the exception of task complexity (as defined by SOLO taxonomy). Our findings highlight the importance of learning from errors and of learning environment design.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
The investigation of environment impact have important role to development of a city. The application of the artificial intelligence in form of computational models can be used to analyze the data. ...One of them is rough set theory. The utilization of data clustering method, which is a part of rough set theory, could provide a meaningful contribution on the decision making process. The application of this method could come in term of selecting the attribute of environment impact. This paper examine the application of variable precision rough set model for selecting attribute of environment impact. This mean of minimum error classification based approach is applied to a survey dataset by utilizing variable precision of attributes. This paper demonstrates the utilization of variable precision rough set model to select the most important impact of regional development. Based on the experiment, The availability of public open space, social organization and culture, migration and rate of employment are selected as a dominant attributes. It can be contributed on the policy design process, in term of formulating a proper intervention for enhancing the quality of social environment.
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FFLJ, NUK, ODKLJ, UL, UM, UPUK
Most translation tasks in the entertainment industry involve multiple modes of communication, i.e. they are multimodal, not solely language-based. A translator is expected to analyse, evaluate and ...transfer each of those modes to render an accurate translation of the source text. This is especially important in films, documentaries, TV and animated shows - multimodal scripts which are being localised for various contexts. An important step in the translation process in the entertainment industry should be the identification of translation errors in the final product which should be based on a proper translation error classification. Given that available translation error classifications rely solely on linguistic modes of communication, the aim of this paper is to propose a multimodal translation error classification which would be based on the multimodality of scripts to be translated and thus provide a reliable tool for the quality check of the final translation product in the entertainment industry. In that way, translators in this industry will be alerted to recognise elements (e.g. tone of voice, facial expressions, proximity, etc.) existing in multimodal scripts where both the source and the target texts as essential parts of the scripts are multimodal products.
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BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
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