Error Handling Strategies Research Articles

Safety management within the scope of teaching practical clinical skills: framing errors for cardiopulmonary resuscitation training – a multi-arm randomized controlled equivalence trial

Introduction Cardiopulmonary resuscitation (CPR) is among the most important skills in clinical practice. Errors can happen here, just like everywhere, and potentially have severe consequences. Two common error handling strategies known from practice are Error Management (EM) and Error Avoidance (EA). However, its effects on medical performance outcomes remain unclear. This study aimed to examine the role of error framing in basic life support (BLS) training for future healthcare professionals. Materials and Methods In an equivalence trial (N = 430), first-year medical, dentistry, physiotherapy, and midwifery students underwent BLS training. In the three study arms, participants received either (1) instructions framing errors positively (EM), (2) instructions framing errors to be avoided (EA), or (3) no further instructions (Control). CPR performance was assessed using a resuscitation manikin measuring compression depth (CD) and compression rate (CR). The self-confidence ratings were assessed using a questionnaire. Equivalence margins for the outcome parameters and sample size calculations were based on previous standard BLS studies, using two-sided 95% confidence intervals to determine significance of equivalence. Results The results regarding CD revealed equivalence with a trend toward superiority of EM over EA (proportional difference 23.3%-points; 95% CI 11.4%–34.2%) and EM over control (proportional difference 23.4%-points; 95% CI 11.5%–34.2%.) and significant equivalence of EA and control (proportional difference 0.1%-points; 95% CI −11.6%–11.7%). Significant equivalence was determined for all study arms with respect to CR and self-confidence. Conclusion Our study revealed that EM was not detrimental to learners’ CPR performance. Given existing research on long-term beneficial effects of EM on patient safety, coupled with the proven equivalence of EM and EA concerning short-term performance, we argue that EM is a promising approach for future medical education purposes. Raising awareness of error framing and teaching error-handling strategies is expected to benefit ongoing safety management efforts in medical education and beyond.

What Determines Civil Servants’ Error Response? Evidence From a Conjoint Experiment

To err is human and learning from mistakes is essential for finding viable solutions to grand societal challenges through development and innovation. Yet, public organizations often exhibit a punitive zero-error culture, and public employees are stereotyped as error and risk-averse. Little is known about the underlying behavioral mechanisms that determine civil servants’ likelihood of handling errors positively, namely reporting and correcting them instead of ignoring and hiding them to avoid blame. Based on the transactional theory of stress coping, we argue that individuals’ error-handling strategies relate to both rational and emotional evaluations of error-specific and consequential contextual factors. Using a conjoint survey experiment conducted with N = 276 civil servants in Germany ( Obs. = 1,104), this study disentangles the effects of error-related, individual, and organization-cultural factors as decisive drivers of individuals’ error response. We find that error characteristics (type and harmfulness) determine error-handling behavior, which is revealed to be independent from organizational error culture and individual error orientation, providing important and novel insights for theory and practice.

Using Different Error Handling Strategies to Facilitate Older Users' Interaction With Chatbots in Learning Information and Communication Technologies.

To support older users’ accessibility and learning of the prevalent information and communication technologies (ICTs), libraries, as informal learning institutes, are committed to information literacy education activities with friendly interfaces. Chatbots using Voice User Interfaces (VUIs) with natural and intuitive interactions have received growing research and practical attention; however, older users report regular frustrations and problems in using them. To serve as a basis for the subsequent design and development of an automated dialog mechanism in senior-friendly chatbots, a between-subject user experiment was conducted with 30 older adults divided into three groups. The preliminary findings on their interactions with the voice chatbots designed with different error handling strategies were reported. Participants’ behavioral patterns, performances, and the tactics they employed in interacting with the three types of chatbots were analyzed. The results of the study showed that the use of multiple error handling strategies is beneficial for older users to achieve effectiveness and satisfaction in human-robot interactions, and facilitate their attitude toward information technology. This study contributes empirical evidence in the genuine and pragmatic field of gerontechnology and expands upon voice chatbots research by exploring conversation errors in human-robot interactions that could be of further application in designing educational and living gerontechnology.

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.

Application of Multi-Objective Optimization Problem Based on Genetic Algorithm

With the rise and rapid development of mobile communications, smart terminals and data mining technologies, we are entering the mobile Internet era. When dealing with the multi-objective problem encountered in the network, it cannot control the operation strategy of various specific applications by itself. Therefore, a fault-tolerant mechanism is needed to provide a high degree of flexibility at the application level. When the mobile database changes, only the error handling strategy needs to be modified without considering the specific code of the application. This paper mainly introduces the application research of multi-objective optimization problem based on genetic algorithm. In this paper, the application research of multi-objective optimization problems based on genetic algorithm is used, and the multi-objective evolutionary algorithm still needs to be improved in terms of convergence and distribution. Some algorithms with better convergence performance often have slightly insufficient distribution performance, but the distribution performance is relatively low. The best algorithm often needs to improve its convergence performance. Therefore, it is of great significance to study the multi-objective co-evolution genetic algorithm. The experimental results of this paper show that the application research for the multi-objective optimization problem based on genetic algorithm increases the optimization rate of multi-objective problem by 14%, and the limitation of the multi-objective optimization problem of genetic algorithm provides good indoor path planning for the application of genetic algorithm. Analysis, discussion and summary of the methods and approaches to enrich the academic research results.

Error handling in the classroom: an experimental study of teachers’ strategies to foster positive error climate

The present research investigated the possibility to foster positive classroom climate, achievement emotions, and adaptive beliefs about errors by manipulating teachers’ error handling strategies. Through a pre-post experimental design, teachers’ error handling strategies were manipulated during a fictitious lesson in the primary school context. The experimenter, who was presented as an external teacher, carried out the lesson using positive and supportive error handling strategies (experimental condition) or neutral error handling strategies (control condition). The aim was to test differences in pupils’ perceived error climate, achievement-related emotions, and error beliefs comparing the two conditions. A total of 108 fifth-grade primary school pupils took part in the research. The main results revealed that dealing with pupils’ errors using a constructive and encouraging strategy that supports them in learning from their errors (positive error handling) increased, compared with a neutral error handling, their perception of being in a trustful and supportive learning climate. This study represents the first experimental attempt in which error-related teaching strategies have been directly manipulated to identify their causal impact on primary school pupils’ perceived error climate.

Comparative analyses of error handling strategies for next-generation sequencing in precision medicine

Next-generation sequencing (NGS) offers the opportunity to sequence millions and billions of DNA sequences in a short period, leading to novel applications in personalized medicine, such as cancer diagnostics or antiviral therapy. Nevertheless, sequencing technologies have different error rates, which occur during the sequencing process. If the NGS data is used for diagnostics, these sequences with errors are typically neglected or a worst-case scenario is assumed. In the current study, we focused on the impact of ambiguous bases on therapy recommendations for Human Immunodeficiency Virus 1 (HIV-1) patients. Concretely, we analyzed the treatment recommendation with entry blockers based on prediction models for co-receptor tropism. We compared three different error handling strategies that have been used in the literature, namely (i) neglection, (ii) worst-case assumption, and (iii) deconvolution with a majority vote. We could show that for two or more ambiguous positions per sequence a reliable prediction is generally no longer possible. Moreover, also the position of ambiguity plays a crucial role. Thus, we analyzed the error probability distributions of existing sequencing technologies, e.g., Illumina MiSeq or PacBio, with respect to the aforementioned error handling strategies and it turned out that neglection outperforms the other strategies in the case where no systematic errors are present. In other cases, the deconvolution strategy with the majority vote should be preferred.

ANALISA PERAN MODERASI AUDIT TENURE DALAM HUBUNGAN ANTARA ORIENTASI KESALAHAN INDIVIDU DAN IKLIM KESALAHAN ORGANISASI TERHADAP PENANGANAN KESALAHAN PADA AUDITOR INSPEKTORAT KEMENTERIAN DI INDONESIA

The auditor’s error handling strategy as auditor behavior after detecting an auditee’s error has significant impact on repeat error finding especially unintentional errors. The purpose of this study was to investigate factor determining error management as auditor error handling strategy from organizational and individual aspect also investigate moderation role of audit tenure. 103 government auditor involve in this study as respondent collecting via convenience sampling, with linear regression and interaction moderated regression as data analysis method. Result of this study shows both organizations’ error climate and person’s individual error orientation has positive significant impact on error management as auditor error handling strategy otherwise moderation of audit tenure have no empirical evidence. Thus error management is conducting by auditor in an open climate organization with greater person’s individualerror orientation.

Drift-Compensated Robust Watermarking Algorithm for H.265/HEVC Video Stream

It has been observed in the recent literature that the drift error due to watermarking degrades the visual quality of the embedded video. The existing drift error handling strategies for recent video standards such as H.264 may not be directly applicable for upcoming high-definition video standards (such as High Efficiency Video Coding (HEVC)) due to different compression architecture. In this article, a compressed domain watermarking scheme is proposed for H.265/HEVC bit stream that can handle drift error propagation both for intra- and interprediction process. Additionally, the proposed scheme shows adequate robustness against recompression attack as well as common image processing attacks while maintaining decent visual quality. A comprehensive set of experiments has been carried out to justify the efficacy of the proposed scheme over the existing literature.

Using simulation to evaluate error detection strategies: A case study of cloud-based deployment processes

The processes for deploying systems in cloud environments can be the basis for studying strategies for detecting and correcting errors committed during complex process execution. These cloud-based processes encompass diverse activities, and entail complex interactions between cloud infrastructure, application software, tools, and humans. Many of these processes, such as those for making release decisions during continuous deployment and troubleshooting in system upgrades, are highly error-prone. Unlike the typically well-tested deployed software systems, these deployment processes are usually neither well understood nor well tested. Errors that occur during such processes may require time-consuming troubleshooting, undoing and redoing steps, and problem fixing. Consequently, these processes should ideally be guided by strategies for detecting errors that consider trade-offs between efficiency and reliability. This paper presents a framework for systematically exploring such trade-offs. To evaluate the framework and illustrate our approach, we use two representative cloud deployment processes: a continuous deployment process and a rolling upgrade process. We augment an existing process modeling language to represent these processes and model errors that may occur during process execution. We use a process-aware discrete-event simulator to evaluate strategies and empirically validate simulation results by comparing them to experiences in a production environment. Our evaluation demonstrates that our approach supports the study of how error-handling strategies affect how much time is taken for task-completion and error-fixing.

"Error Prevention, Error Management, or Both?"

All people make errors, but how people think and perform after errors is theorized to be affected by the way errors are framed. The literature differentiates between two error-handling strategies: error prevention, which focuses on removing all errors, and error management, which focuses on catching errors, learning from them, and minimizing their negative consequences. In the present study we develop and test a theoretical model in order to establish whether it is the presence or absence of error management or error prevention instructions, or their combination that influence people's cognitions and performance. Our findings show that error prevention has negative effects on cognition (fewer on-task thoughts; more negative self-related off-task thoughts) and adaptive transfer performance (trend), while error management dampens people's appraisal of errors as threats. Thus, error prevention has more negative effects, whereas error management reduces negative effects rather than strengthening positive effects. The results indicate that the way error prevention and error management act and interact is more complex than previously thought and work that incorporates full-factorial designs is necessary in order to establish what drives the effects we find.

Error treatment in students' written assignments in discourse analysis

In an attempt to highlight the need for lecturers to adopt clear strategies for treating language usage errors in written assignments, the writer of this article analysed selected students' essays to determine some of the error handling strategies that can be used at tertiary level. The suggestions made at the end of the study were based on the analysis of data collected from student's written work in Discourse Analysis. Although there is generally no consensus on how lecturers should treat students' errors in written assignments, observations in this study enabled the researcher to provide certain strategies that lecturers can adopt. Key words: Error treatment; error handling; corrective feedback, positive cognitive feedback; negative cognitive feedback; global errors; local errors; Terminable Units (T-Units) [Jnl for Language Teaching 37(2) 2003: 224-236]

Dynamic Error Handling in Service Oriented Applications

Service Oriented Computing (SOC) allows for the composition of services which communicate using unidirectional one-way or bidirectional request-response communication patterns. Most service orchestration languages proposed so far provide also primitives for error handling based on fault, termination, and compensation handlers. Our work is motivated by the difficulties encountered in programming some error handling strategies using current error handling primitives. We propose as a solution an orchestration programming style in which handlers are dynamically installed. We assess our proposal by formalizing our approach as an extension of the process calculus SOCK and by proving that our formalization satisfies some expected high-level properties.

Error handling techniques of genetic algorithms in parallel computing environment

It is easy to create parallel genetic algorithm software with master-slave type paralelization on a cluster of workstations. In a real situation the probability of errors in communication or in some of the slave processes during a long calculation is significant.In this paper we deal with different error handling strategies in master-slave type paralelization of standard GA algorithms and show results of test calculations. Our simulations are close to real applications in the sense that we examine the best achieved objective function value at a fixed wall clock time with different error handling strategies depending on the probability of errors and number of processors. Using these results we make suggestions on the selection of a good error handling method in different optimization problems.

Handling uncertainty in multimodal pervasive computing applications

Multimodal interaction can improve accessibility to pervasive computing applications. However, recognition-based interaction techniques used in multimodal interfaces (e.g. speech and gesture recognition) are still error prone. Recognition errors and misinterpretations can compromise the security, robustness, and efficiency of pervasive computing applications. In this paper we briefly review the various error handling strategies that can be found in the multimodal literature. We then discuss the new challenges arising from novel affective and context-aware applications for error correction. We show that traditional multimodal error handling strategies are ill adapted to pervasive computing applications, where the computing devices have become invisible, and when users may not be aware of their own behaviour. Finally, we present an original experimental study into users’ synchronisation of speech and pen inputs in error correction. The results of the study suggest that users are likely to modify their synchronisation patterns in the belief that it can help error resolution. This study is a first step towards a better understanding of spontaneous user strategies for error correction in multimodal interfaces and pervasive environments.

Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

In this paper, we survey the different types of error-handling strategies that have been described in the literature on recognition-based human–computer interfaces. A wide range of strategies can be found in spoken human–machine dialogues, handwriting systems, and multi-modal natural interfaces. We then propose a taxonomy for classifying error-handling strategies that has the following three dimensions: the main actor in the error-handling process (machine versus user), the purpose of the strategy (error prevention, discovery, or correction), and the use of different modalities of interaction. The requirements that different error-handling strategies have on different sets of interaction modalities are also discussed. The main aim of this work is to establish a classification that can serve as a tool for understanding how to develop more efficient and more robust multi-modal human–machine interfaces.

Building syntax-aware editors for visual languages

Syntax-aware editors are a class of editors prompting users into writing syntactically correct programs by exploiting visual language syntax. They are particularly useful in those application domains where the way a visual symbol spatially relates to others depends from the context. This does not mean constraining users to enter only correct syntactic states in a visual sentence, rather it means detecting both syntax and potential semantic errors as early as possible, and providing error feedbacks in a non-intrusive way during editing. As a consequence, error handling strategies are an essential part of this editing style. In this work, we present a strategy for the automatic generation of syntax-aware visual language editors integrating incremental subsentence parsers into freehand editors. The proposed parsing strategy has turned out to be useful in many application domains involving spatial information systems, thanks to the possibility of interactively prompting feasible visual sentence extensions, and to the presence of a non-correcting error recovery strategy. A first experimental prototype implementing the whole approach has been embedded into the VLDesk system, and empirical studies have been performed in order to verify the performance and the effectiveness of the proposed approach.

Towards Syntax-Aware Editors for Visual Languages

Editors for visual languages should provide a user-friendly environment supporting end users in the composition of visual sentences in an effective way. Syntax-aware editors are a class of editors that prompt users into writing syntactically correct programs by exploiting information on the visual language syntax. In particular, they do not constrain users to enter only correct syntactic states in a visual sentence. They merely inform the user when visual objects are syntactically correct. This means detecting both syntax and potential semantic errors as early as possible and providing feedback on such errors in a non-intrusive way during editing. As a consequence, error handling strategies are an essential part of such editing style of visual sentences.In this work, we develop a strategy for the construction of syntax-aware visual language editors by integrating incremental subsentence parsers into free-hand editors. The parser combines the LR-based techniques for parsing visual languages with the more general incremental Generalized LR parsing techniques developed for string languages. Such approach has been profitably exploited for introducing a noncorrecting error recovery strategy, and for prompting during the editing the continuation of what the user is drawing.

Exploring human error recovery strategies: Implications for spoken dialogue systems

In this study, an explorative experiment was conducted in which subjects were asked to give route directions to each other in a simulated campus (similar to Map Task). In order to elicit error handling strategies, a speech recogniser was used to corrupt the speech in one direction. This way, data could be collected on how the subjects might recover from speech recognition errors. This method for studying error handling has the advantages that the level of understanding is transparent to the analyser, and the errors that occur are similar to errors in spoken dialogue systems. The results show that when subjects face speech recognition problems, a common strategy is to ask task-related questions that confirm their hypothesis about the situation instead of signalling non-understanding. Compared to other strategies, such as asking for a repetition, this strategy leads to better understanding of subsequent utterances, whereas signalling non-understanding leads to decreased experience of task success.

Error-correction detection and response generation in a spoken dialogue system

Speech understanding errors in spoken dialogue systems can be frustrating for users and difficult to recover from in a mixed-initiative spoken dialogue system. Handling such errors requires both detecting error conditions and adjusting the response generation strategy accordingly. In this paper, we show that different response wording choices tend to be associated with different user behaviors that can impact word recognition performance in a telephone-based dialogue system. We leverage these findings in a system that integrates an error correction detection module with a modified dialogue strategy in order to drive the response generation module. In a user study, we find slight preferences for a dialogue system using this error handling strategy over a simple reprompting strategy.