Task Model Research Articles

Computational processes of simultaneous learning of stochasticity and volatility in humans

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Verification and Validation of Cognitive Workload Models for Adaptive Automation Tasks

Technological advances that seek to address future operational challenges abound. While advanced capabilities are being developed, there is an important space for human design considerations, including cognitive workload. One proposed solution to improve cognitive workload management is adaptive automation (AA). This research used a novel, model-based approach to assess the impacts of AA on cognitive workload. This assessment modeled the tasks in NASA’s Multi-attribute Task Battery-II (MATB-II) using the Improved Performance Research Integration Tool (IMPRINT). The effort sought to investigate the relationship of cognitive workload, situation awareness, and performance through three human-in-the-loop studies with 120 participants using MATB-II. The research also attempted to validate cognitive workload models from IMPRINT. The IMPRINT models were representative of the MATB tasks with statistically significant differences between workload conditions, which mirrored the models’ predictions. The results demonstrated that AA system task models can be developed using IMPRINT to provide design recommendations.

Development and Validation of a Laparoscopic Sacrocolpopexy Training Model.

Proper training is necessary to develop the highly specialized skills required to safely perform laparoscopic sacrocolpopexy. Currently, there is no validated training model for laparoscopic sacrocolpopexy that includes dissection of the presacral space, both vaginal and presacral mesh attachments, and peritoneal closure. This study aimed to create a procedure specific hierarchical task analysis for laparoscopic sacrocolpopexy and then develop and validate a corresponding laparoscopic sacrocolpopexy pelvic training model for the simulation environment. This was an observational simulation study that was divided into 5 phases: (1) development of hierarchical task analysis, (2) model construction, (3) participant recruitment and simulation testing, (4) reliability and validity testing, and (5) creation of a standard passing performance measure. Construct, face, and content validity were established for this model. According to the participating experts, the model was able to replicate the steps of presacral dissection, anterior vaginal and sacral mesh attachment, and peritoneal closure. Thirteen trainees and 5 experts completed the simulation, and all "agreed" or "strongly agreed" that the model seemed useful for improving suturing technique and learning the procedure. Additionally, a passing performance measure was determined through contrasting groups methodology. We developed a novel, reusable, and validated training model that can be utilized as a training resource for the many critical skills necessary to safely and efficiently perform laparoscopic sacrocolpopexy.

Development and expert inspections of the user interface for a primary care decision support system

BackgroundGeneral practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly. ObjectivesThe objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability. MethodsThe user interface prototype was iteratively developed based on previous research, design principles, and usability guidelines. During the development phase, three usability inspections were carried out by all experts at four-week intervals as heuristic walkthrough. Each inspection consisted of two parts: 1) Task-based inspection 2) Free exploration and evaluation based on usability heuristics. Five domain experts assessed the current status of development.The tasks in the inspections were based on the task model derived in the requirements analysis: perform data entry, review and discuss results, schedule further diagnostics, refer to specialists and close case. ResultsAs a result of this iterative development, a high-fidelity, clickable user interface prototype was created that is able to fulfil all six tasks of our task model. The usability inspections identified a total of 196 usability issues (for all 3 inspections; Part 1: 90 issues, Part 2: 106 issues), ranging in severity from minor to severe. These served the continuous adjustment and improvement of the prototype. All main tasks were completed successfully despite these problems. ConclusionUsability inspections through heuristic walkthroughs can support and optimise the development of a user-centred decision support system in order to ensure its suitability for performing relevant tasks.

Exploring the relationship between cognition and mental health in intersex participants in the UK Biobank study

Objective: The physical and mental health of intersex individuals is woefully understudied. A recent survey of intersex individuals found high rates of self-reported cognitive issues such as difficulty remembering and concentrating as well as high rates of mental health issues, such as depression and anxiety. Method: The current study explores whether cognitive differences are observed between 353 intersex and over 400,000 non-intersex people using a latent model of cognitive tasks derived from measures in the UK (United Kingdom) Biobank study. Results: There were no differences in intelligence between intersex people and non-intersex people. We found significantly lower executive function and processing speed in intersex individuals versus non-intersex individuals. However, after accounting for mental health differences via regression and case-control matching, there were no significant differences in executive function or processing speed between intersex individuals and non-intersex individuals. Conclusion: Mental health differences between intersex and non-intersex individuals may account for differences in cognitive factor scores.

On the degree of parallelism for parallel real-time tasks

The degree of parallelism, which measures how a task can execute concurrently, is an important characterization in scheduling. This paper studies the degree of parallelism in the domain of real-time scheduling of parallel tasks, including the DAG task model and the conditional DAG task model. The definition of the degree of parallelism for DAG tasks is clarified; the definition and computing algorithm of the degree of parallelism for conditional DAG tasks are proposed. By leveraging the degree of parallelism, new response time bounds are derived and simple but effective real-time scheduling approaches are presented. This research is the first work to study the degree of parallelism for conditional DAG tasks and explore its benefits in real-time scheduling. Experimental results demonstrate that the proposed scheduling approaches significantly outperform existing state-of-the-art methods.

Silp_nlp at LLMs4OL 2024 Tasks A, B, and C: Ontology Learning through Prompts with LLMs

Our team, silp_nlp, participated in the LLMs4OL Challenge at ISWC 2024, engaging in all three tasks focused on ontology generation. The tasks include predicting the type of a given term, extracting a hierarchical taxonomy between two terms, and extracting non-taxonomy relations between two terms. To accomplish these tasks, we used machine learning models such as random forest, logistic regression and generative models for the first task and generative models such as llama-3-8b-instruct, mistral 8*7b and GPT-4o-mini for the second and third tasks. Our results showed that generative models performed better for certain domains, such as subtasks A6 and B2. However, for other domains, the prompt-based technique failed to generate promising results. Our team achieved first place in six subtasks and second place in five subtasks, demonstrating our expertise in ontology generation.

A Knowledge Distillation Method Based on Evidence Theory to Prevent Catastrophic Forgetting

Abstract Incremental learning is an emerging machine-learning approach designed to prevent catastrophic forgetting while learning a new task with knowledge retained from previous tasks. However, existing methods often must fully utilize the distributional information of the old task model’s outputs. To alleviate this, we propose a class incremental learning method grounded in evidence theory to leverage the distributional information of outputs from the old task model, which integrates uncertainty estimation into the knowledge distillation process to ensure the new task model effectively learns from the old task model’s distribution information. Specifically, we incorporate uncertainty estimation based on evidence theory to calculate knowledge distillation loss during training. We compute the uncertainty estimates of outputs from both the new and old task models, and then they are used in the knowledge distillation loss calculation. We propose a novel classification strategy that considers outputs’ probability and uncertainty estimates, which could determine the sample category without requiring an additional training phase or supplementary models. Experiments on the CIFAR-100 and ImageNet datasets demonstrate the effectiveness of the proposed method, showing that it outperforms existing methods by improving accuracy by 1%-2%. Results suggest that leveraging the distribution information of model outputs can effectively mitigate catastrophic forgetting in deep learning models within incremental learning scenarios.

A depth graph attention-based multi-channel transfer learning network for fluid classification from logging data

Fluid classification is a fundamental task in the field of geological sciences to achieve effective reservoir characterization and hydrocarbon exploration. Traditional fluid classification methods are often limited by long processing times and an inability to capture complex relationships within the data. To address this issue, this paper proposes a novel deep learning approach—the Deep Graph Attention Multi-channel Transfer Learning Network (DGMT), aimed at improving the efficiency and accuracy of fluid classification from logging data. This model comprises three key components: a graph attention layer, a multi-channel feature extractor, and a transfer learning module. The graph attention layer is designed to handle spatial dependencies between different logging channels, enhancing classification accuracy by focusing on critical features. The multi-channel feature extractor integrates information from various data sources, ensuring comprehensive utilization of the rich information in logging data. The transfer learning module allows the model to transfer knowledge from pre-trained models of similar tasks, accelerating the training process and significantly improving the model's generalization ability and robustness. This feature enables the DGMT model to adapt to different geological environments and logging conditions, showing superior performance over traditional methods. To validate the effectiveness of the DGMT model, we conducted experiments on actual logging datasets containing multiple oil wells. The experimental results indicate that, compared to common machine learning algorithms and other deep learning methods, the DGMT model significantly improves in accuracy and other classification performance metrics.

SMART: Search Maps and Routes/Trails of Both Subtasks and Sources to Support Complex Task‐based Search

ABSTRACTTowards the goal of constructing an agent that can assist users in completing complex tasks and searching for information from multiple sources. We propose a complex task‐supporting application, SMART (Search Maps and Routes/Trails), which combines complex task modeling through users' web activities and expands search support to include maps and routes/trails of both subtasks and sources within complex tasks through case‐based reasoning. We also summarize the results of a user study conducted to evaluate the usability of the SMART prototype system. The experimental results show that SMART could help searchers make search strategies, select information sources effectively, increase confidence during task performance, and improve search results. This highlights the value of both types of maps and routes/trails of subtasks and sources in complex task‐supporting.

A two-task predictor for discovering phase separation proteins and their undergoing mechanism.

Liquid-liquid phase separation (LLPS) is one of the mechanisms mediating the compartmentalization of macromolecules (proteins and nucleic acids) in cells, forming biomolecular condensates or membraneless organelles. Consequently, the systematic identification of potential LLPS proteins is crucial for understanding the phase separation process and its biological mechanisms. A two-task predictor, Opt_PredLLPS, was developed to discover potential phase separation proteins and further evaluate their mechanism. The first task model of Opt_PredLLPS combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) through a fully connected layer, where the CNN utilizes evolutionary information features as input, and BiLSTM utilizes multimodal features as input. If a protein is predicted to be an LLPS protein, it is input into the second task model to predict whether this protein needs to interact with its partners to undergo LLPS. The second task model employs the XGBoost classification algorithm and 37 physicochemical properties following a three-step feature selection. The effectiveness of the model was validated on multiple benchmark datasets, and in silico saturation mutagenesis was used to identify regions that play a key role in phase separation. These findings may assist future research on the LLPS mechanism and the discovery of potential phase separation proteins.

How disinhibition explains reward omission leadership

Disinhibition is a lack of restraint manifesting as tactless, uncivil, and inappropriate behavior; reward omission leadership is the failure of a leader to reward desirable behavior. We hypothesize disinhibition is associated with reward omission leadership. We test this possibility by associating an objective measure of disinhibition and personality models of disinhibition with reward omission leadership. In S1 ( N = 166), the go/no-go task and personality models of disinhibition were generally associated with reward omission leadership. In S2 ( N = 176), using multi-actor ratings, personality models of disinhibition were generally associated with reward omission leadership. In S3 ( N = 91 junior managers; 273 employees), the tested personality model of disinhibition was associated with reward omission leadership using employee ratings from a non-Western organization. Results suggest disinhibition predicts reward omission leadership and sheds light on what is meant by disinhibition. JEL Classification: D23

Supplemental Material for Variance (Un)Explained: Experimental Conditions and Temporal Dependencies Explain Similarly Small Proportions of Reaction Time Variability in Linear Models of Perceptual and Cognitive Tasks

Supplemental Material for Variance (Un)Explained: Experimental Conditions and Temporal Dependencies Explain Similarly Small Proportions of Reaction Time Variability in Linear Models of Perceptual and Cognitive Tasks

Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks model

The privacy-protected algorithm (PPA) is pivotal in the realm of machine learning, especially for handling sensitive data types, such as medical and financial records. PPA enables two distinct operations: data publishing and data analysis, each capable of functioning independently. However, the field lacks a unified framework or an efficient algorithm to synergize these operations. This deficiency inspires our current research endeavor. In this paper, we introduce a novel dual-mode empirical risk minimization (D-ERM) model, specifically designed for integrated learning tasks. We also develop an alternating minimization differential privacy protection algorithm (AMDPPA) for implementing the D-ERM model. Our theoretical analysis confirms the differential privacy and accuracy of AMDPPA. We validate the algorithm’s efficacy through numerical experiments using real-world datasets, demonstrating its ability to effectively balance privacy with learning efficiency.

Voluntary turnover of high achievers: A systems psychodynamics analysis with CIBART

Orientation: Voluntary turnover is a costly and frequently disruptive organisational phenomenon, particularly when it involves high achievers.Research purpose: The study aimed to develop a psychodynamic exposé of voluntary turnover by applying the conflict, identity, boundaries, authority, roles and task (CIBART) model to the lived experiences of high achievers.Motivation for the study: The research primarily focusses on voluntary turnover as an individual, rational decision-making process. It recognises the contribution of unconscious systems psychodynamics to voluntary turnover. Research conducted from this perspective is necessary to gain a more comprehensive and in-depth understanding of the topic.Research approach/design and method: Seven case studies of individual voluntary turnover were conducted in a large private retail company. Data were collected using free association narrative (FANI) interviews. The analysis of the data was guided by hermeneutic phenomenology, and the interpretive frame used was the CIBART model.Main findings: Six themes were constructed to describe the conflict dynamics along each of the six CIBART dimensions. Within each theme, the conflict dynamic is exposed, revealing the subsequent anxiety and defences that accumulate leading to the decision to leave. Ultimately, the six themes synthesise to propose that voluntary turnover is a conscious manifestation, yet an unconscious defence against system-wide conflict.Practical/managerial implications: Facilitating leaders’ understanding and processing of the conflict dynamics that occur across the CIBART dimensions, and which contribute to voluntary turnover, create opportunities for both transformation and prevention.Contribution/value-add: The research offers insights into the psychodynamics of voluntary turnover, which consulting psychologists and organisational leaders can utilise to address this phenomenon within their work groups.

Real-time service task scheduling with fine-grained resource utilization to benefit important industrial business

Scheduling of service-oriented industrial businesses to meet the real-time requirements and improve the computing resources utilization is important. To this end, a Fine-grained Online Service Task Scheduling (FOSTS) method is proposed. First, a service task model that separates the invoked service from the task is established. Then, a service invocation-oriented allocation mechanism that allocates the invoked tasks based on priority is proposed to achieve fine-grained resource utilization. In addition, a task suspension mechanism to release resources for important tasks by suspending non-important tasks is proposed. The proposed FOSTS is tested in a typical industrial scenario and its performance is compared with many common and state-of-the-art methods. The simulation results show the effectiveness and superior performance of the proposed FOSTS compared to the other methods.

How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies

This paper analyses the impact of the use of industrial robots on labor income shares at both the theoretical and empirical levels. On the theoretical side, the role of induced technological progress, the creation of new tasks, and the penetration of industrial robots on labor income shares are systematically explored by incorporating industrial robots into task models and endogenizing the task induced technological progress. Empirically, industrial robot penetration at the regional level in China is constructed from the industrial robots data released by the International Federation of Robotics (IFR), which is matched with Chinese A-share listed companies in 2011–2019, and the causal strategy of Bartik-style instrumental variables is used to analyze the impact of the penetration of industrial robots and task induced technological progress on the share of labor income. The study shows that the penetration of industrial robots significantly increases the share of labor income, but the task induced technological progress reduces the share of labor income, and that the impact of the penetration of industrial robots varies significantly external financing dependence, ownership, and regions. From the perspective of the impact mechanism, robot penetration increase the levels of inputs complementarity and hence reduce the elasticity of substitution between robots and labor. Meanwhile, robots can effectively increase the wage level of the labor force, thus increasing the labor income share. The findings of this paper provide a decision-making reference for how industrial robots can better serve human beings in the new era so that workers can share the fruits of economic development.

Automation: Theory, Evidence, and Outlook

This article reviews the literature on automation and its impact on labor markets, wages, factor shares, and productivity. I first introduce the task model and explain why this framework offers a compelling way to think about recent labor market trends and the effects of automation technologies. The task model clarifies that automation technologies operate by substituting capital for labor in a widening range of tasks. This substitution reduces costs, creating a positive productivity effect, but it also reduces employment opportunities for workers displaced from automated tasks, creating a negative displacement effect. I survey the empirical literature and conclude that there is wide qualitative support for the implications of task models and the displacement effects of automation. I conclude by discussing shortcomings of the existing literature and avenues for future research.

Trends and prospects for the development of modeling in accounting

The historical role of modeling in the development of accounting and the modern interpretation of the use of models for solving management problems in accounting automation are shown. The dynamics of the spread of different models of integrated accounting automation are presented, and trends in the complexity of models with the development of information technology are highlighted. The features of the development of modeling of management tasks in the digital transformation of agriculture are shown. The need to develop accounting modeling at the macroeconomic level is noted.