Error Learning Research Articles

CEO anger: a catalyst for error recognition and learning

PurposeThis paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.Design/methodology/approachA double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.FindingsThe findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.Originality/valueThis study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

Neuroimaging Signatures of Metacognitive Improvement in Sensorimotor Timing.

Error monitoring is an essential human ability underlying learning and metacognition. In the time domain, humans possess a remarkable ability to learn and adapt to temporal intervals, yet the neural mechanisms underlying this are not clear. Recently, we demonstrated that humans improve sensorimotor time estimates when given the chance to incorporate previous trial feedback ( Bader and Wiener, 2021), suggesting that humans are metacognitively aware of their own timing errors. To test the neural basis of this metacognitive ability, human participants of both sexes underwent fMRI while they performed a visual temporal reproduction task with randomized supra-second intervals (1.5-6 s). Crucially, each trial was repeated following feedback, allowing a "re-do" to learn from the successes or errors in the initial trial. Behaviorally, we replicated our previous finding of improved re-do trial performance despite temporally uninformative (i.e., early or late) feedback. For neuroimaging, we observed a dissociation between estimating and reproducing time intervals. Estimation engaged the default mode network (DMN), including the superior frontal gyri, precuneus, and posterior cingulate, whereas reproduction activated regions associated traditionally with the "timing network" (TN), including the supplementary motor area (SMA), precentral gyrus, and right supramarginal gyrus. Notably, greater and more extensive DMN involvement was observed in re-do trials, whereas for the TN, it was more constrained. Task-based connectivity between these networks demonstrated higher inter-network correlation primarily when estimating initial trials, while re-do trial communication was higher during reproduction. Overall, these results suggest that the DMN and TN jointly mediate subjective self-awareness to improve timing performance.

Neural correlates of prediction error in patients with schizophrenia: evidence from an fMRI meta-analysis.

Abnormal processes of learning from prediction errors, i.e. the discrepancies between expectations and outcomes, are thought to underlie motivational impairments in schizophrenia. Although dopaminergic abnormalities in the mesocorticolimbic reward circuit have been found in patients with schizophrenia, the pathway through which prediction error signals are processed in schizophrenia has yet to be elucidated. To determine the neural correlates of prediction error processing in schizophrenia, we conducted a meta-analysis of whole-brain neuroimaging studies that investigated prediction error signal processing in schizophrenia patients and healthy controls. A total of 14 studies (324 schizophrenia patients and 348 healthy controls) using the reinforcement learning paradigm were included. Our meta-analysis showed that, relative to healthy controls, schizophrenia patients showed increased activity in the precentral gyrus and middle frontal gyrus and reduced activity in the mesolimbic circuit, including the striatum, thalamus, amygdala, hippocampus, anterior cingulate cortex, insula, superior temporal gyrus, and cerebellum, when processing prediction errors. We also found hyperactivity in frontal areas and hypoactivity in mesolimbic areas when encoding prediction error signals in schizophrenia patients, potentially indicating abnormal dopamine signaling of reward prediction error and suggesting failure to represent the value of alternative responses during prediction error learning and decision making.

IIM: an information interaction mechanism for aspect-based sentiment analysis

ABSTRACT Term polarity co-extraction is an aspect-based sentiment analysis task, which has been widely used in the fields of user opinions extraction. It consists of two subtasks: aspect term extraction and aspect sentiment classification. Most existing studies solve aforesaid subtasks as independent tasks or simply unify the two subtasks without making full use of the relationship between tasks to mine the interaction of text information, which leads to low performance for practical applications. Meanwhile, the learning framework for these studies has a label drift phenomenon (LDP) in the process of predictive learning, increasing the learning error rate. To address the above problems, this study unifies subtasks and proposes a Unified framework based on the information interaction mechanism framework, called IIM. Specifically, we design an Information Interaction Channel (IIC) to construct closer semantic features to extract preliminary term-polarity unified labels from the perspective of basic semantics. For label inconsistency between aspect terms, a Position-aware Module (SAM) is proposed to alleviate the Label Drift Phenomenon (LDP). Moreover, we introduce a syntax-attention graph neural network (Syn-AttGCN) to model the syntactic structure of text and strengthen the emotional connection between aspect terms. The experimental results show that IIM outperforms most baselines. Meanwhile, the SAM module has a certain slowing effect on LDP.

CONSTRUCTIVISM LEARNING THEORY IN EDUCATION: CHARACTERISTICS, STEPS AND LEARNING MODELS

Education seeks to build the best quality human resources for the future.One way to make this happen is by knowing the various forms of learning theories that function so that the process of errors in learning can be minimized. Apart from that, to increase students' enthusiasm for learning, teachers must innovate, including byusing a constructivist approach to learning. So this research aims to describe the Constructivist Learning Theory in Education. The method used is a literature study with a qualitative descriptive approach, collecting data from primary and secondary sources, then analyzing the content of relevant discussions and drawing conclusions. The results of the research show that constructivist learning theory is an approach to the individual learning process in which it has principles and characteristics to answer various relevant problems, adapts to the current curriculum, focuses on student activity, and participates in assessing students' opinions when they express their opinions. The steps must include apperception, exploration, consolidation, attitude formation, and assessment using a format. Meanwhile, several learning models that are currently running are based on constructivism, including Problem-Based Learning, Authentic Learning, Inquiry-Based Learning, and Cooperative Learning.Keywords: Learning Theory, Constructivism, Education, Learning Models

Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

A comparison of stimulus fading and stimulus shaping on perceptual category learning.

Errorless learning strategies such as stimulus fading and stimulus shaping are commonly used to teach complex skills like categorisation and concept formation. Despite widespread use, very few studies have explored the comparative effectiveness of these procedures in well-controlled analyses. The vast majority of existing studies have been undertaken with clinical populations and have involved small numbers of participants (e.g., Single-case designs). The present study sought to compare stimulus fading, stimulus shaping, and trial-and-error learning in a perceptual categorisation task. In Experiment 1, we found robust benefits of stimulus shaping when compared to stimulus fading or trial-and-error learning on measures of initial acquisition of discrimination and one measure of stimulus generalisation. These findings were replicated in a second experiment in which the dimension of fading/shaping was changed from a modification of the comparison stimuli (S-) to a modification of the target stimulus (S +). We discuss the implications of our findings for the selection of errorless learning strategies in clinical settings.

ANALISIS KESALAHAN PENGUCAPAN DALAM PEMBELAJARAN BAHASA INGGRIS DI KELAS VII SMP PEMETUNG (PENDEKATAN LINGUISTIK TERAPAN)

This research aims to analyze pronunciation errors in English learning in class VII Pemetung Middle School using an applied linguistics approach. This research used an observation method on class VII students at Pemetung Middle School. Observations were carried out to identify pronunciation errors that often occur. Data was collected from direct observation and then analyzed using applied linguistics concepts. The results of the study showed that there were various pronunciation errors made by class VII students at Pemetung Middle School. Some of the most common errors involve errors in pronunciation of consonants, vowels, as well as intonation. These errors are often caused by the influence of a student's native language or the influence of social media influencing the way they speak English. An applied linguistics approach is used to design learning strategies aimed at correcting these pronunciation errors. By applying this approach, students are taught how to use English phonological rules and are given special exercises to improve their pronunciation. This research contributes to the understanding of pronunciation errors in English learning at Pemetung Middle School and provides concrete solutions to improve students' ability to speak English correctly. In addition, this research also provides a more in-depth view of the application of the applied linguistics approach in teaching English in secondary education environments.

Anchor Ball Regression Model for large-scale 3D skull landmark detection

Recent deep learning models have exhibited impressive performance in the area of 3D skull landmark detection, but most of them aimed to detect a fixed number of landmarks. This paper focuses on automatically detecting an arbitrary number of landmarks on CT volumes, which meets the real clinical needs. To achieve robust performance for detecting arbitrary molar landmarks, we propose a novel 3D landmark detection model named Anchor Ball Regression Model (ABRM), which combines landmark detection and landmark classification losses for network training. For landmark detection, a novel landmark regression loss is proposed by predicting offsets to anchor balls instead of directly predicting landmarks. For landmark classification, an online hard negative mining loss is used to reduce absent landmarks’ learning errors, and a small regularization constraint loss is performed for voxels outside the anchor balls. The network backbone of ABRM is obtained by manually pruning popular 3D-CNNs. We also present an available large-scale benchmark dataset in this paper, which, to the best of our knowledge, is the largest dataset for 3D skull landmark detection. The dataset comprises of 658 CT volumes, with 14 landmarks labeled by two junior and one senior doctors. The ABRM presents a good robust performance and outperforms other models on this dataset. The codes and dataset are accessible at https://github.com/ithet1007/mmld_code.

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

At present, wind power is one of the most promising clean energy sources. Due to its high variability, accurate wind speed forecasting is an essential part of the wind power industry. This paper proposes a comprehensive forecasting system consisting of three parts: Firstly, the novel SC-EWT wind speed signal reconstruction system proposed in this article can effectively extract information from wind speed signals. Secondly, the QBGA-GRU model is used for signal reconstruction prediction, where QBGA is an improved genetic algorithm proposed in this article that can improve prediction accuracy. Finally, the IPSO-BLS algorithm is used to quickly analyze and predict prediction errors and further correct the predicted results. The system can provide users with adaptive signal decomposition, high-precision deep learning prediction and error analysis functions to achieve efficient and fast short-term wind speed forecasting, which can be used in the fields of wind power integration deployment and wind turbine early warning. The experiments show that the proposed prediction strategy has good predictive ability for wind speed samples in two regions, and its error is smaller than that of commonly used intelligent algorithms. Compared with other models, the proposed comprehensive prediction strategy has better predictive performance and smaller prediction errors than other combination models.

A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction

Wind power prediction is crucial for successfully integrating large-scale wind energy with the grid and achieving a carbon-neutral energy mix. However, previous studies encountered challenges to noise reduction, information extraction, error handling, and uncertainty estimation. To address these limitations, this study proposed a novel wind power forecasting system, systematically enhancing forecasting capability by refining the raw time series using horizontal denoising and vertical granulation methods, utilizing a bidirectional deep learning model for capturing nonlinear features and optimizing its core hyperparameters with a newly developed multi-objective enhanced version of metaheuristic algorithm to achieve Pareto optimal solutions, employing the traditional statistical approach for residual linear feature extraction from error series and effective correction, and integrating quantile regression for interval prediction. The predictive performance was evaluated based on wind power, speed, and direction data from two different countries, the system yielded correlation coefficients of 0.9796, 0.9943, 0.9795, and 0.9780 in spring, summer, autumn and winter for China, and 0.9950 for Turkey, demonstrating its pronounced advantages over comparative models. Overall, the proposed system contributes to the advancement of wind-power prediction theory and holds substantial potential for practical applications.

A review on the applicability of machine learning techniques to the metamodeling of energy systems

The use of physics-based models for the development and optimization of energy systems is popular due to their versatility. However, their inherent complexity often makes these simulations computationally prohibitive, despite the current computational power available nowadays. This problem is aggravated when considering renewable energy systems, which are subjected to intermittent weather conditions and must be operated in connection with a grid of variable demand. In that sense, the use of metamodels has shown immense potential in the simulation, analysis, and optimization of energy systems. Therefore, this review presents a comprehensive analysis of the extensive application of metamodeling techniques in energy systems. After conducting a careful search using the Scopus database while targeting a well-defined group of keywords, 474 articles published from 2013 to 2023 were obtained in the present literature review. With a rigorous filtering and screening process, the first pool of articles was reduced to 126. The resulting articles were then separated in two main groups, reviews and research articles. Each of these groups was further organized in terms of the energy system studied, as well as the applicability, machine learning technique and error indicator. The results indicate a steady and significant growth in the number of publications associated with energy systems and different types of metamodeling techniques. The reasons are arguably twofold, reduction of computational time provided by the metamodels and their good accuracy. Nevertheless, and as reported by other studies, the review also identified the need to develop a standardized method for evaluating the error analysis of these models and a comparison between their actual efficiency, which includes accuracy and time. Furthermore, no clear relation was found between specific types of metamodels and specific energy systems.

Physical Layer-Aware Digital-Analog Co-Design for Photonic Convolution Neural Network

Photonic convolution neural network (CNN) has shown tremendous capability to provide Tera-level operations/sec (OPS) for streaming object detection with ultra-low energy consumption. However, due to the nature of analog photonic computing which is constrained by accumulated noise from various sources, its physical operating precision is significantly lower than that of electronic artificial intelligence (AI) accelerators powered by tensor processing unit (TPU) and graphic processing unit (GPU). In this paper, we propose a robust digital-analog co-designed photonic CNN using error learning and Kirsch edge enhancement. Experimental results show that, nearly 96.1% recognition accuracy, compared with 34.2% in baseline photonic CNN, can be achieved at global 2-bit precision through physical error learning in dense layer and edge enhancement by photonic Kirsch operator. The proposed photonic CNN architecture is promising to provide 10 Tera-OPS scale computing speed deployed by the state of art PAM-4 optical modules with high energy efficiency.

Output-feedback optimized consensus for directed graph multi-agent systems based on reinforcement learning and subsystem error derivatives

Output-feedback optimized consensus for directed graph multi-agent systems based on reinforcement learning and subsystem error derivatives

Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies potentially destabilizing the grid and potentially causing energy shortages or excesses. This study develops a data-driven approach to forecast wind power from 30 min to 12 h ahead using historical wind power data collected by the Supervisory Control and Data Acquisition (SCADA) system from one wind turbine, the Enercon/E92 2350 kW model, installed at Casa Nova, Bahia, Brazil. Those data were measured from January 2020 to April 2021. Time orientation was embedded using sine/cosine or cyclic encoding, deriving 16 normalized features that encapsulate crucial daily and seasonal trends. The research explores two distinct strategies: error prediction and error correction, both employing a sequential model where initial forecasts via k-Nearest Neighbors (KNN) are rectified by the Extra Trees Regressor. Their primary divergence is the second model’s target variable. Evaluations revealed both strategies outperforming the standalone KNN, with error correction excelling in short-term predictions and error prediction showing potential for extended forecasts. This exploration underscores the imperative importance of methodology selection in wind power forecasting.

Flow field distribution and structural strength performance evaluation of fixed offshore wind turbine based on digital twin technology

Due to the adverse impact of the offshore environment, the cost of on-site monitoring, operation and maintenance of offshore wind turbines is greatly increased. Here, a digital twin (DT) method based on digital model and computational fluid dynamics (CFD) simulation database is proposed, which is used to rapidly predict and synchronously display the distribution of the wake field, structural deformation and stress of fixed OWTs. First, a large number of data have been calculated by using CFD method. Second, the three-dimensional finite element model is reduced to a digital model by the proper orthogonal decomposition method, all data is stored in a multi-source heterogeneous database. Furthermore, the anisotropic inverse distance weighted interpolation and particle swarm optimization algorithm methods are used to obtain uncalculated data results and supplement database. Then the Bayesian regularization-back propagation neural network method is developed to correct the data with large errors. The results show that the learning error of the flow field and structural strength is less than 10% and 4% compared with the CFD method, respectively. This study could provide a rapid monitoring engineering reference for the safety assessment of turbine flow field distribution and structural strength.

Joint optimization strategy of task offloading to mobile edge computing

Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption.

The efficacy of written corrective feedback: Searching for the best predictors

AbstractThe efficacy of written corrective feedback (WCF) is affected by a variety of variables. To maximize the effect of WCF, it is necessary to find out what variables are more predictive of learning. The present study explored the best predictors of learning among six facets of WCF: explicitness, intensiveness, error type, complexity, languaging, and mode of languaging. Seventy‐five first‐year university students wrote an essay and received WCF on their errors. Then the essays with WCF were returned to learners, who were assigned to five conditions: languaging about WCF in written form prompted, languaging about WCF in written form unprompted, languaging in oral form prompted, languaging in oral form unprompted, and no languaging. The languaging groups languaged about the WCF they received and the non‐languaging group only looked through the corrections on their essays. One day after the languaging activity, all groups took an immediate test, and 4 weeks later, a delayed test. It was found that learning in the immediate test was best predicted by error type, complexity, and languaging, and learning in the delayed test was best predicted by error type and mode of languaging. Based on these findings, the pedagogical implications of the study are discussed.

Strategic asset allocation with distorted beliefs

We propose an approximate solution for the asset allocation model in which the dynamics of the investment opportunity set are subject to regime shifts and the regime updating/predicting procedures are contaminated by psychological biases, including optimism, pessimism, conservatism, representativeness, momentum, and reversal. We employ monthly U.S. data to study the model and find that the optimistic investor earns higher returns and outperforms the rational (Bayesian) investor. We find that the outperformance of optimism results from the return predictability and that learning errors of optimism are offset by the information contained in the data sample. Psychological biases that can generate a large variability in beliefs or state uncertainty induce the investor to tilt toward cash and bonds.

Analysis of Teacher Behavior Errors in Learning

This study aims to analyze teacher behavior errors in learning at KB Mekar Sari, Ngargoyoso District, Karanganyar Regency . This research is a qualitative descriptive research with a case study type. The object of this research is teacher behavior in learning. Collecting data on teacher behavior in learning is obtained through observation . Data validation technique with technical triangulation . The data analysis technique used is the content analysis model . The results of the study show that there are errors in teacher behavior in learning , namely, the teacher is sometimes angry, the teacher often orders and forbids (3M) . Some teacher behaviors that should be carried out can be seen in the TBC ( Teacher Behavior Continuum ) stage or the continuum of teacher behavior in learning.