Prostate brachytherapy seed reconstruction using an adaptive grouping technique

A number of opportunistic content sharing services were developed that exploit device-to-device contacts for infrastructure-less operation. All of them depend, like geo-based ad-hoc routing protocols, on mobile devices knowing their respective positions to accurately perform data replication. In this paper, we explore the impact of different types of location errors on the performance of such a service. We use a GPS error distribution for mobiles derived from real-world measurements, consider different frequencies for GPS readings, and account for only subsets of mobile devices actively using GPS. We carry out extensive simulation studies using synthetic mobility models as well as real-world traces to assess the impact of different types of errors. We find that, overall, opportunistic content sharing is quite robust provided that a sufficient number of nodes support GPS and allow the others to have a rough estimate of where they are. Whether or not the GPS position is prone to errors affects some scenarios and is almost negligible in others.

We describe a method of assessing the implications for human error on user interface design of safety-critical systems. In previous work we have proposed a taxonomy of influencing factors that contribute to error. In this article, components of the taxonomy are combined into a mathematical and causal model for error, represented as a Bayesian Belief Net (BBN). The BBN quantifies error influences arising from user knowledge, ability, and the task environ-ment, combined with factors describing the complexity of user action and user interface quality. The BBN model predicts probabilities of different types of errorslips and mistakes for each component action of a task involving user-system interaction. We propose an Impact Analysis Method that involves running test scenarios against this causal model of error in order to determine user interactions that are prone to different types of error. Applying the proposed method will enable the designer to determine the combinations of influencing factors and their interactions that are most likely to influence human error. Finally we show how such scenario-based causal analysis can be useful as a means of focusing on relevant guidelines for safe user interface design. The proposed method is demonstrated through a case study of an operator performing a task using the control system for a laser spectrophotometer.We describe a method of assessing the implications for human error on user interface design of safety-critical systems. In previous work we have proposed a taxonomy of influencing factors that contribute to error. In this article, components of the taxonomy are combined into a mathematical and causal model for error, represented as a Bayesian Belief Net (BBN). The BBN quantifies error influences arising from user knowledge, ability, and the task environ-ment, combined with factors describing the complexity of user action and user interface quality. The BBN model predicts probabilities of different types of errorslip for each component action of a task involving user-system interaction. We propose an Impact Analysis Method that involves running test scenarios against this causal model of error in order to determine user interactions that are prone to different types of error. Applying the proposed method will enable the designer to determine the combinations of influencing factors and their interactions that are most likely to influence human error. Finally we show how such scenario-based causal analysis can be useful as a means of focusing on relevant guidelines for safe user interface design. The proposed method is demonstrated through a case study of an operator performing a task using the control system for a laser spectrophotometer.

The National Training Programme for laparoscopic colorectal surgery in England was implemented to ensure training was supervised, structured, safe and effective. Delegates were required to pass a competency assessment (sign-off) before undertaking independent practice. This study described the types of errors identified and associated these errors with competency to progress to independent laparoscopic colorectal practice. All sign-off submissions from the start of the process in January 2008 until July 2013 were included. Content analysis was used to categorise errors. Bayes factor (BF) was used to measure the impact of individual error on assessment outcome. A smaller BF indicates that an error has stronger associations with unsuccessful assessments. Bayesian network was employed to graphically represent the reasoning process whereby the chance of successful assessment diminished with the identification of each error. Quality of the error feedback was measured by the area under the ROC curve which linked the predictions from the Bayesian model to the expert verdict. Among 370 assessments analysed, 240 passed and 130 failed. On average, 2.5 different types of error were identified in each assessment. Cases that were more likely to fail had three or more different types of error (χ(2) = 72, p < 0.0001) and demonstrated poorer technical skills (CAT score <2.7, χ(2) = 164, p < 0.0001). Case complexity or right- versus left-sided resection did not have a significant impact. Errors associated with dissection (BF = 0.18), anastomosis (BF = 0.23) and oncological quality (BF = 0.19) were critical determinants of surgical competence, each reducing the odds of pass by at least fourfold. The area under the ROC curve was 0.84. Errors associated with dissection, anastomosis and oncological quality were critical determinants of surgical competency. The detailed error analysis reported in this study can guide the design of future surgical education and clinical training programmes.

Self-explanations (SE) are an effective method to promote learning because they can help students identify gaps and inconsistencies in their knowledge and revise their faulty mental models. Given this potential, it is beneficial for intelligent tutoring systems (ITS) to promote SEs and adaptively respond based on SE quality. We developed and evaluated classification models using combinations of SE content (e.g., inverse weighted word-overlap) and contextual cues (e.g., SE response time, topic being discussed). SEs were coded based on correctness and presence of different types of errors. We achieved some success at classifying SE quality using SE content and context. For correct vs. incorrect discrimination, context-based features were more effective, whereas content-based features were more effective when classifying different types of errors. Implications for automatic assessment of learner SEs by ITSs are discussed.

The relationship between errors and size in knowledge-based systems

PurposeThe study pursues two goals: first, as a replication study, the purpose of this paper is to test a model of learning from errors in the domain of insurance industry. Second, to increase insights in learning from errors, the authors focussed on different types of errors.Design/methodology/approachThe authors conducted a cross-sectional survey in the insurance industry (N=206). The authors used structural equation modelling and path modelling to analyse the data. To be able to analyse different types of errors, the authors used Critical Incident Technique and asked participants to describe error situations.FindingsFindings from the study are that the model of learning from errors could partly be replicated. The results indicate that a non-punitive orientation towards errors is an important factor to reduce the tendency of insurance agents to cover up errors when knowledge and rule-based errors happen. In situations of slips and lapses error strain has a negative influence on trust and non-punitive orientation which in turn both reduce the tendency to cover up errors.Research limitations/implicationsLimitation is the small sample size. By using Critical Incidents Technique, the authors were able to analyse authentic error situations. Implications of the results concern the importance of error-friendly climate in organisations.Originality/valueReplication studies are important to generalise results to different domains. To increase the insight in learning from errors, the authors analysed influencing factors with regard to different types of errors.

In this paper, we propose a framework to evaluate the impact on power system dynamic performance of different types of errors in the measurements utilized by the automatic generation control system. To address the random nature of these errors, stochastic system analysis methods are utilized to evaluate the statistics of system state variables. By examining the convergence properties of these statistics, errors that cause instability are identified. A reduced-order model, obtained by using singular perturbation arguments, is also formulated; this model enables us to provide analytical expressions for capturing the impact of the errors. The proposed method is illustrated and verified through several case studies with different types of errors on a simplified New England/New York system model.

Performance on the Bender Visual-Motor Gestalt test has been repeatedly shown to be sensitive to the effects of brain lesions. However, brain damaged and control populations typically overlap greatly in their error scores. A new Bender scoring system is presented with differential weighting for errors of greater or lesser severity, which permits a profile analysis of different types of error as well. Preliminary data from a validation study are included. There are several scoring systems for the Bender Visual-Motor Gestalt Test (Bender, 1938) including those developed by Hain (1964), Koppitz (1964) and Pascal and Suttell (1951) as well as that which was developed by Bender (1938). Each of these systems has been an attempt to objectify the differences in performance often seen between groups of people with different diagnoses. Developmental differences also have been shown on the Bender (Koppitz, 1964). Several studies have demonstrated that, based upon total error scores on the Bender, there are significant differences in performance between brain-damaged and other non brain-damaged groups (Hain, 1964; Kramer and Fenwick, 1966; Tymchuk, 1974). The rationale for the use of the Bender for individual classification derives from the fact that people with known brain lesions demonstrate disordered performances on tests such as the Bender including the Benton Visual Retention Test (Benton, 1963), the Graham-Kendall Memory for Designs Test (1960) and the Halstead-Wepman Aphasia Screening Test (1949). Scores on the Bender, however, cannot be used to classify subjects on an individual basis into the brain-damaged or non brain-damaged groups because of the great overlap in error scores (Cronbach, 1970). Despite this difficulty in individual classification, the Bender is a useful tool for research purposes in showing change in performance after training and has been used to predict learning disabilities (Silver, 1973) so that an objective method for scoring is needed. The author has developed a scoring system which goes beyond the other systems in that the errors are defined more finely with different weights given depending on the extent of the error. This system also has categories for the different types of errors which allows for a profile analysis of errors. The categories of errors include rotations, integration, distortion, size, perseveration and others (including collisions, incoordination, erasures, nonerasures or doubled or blackened lines). Each of the other scoring systems for the Bender include some or all of these types of errors, but do not offer scores for the individual types. As an example of the fineness of the error categories, for figure A, Rotation errors of the entire figure of 45, 90 and 180 degrees are given scores of 1, 2, or 3 respectively. For Integration errors when there is one-eighth inch or more overlap or separation for Figure A, this is scored 1 while super-imposition of the figures or where one figure is missing is scored 2. Various degrees of distortions are also scored 1, 2, or 3. This system also considers time scores for the individual errors as well as a total time score although in the present study time scores were unavailable. This system is an extension of several others including the Koppitz (1964) and is not meant to replace those systems. The purpose of the system is to be used where delineation of finer error categories may seem appropriate.

The specificity of the Wisconsin Card Sorting Test (WCST) for assessing frontal lobe pathology remains controversial, although lesion and cerebral blood flow studies continue to suggest a role for the dorsolateral prefrontal cortex in WCST performance. Inconsistencies might derive from the extended use of various WCST scores as equivalent indicators of frontal pathology. In this study, event-related potentials (ERPs) were recorded from 32 normal subjects who committed perseverative and non-perseverative errors. Both types of WCST errors evoked anomalous but distinct ERP patterns over frontal lobe regions. Perseverative errors were also associated with a dysfunctional extrastriate response to stimulation. This evidence suggests that perseverative and non-perseverative errors result from disruptions in two different prefrontal neural networks engaged during card sorting.

Discourse markers are words and expressions (such as: firstly, then, for example, because, as a result, likewise, in comparison, in contrast) that explicitly state the relational structure of the information in the text, i.e. signalling a sequential relationship between the current message and the previous discourse. Using these markers improves the cohesion and coherence of texts, facilitating reading comprehension. Although often included in tools that support the rhetoric structuring of texts, discourse markers have hardly been explored in writing support tools for learners of a second language. However, learners of a second language, including those at advanced levels, have trouble producing these lexical items, frequently replacing them with items from their native language or with literal translations of items in their own language, which often do not result in proper lexical items in the second language. In addition, students learn a single marker per function and use it repeatedly, producing monotonous texts. With the aim of contributing to reducing these difficulties, this paper presents a lexicon that will be used to support the task of automatically detecting and correcting discourse marker errors. Several heuristics have been evaluated to generate different types of errors. Automatic translation methods were used to semi-automatically compile the lexicon used in these heuristics. Similarity measures were also combined with these heuristics to correct discourse marker errors. The evaluated methods proved to be suitable for the task of identifying some types of discourse marker errors and can potentially identify many others, as long as new lexical inputs are incorporated into them.

ABSTRACTObjectives: This cross-sectional experiment examined the influence of aging on cross-cultural differences in memory errors. Previous research revealed that Americans committed more categorical memory errors than Turks; we tested whether the cognitive constraints associated with aging impacted the pattern of memory errors across cultures. Furthermore, older adults are vulnerable to memory errors for semantically-related information, and we assessed whether this tendency occurs across cultures.Methods: Younger and older adults from the US and Turkey studied word pairs, with some pairs sharing a categorical relationship and some unrelated. Participants then completed a cued recall test, generating the word that was paired with the first. These responses were scored for correct responses or different types of errors, including categorical and semantic.Results: The tendency for Americans to commit more categorical memory errors emerged for both younger and older adults. In addition, older adults across cultures committed more memory errors, and these were for semantically-related information (including both categorical and other types of semantic errors).Conclusion: Heightened vulnerability to memory errors with age extends across cultural groups, and Americans’ proneness to commit categorical memory errors occurs across ages. The findings indicate some robustness in the ways that age and culture influence memory errors.

Grammar errors are a natural part of everyday written communication. They are not a uniform group, but vary from morphological errors to ungrammatical word order and involve different types of word classes. In this study, we examine whether some types of naturally occurring errors attract more attention than others during reading, measured by detection rates. Data from 211 Danish high school students were included in the analysis. They each read texts containing different types of errors: syntactic errors (verb-third word order), morphological agreement errors (verb conjugations; gender mismatches in NPs) and orthographic errors. Participants were asked to underline all errors they detected while reading for comprehension. We examined whether there was a link between the type of errors that participants did not detect, the type of errors which they produce themselves (as measured in a subsequent grammar quiz), and the type of errors that are typical of high school students in general (based on error rates in a corpus). If an error is infrequent in production, it may cause a larger surprisal effect and be more attended to. For the three subtypes of grammar errors (V3 word order, verb errors, NP errors), corpus error rates predicted detection rates for most conditions. Yet, frequency was not the only possible explanation, as phonological similarity to the correct form is entangled with error frequency. Explicit grammatical awareness also played a role. The more correct answers participants had in the grammar tasks in the quiz, the more errors they detected. Finally, we found that the more annoyed with language errors participants reported to be, the more errors they detected. Our study did not measure eye movements, but the differences in error detection patterns point to shortcomings of existing eye-tracking models. Understanding the factors that govern attention and reaction to everyday grammar errors is crucial to developing robust eye-tracking processing models which can accommodate non-standard variation. Based on our results, we give our recommendations for current and future processing models.

Analysis of age differences in types of errors on the Raven's Advanced Progressive Matrices

Selective impairment for reading numbers and number words: a single case study

In the era of big data, data may come from multiple sources, known as multi-view data. Multi-view clustering aims at generating better clusters by exploiting complementary and consistent information from multiple views rather than relying on the individual view. Due to inevitable system errors caused by data-captured sensors or others, the data in each view may be erroneous. Various types of errors behave differently and inconsistently in each view. More precisely, error could exhibit as noise and corruptions in reality. Unfortunately, none of the existing multi-view clustering approaches handle all of these error types. Consequently, their clustering performance is dramatically degraded. In this paper, we propose a novel Markov chain method for Error-Robust Multi-View Clustering (EMVC). By decomposing each view into a shared transition probability matrix and error matrix and imposing structured sparsity-inducing norms on error matrices, we characterize and handle typical types of errors explicitly. To solve the challenging optimization problem, we propose a new efficient algorithm based on Augmented Lagrangian Multipliers and prove its convergence rigorously. Experimental results on various synthetic and real-world datasets show the superiority of the proposed EMVC method over the baseline methods and its robustness against different types of errors.