Task Model Research Articles

Использование учебных стратегий при обучении испанскому языку как второму иностранному в языковом вузе

The article presents examples of learning strategies that are used in the process of learning Spanish and offers models of learning tasks within the framework of learning strategies for different levels of Spanish proficiency. The necessity of familiarizing students with the culture of Spain and Latin American countries is indicated. The purpose of the study is to identify the effectiveness of using educational strategies in classes on mastering Spanish as a second foreign language at a language university. Metacognitive, communicative, interactive and affective strategies and examples of their use in teaching Spanish as a second foreign language are chosen as the object of research. It is noted that the application of educational strategies in practical classes contributes to success and helps a future specialist in their further professional activities. The scientific novelty of the work lies in the proof of the need to use specific learning strategies to achieve a high level of proficiency in a foreign language in practical classes at a language university. As a result of the study, the essence of the concept of “learning strategy” was revealed, communicative situations were created and described, as well as a number of other types of exercises that took place in practical Spanish classes. The effectiveness of using metacognitive, communicative, interactive, affective strategies to increase motivation and achieve a high level of foreign language proficiency, as well as to promote a tolerant attitude towards the culture of the target language, was shown.

Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation.

To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in computer vision and pattern recognition communities. In UDA scenario, the training and test data belong to different domains while the task model is learned to be invariant. Recently, empirical connections between transferability and discriminability have received increasing attention, which is the key to understand the invariant representations. However, theoretical study of these abilities and in-depth analysis of the learned feature structures are unexplored yet. In this work, we systematically analyze the essentials of transferability and discriminability from the geometric perspective. Our theoretical results provide insights into understanding the co-regularization relation and prove the possibility of learning these abilities. From methodology aspect, the abilities are formulated as geometric properties between domain/cluster subspaces (i.e., orthogonality and equivalence) and characterized as the relation between the norms/ranks of multiple matrices. Two optimization-friendly learning principles are derived, which also ensure some intuitive explanations. Moreover, a feasible range for the co-regularization parameters is deduced to balance the learning of geometric structures. Based on the theoretical results, a geometry-oriented model is proposed for enhancing the transferability and discriminability via nuclear norm optimization. Extensive experiment results validate the effectiveness of the proposed model in empirical applications, and verify that the geometric abilities can be sufficiently learned in the derived feasible range.

Lesions to different regions of frontal cortex have dissociable effects on voluntary persistence.

Deciding how long to keep waiting for uncertain future rewards is a complex problem. Previous research has shown that choosing to stop waiting results from an evaluative process that weighs the subjective value of the awaited reward against the opportunity cost of waiting. Activity in ventromedial prefrontal cortex (vmPFC) tracks the dynamics of this evaluation, while activation in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) ramps up before a decision to quit is made. Here, we provide causal evidence of the necessity of these brain regions for successful performance in a willingness-to-wait task. 28 participants (20 female and 8 male) with lesions to different regions of the frontal lobe were tested on their ability to adaptively calibrate how long they waited for monetary rewards. We found that participants with lesions to the vmPFC waited less overall, while participants with lesions to the dmPFC and anterior insula were specifically impaired at calibrating their level of persistence to the environment. These behavioral effects were accounted for by systematic differences in parameter estimates from a computational model of task performance.Significance Statement Achieving positive outcomes in education, health or personal finance often involves pursuing larger future rewards at the cost of smaller, more immediate ones. Most neuroscience research on future reward pursuit has focused on the initial discrete choice between a smaller reward that will arrive quickly or a larger reward that will arrive later. However, once the choice has been made, persisting in the initial choice of the later reward through the waiting period is perhaps even more critical to success. Here, we identify specific and dissociable causal roles for different regions of prefrontal cortex in determining people's ability to adaptively persist. This finding extends our understanding of how the brain supports subjective value maximization in the context of delayed rewards.

Decomposing dynamical subprocesses for compositional generalization

A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants experienced sequences of compound images formed from two graphs' product spaces (group 1: G1 and G2, group 2: G3 and G4). In transfer learning, both groups encountered compound images from the product of G1 and G3, composed entirely of new images. We show that subprocess knowledge transferred between task phases, such that in a new task environment each group had enhanced accuracy in predicting subprocess dynamics they had experienced during prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, without an ability to transfer knowledge, failed to explain these data. Instead, behavior was consistent with a predictive representation model that maps task states between prior and transfer learning. These results help advance a mechanistic understanding of how humans discover and abstract subprocesses composing their experiences and compositionally reuse prior knowledge as a scaffolding for new experiences.

Towards Computer-Supported Functional Modelling in Engineering Design Education

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool’s lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions.

Towards Holistic Functional Task Analysis

ABSTRACTTask analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

Fast Broad Multiview Multi-Instance Multilabel Learning (FBM3L) With Viewwise Intercorrelation.

Multiview multi-instance multilabel learning (M3L) is a popular research topic during the past few years in modeling complex real-world objects such as medical images and subtitled video. However, existing M3L methods suffer from relatively low accuracy and training efficiency for large datasets due to several issues: 1) the viewwise intercorrelation (i.e., the correlations of instances and/or bags between different views) are neglected; 2) the diverse correlations (e.g., viewwise intercorrelation, interinstance correlation, and interlabel correlation) are not jointly considered; and 3) high computation burden for training process over bags, instances, and labels across different views. To resolve these issues, a novel framework called fast broad M3L (FBM3L) is proposed with three innovations: 1) utilization of viewwise intercorrelation for better modeling of M3L tasks while existing M3L methods have not considered; 2) based on graph convolutional network (GCN) and broad learning system (BLS), a viewwise subnetwork is newly designed to achieve joint learning among the diverse correlations; and 3) under BLS platform, FBM3L can learn multiple subnetworks jointly across all views with significantly less training time. Experiments show that FBM3L is highly competitive (or even better than) in all evaluation metrics up to 64% in average precision (AP) and much faster than most M3L (or MIML) methods (up to 1030 times), especially on large multiview datasets ( ≥ 260 K objects).

Utility-Driven Approach to Onboard Scheduling and Execution for an Autonomous Europa Lander Mission

Jupiter’s icy moon Europa is among the most promising locations to explore for signs of extraterrestrial life in our solar system. However, searching for biosignatures on the surface of Europa presents an unprecedented set of challenges: immense uncertainty, limited energy, and few opportunities for operator feedback. Effectively carrying out a robotic science campaign under these conditions will require a system with a greater degree of autonomy than any planetary exploration mission to date. In particular, onboard scheduling and execution are required for robustness to uncertainty in surface conditions, variation in lander performance, and science discoveries to maximize the quantity and quality of science data returned to Earth during a fixed, limited lander lifetime. Here we represent a proposed Europa Lander surface mission as a utility-driven hierarchical task network and establish through analysis that onboard autonomy using automated mission planning with execution feedback and predictive task models results in mission execution that is more consistently productive compared to traditional static approaches. We design a simulated onboard autonomy framework built by integrating two software components—Multi-mission EXECutive and Europa Lander Autonomy Prototype—to properly simulate the Europa Lander domain and demonstrate empirically that the proposed planning and execution system is capable of commanding a set of realistic surface scenarios as part of a larger Europa Lander surface autonomy software prototype. We expect that an approach to scheduling and execution grounded in decision theory will be an enabling technology for future tightly constrained planetary surface missions.

Resources don't grow on trees: A framework for resource-driven adaptation

Software systems execute tasks that depend on different types of resources. However, the variability of resources may interfere with the ability of software systems to execute important tasks. In this paper, we present a proactive and reactive resource-driven adaptation framework that supports different types of resources and is based on the prioritisation of tasks. In our previous work, we defined a task modelling notation called SERIES. This paper builds on that notation and focuses on the adaptation process to improve resource utilisation by substituting tasks with their variants, substituting resources with alternative ones, executing tasks in a different order, or cancelling the execution of low-priority tasks. The framework was evaluated in terms of the percentage of executed critical task requests, average criticality of the executed task requests in comparison to the non-executed ones, overhead, and scalability. The evaluation was executed using two datasets related to a medicine consumption system and a manufacturing system. The evaluation results showed that the proposed framework increased the number of executed critical task requests and the average criticality of the executed task requests under resource variability with marginal overhead.

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.