Task Model Research Articles

Teachers' perceptions of the epistemic aims and evaluation criteria of multiple text integration

The importance of intertextual integration in current information societies has led to extensive research on how to foster students' integration competence. Although teachers have a central role in promoting integration skills, research into their perceptions of intertextual integration tasks is still scarce. Hence, the purpose of this study was to explore language arts teachers' views on the epistemic aims and evaluation criteria of intertextual integration. Fifty-four language arts teachers were asked to evaluate three integrated essays, supposedly written by students, that reflected typical violations of key integration quality criteria. In addition, they responded to open-ended questions about intertextual integration aims and evaluation criteria. Teachers successfully identified only about a third of the integration quality criteria violations that were embedded in the essays. Nonetheless, they collectively raised diverse evaluation criteria that addressed content selection and understanding, argumentation quality, source use and intertextuality, structure and organization, and language sophistication. Teachers' most prevalent aims were related to information processing and to the development of learners’ reading and writing skills. However, teachers also referred to knowledge-building goals. Quantitative and qualitative analyses revealed interrelations between teachers' perceptions of evaluation criteria and their integration aims. Overall, these findings demonstrate that language arts teachers have rich multiple-text integration task models. At the same time, they suggest that many teachers might not perceive intertextual integration as an opportunity for knowledge building and for developing learners’ critical thinking. The results of this study can inform professional development efforts to support instruction of intertextual integration.

Regional choroidal thickness estimation from color fundus images based on convolutional neural networks

PurposeThis study aims to estimate the regional choroidal thickness from color fundus images from convolutional neural networks in different network structures and task learning models. Method1276 color fundus photos and their corresponding choroidal thickness values from healthy subjects were obtained from the Topcon DRI Triton optical coherence tomography machine. Initially, ten commonly used convolutional neural networks were deployed to identify the most accurate model, which was subsequently selected for further training. This selected model was then employed in combination with single-, multiple-, and auxiliary-task training models to predict the average and sub-region choroidal thickness in both ETDRS (Early Treatment Diabetic Retinopathy Study) grids and 100-grid subregions. The values of mean absolute error and coefficient of determination (R2) were involved to evaluate the models' performance. ResultsEfficientnet-b0 network outperformed other networks with the lowest mean absolute error value (25.61 μm) and highest R2 (0.7817) in average choroidal thickness. Incorporating diopter spherical, anterior chamber depth, and lens thickness as auxiliary tasks improved predicted accuracy (p-value = 6.39×10−44, 2.72×10−38, 1.15×10−36 respectively). For ETDRS regional choroidal thickness estimation, multi-task model achieved better results than single task model (lowest mean absolute error = 31.10 μm vs. 33.20 μm). The multi-task training also can simultaneously predict the choroidal thickness of 100 grids with a minimum mean absolute error of 33.86 μm. ConclusionsEfficientnet-b0, in combination with multi-task and auxiliary task models, achieve high accuracy in estimating average and regional macular choroidal thickness directly from color fundus photographs.

A Methodology for Ship Cabin Equipment Layout Considering Human Factor Reliability Optimization

_ The paper proposes an innovative methodology of Human Cognitive Reliability- Cognitive Reliability and Error Analysis Method (HCR-CREAM) coupled with A* search and genetic algorithm (GA) to tackle ship cabin equipment layout considering human factor reliability optimization with the goal of minimizing human error probability (HEP) subjected to practical requirements. After establishing the mathematical model of cabin equipment inspection tasks in ship cabin equipment layout problem through HCR-CREAM and equipment geometric simplification, a method of the horizontal movement based on minimum distance is presented to avoid the equipment overlapping, then A* search is used for planning inspection paths and GA with selection, crossover, and mutation operators is applied to solve equipment layout results. A case of equipment layout in a certain ship engine room has been taken to carry out parameter sampling experiments by Latin Hypercube for GA. The results show the solution effect of GA is less affected by its parameter variation. And through the comparison with the initial equipment layout, the indicators influencing the HEP of the optimized result have been improved, thus significantly reducing HEP. Keywords ship cabin equipment layout; human factor reliability optimization; HCRCREAM; A* search; genetic algorithm

Моделирование учебных планов профильного обучения

The article focuses on the need to modernize approaches to organizing specialized education in high school in connection with the change in vectors of economic and technological development of the country, which are reflected in the latest decisions of the President and government of the Russian Federation. To achieve this task, an organizational and content model of specialized training is presented, which makes it possible to combine the goals of participants in educational relations, including the state, and the requirements of the regulatory framework of Russian education. The result of this combination is a curriculum for specialized training, examples of the compilation of which for a technological profile of an engineering orientation complete the article.

Machine Learning Technique Reveals Intrinsic EEG Connectivity Characteristics of Patients With Mild Stroke During Cognitive Task Performing

Although convergent evidence has shown that patients with mild stroke (MS) are commonly accompanied by post-stroke cognitive and/or memory impairment, only disproportionate attention was paid compared to severe stroke. To promote post-stroke management for early intervention in MS-related cognitive impairment, a feasible and convenient method for MS detection is therefore favorable. A data-driven classification framework combined with quantitative graph theoretical analysis was introduced in this work, aiming to provide a comprehensive appreciation of MS-related brain network alterations. EEG functional connectivity (FC) was constructed from 45 patients with MS and 45 healthy participants during two cognitive tasks (i.e., visual and auditory oddball) and set as input for the classification model and graph theoretical analysis. As expected, patients showed significantly reduced behavioral performance in both tasks. Furthermore, we achieved a satisfactory classification accuracy of 88.9% with a decision fusion strategy from classification models of both tasks. The spatio-spectral characteristics of the discriminative FC revealed complex topological distributions in both tasks. Moreover, significantly decreased global efficiency was found, suggesting a MS-related disruption in parallel information processing. Overall, these results demonstrated the potential of FC as salient biomarkers for detecting MS, and extended our understanding of the underlying MS-related neural mechanisms during cognitive processing.

Task-Based Compliance Control for Bottle Screw Manipulation With a Dual-Arm Robot

In this paper, a novel task-based compliance control approach for a dual-arm robot is presented with a bottle screw task. The presented approach aims at overcoming uncertainties from the object model and contact forces during the bottle screw task. A novel framework is proposed to synthesize the task motion planning and compliance control that ensure desired performance of both accuracy and compliant motion. The proposed task-based compliance control approach provides a hierarchical strategy: gross motion planning and fine compliance motion planning. The gross motion planning involves the absolute and relative motion control on a macro scale, while the fine compliance motion planning deals with uncertainties by the compliance control to accomplish a task requiring high precision robustly. A theoretical modeling of the bottle screw task is presented within the proposed framework through the analysis of uncertainties and constraints. The experimental results show that the proposed framework is efficient and robust to operate a set of bottles.

Approach to Perforation Interval Selection Uses AI-Assisted Image Interpretation

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216856, “ML-Driven Integrated Approach for Perforation Interval Selection Based on Advanced Borehole-Image AI-Assisted Interpretation,” by Alexander Petrov, SPE, Mounir Belouahchia, SPE, and Abdelwahab Noufal, SPE, ADNOC. The paper has not been peer reviewed. _ In the complete paper, the authors propose an artificial-intelligence (AI)-assisted work flow that uses machine-learning (ML) techniques to identify sweet spots in carbonate reservoirs. This process involves annotation of geologic features using a well database, with supervision from subject-matter experts (SMEs). The resulting ML model is tested on new wells and can identify pay zones, perforation intervals, and stress analysis. The models successfully detect fractures, breakouts, bedding planes, vugs, and slippage passages with pixel-level precision, reducing borehole-image (BHI) analysis time. BHI Interpretation and Preprocessing The use of BHIs requires manual interpretation and data identification, heavily relying on the expertise and time of SMEs. A widely adopted approach to address this challenge is the use of supervised computer-vision algorithms, a subfield of AI. These algorithms optimize the task function or model based on examples they have learned from data during training. However, when applied to BHIs, certain ML challenges must be addressed, including the following: - Detecting features in wells from different reservoirs using a model trained on wells from one reservoir can be highly challenging because reservoirs may exhibit distinct geological characteristics. - The handling of parts of BHIs with missing data, depicted by vertically slanted white strips, poses considerable difficulty. Therefore, the authors created a deep-learning approach based on a generative adversarial network architecture to fill the gaps automatically (Fig. 1). - The labels provided by geologists often do not have pixelwise precision, causing the machine to become confused while trying to learn inconsistent patterns. The authors use a convolutional neural network (CNN) to compute a probability map for pixels belonging to specific classes. In this application, a class is defined as any of the heterogeneities in the BHI; however, this method is applicable to any type of heterogeneities in an image. After training, the CNN module provides the optimal probability for each pixel in the image. To classify regions in the BHI based on heterogeneities, a class-specific threshold is established. Pixels with values above the thresholds are assigned to the corresponding class, while those below the thresholds are assigned to the background. BHI-Derived Porosity A new approach for borehole-derived porosity was developed in-house to overcome the limitations of existing techniques widely used in the industry. This approach capitalizes on BHIs for multiple analyses, including structural dip assessment, fault and fracture identification, and determination of minimum and maximum horizontal stress orientation. However, its primary strength lies in quantifying the fraction of secondary porosity in heterogeneous or dual-porosity carbonate formations. The authors have devised a novel method that uses borehole electrical images to conduct porosity and image connectivity analysis. By implementing this technique, essential information can be extracted regarding the spatial distribution of porosity and the extent of secondary porosity fraction.

Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Concept and implementation of an adaptive digital hearing training system for cochlear implant aftercare

Within the context of an interdisciplinary research project, we created acutting-edge prototype of an adaptive digital auditory training system designed for cochlear implant (CI) users. By leveraging the evidence-centered design (ECD) framework, we integrated adynamic difficulty adjustment feature that tailors the experience to the unique performance capabilities of each individual user. The ECD provides aconceptual design framework suitable for complex assessments of competence and dynamic performance. In the first phase, the domain of hearing was first defined in the context of CI users. In the development phase the three core models of the ECD, the competence model, the evidence model, and the task model, were developed and implemented. In addition, an asset pool of sound and language files was created, which included comprehensive linguistic feature descriptions for calculating item difficulties. Based on the requirements described, an adaptive exercise generator, an AI service, and other components were implemented. This included the development of agame environment and adashboard for patient data management. The exercises' difficulty levels were determined based on various parameters (e.g., sound, word frequency and number of words, grammatical properties) in combination with defined task types and levels. An adaptive digital auditory training system can help to supervise and train CI patients in acontinuous, interactive process based on their individual needs. We see the ECD as an effective way to build auser-based adaptive system.

Design and evaluation of UltRASim: An immersive simulator for learning ultrasound-guided regional anesthesia basic skills

Virtual reality shows great promise as a technology for training healthcare professionals within a secure simulated environment. This work presents the design, development, and assessment of UltRASim: an immersive simulator for ultrasound-guided regional anesthesia. First, task and skills analyses were performed with domain experts to build the task model of the procedure and determine the simulator’s learning objectives and design constraints. Then, a face and content validity study was conducted with eighteen anesthesiologists to assess the simulator’s prototype. The responses to seven of eleven face validity questions were predominantly positive, indicating a favorable reception. The primary concerns pertained to the fidelity of haptic feedback during needle insertion. This suggests incorporating a higher fidelity haptic device in future design iterations. Conversely, responses to all six questions related to the content validity were predominantly positive. Participants found that the simulator held significant potential as a training tool, particularly for developing hand–eye coordination skills. These findings validate several design choices and highlight areas for improvement in subsequent iterations of UltRASim before its formal validation as a training tool.

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

AbstractIntelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

Digital literacy of university students in non-core training directions

Introduction. Forming and improving the readiness and ability of students in non-core (non-IT) areas of training to effectively, productively (safely) use digital devices, programs, technologies, apply digital competencies (knowledge, abilities and skills) to solve educational problems in the context of educational and professional activities is one of the important goals of educational standards in modern universities.Purpose setting. Purpose of the study is the analysis of the problems of the concept, structure and levels of digital literacy of university students in non-core areas of training.Methodology and methods of the study. Research methods are theoretical analysis and synthesis of research results on the definition, structural understanding and level typology of digital literacy of modern university students in non-core areas of training. The methodological basis of the study was the conceptual provisions and principles of the competency-based and personal-activity approaches.Results. Currently, there are theoretical tasks of developing an integrative model of digital literacy and practical tasks of increasing the digital literacy of students in non-core fields of study, including to the level where the student not only consumes digital competencies, but is able and ready to use them in the process independently or jointly with other students and teachers setting and solving educational and professional problems, including in the context of new professional specializations or even professions arising as a result of the digitalization of their work. The leading role in this process is played in the intrapersonal context by the cognitive components of literacy, and in the interpersonal context by communicative and moral-ethical ones.Conclusions. The author summarizes the results of studies on the formation and development of digital literacy among university students in non-core areas of training. The importance and promise of systematic, holistic work in the context of the formation and development of digital literacy of university students in non-core areas of training is noted. The prospects for theoretical and empirical study of this problem are connected precisely with the study of this component and its integrative role in the formation and development of digital literacy of university students in non-core fields.

A Tracking-Based Two-Stage Framework for Spatio-Temporal Action Detection

Spatio-temporal action detection (STAD) is a task receiving widespread attention and has numerous application scenarios, such as video surveillance and smart education. Current studies follow a localization-based two-stage detection paradigm, which exploits a person detector for action localization and a feature processing model with a classifier for action classification. However, many issues occur due to the imbalance between task settings and model complexity in STAD. Firstly, the model complexity of heavy offline person detectors adds to the inference overhead. Secondly, the frame-level actor proposals are incompatible with the video-level feature aggregation and Region-of-Interest feature pooling in action classification, which limits the detection performance under diverse action motions and results in low detection accuracy. In this paper, we propose a tracking-based two-stage spatio-temporal action detection framework called TrAD. The key idea of TrAD is to build video-level consistency and reduce model complexity in our STAD framework by generating action track proposals among multiple video frames instead of actor proposals in a single frame. In particular, we utilize tailored tracking to simulate the behavior of human cognitive actions and used the captured motion trajectories as video-level proposals. We then integrate a proposal scaling method and a feature aggregation module into action classification to enhance feature pooling for detected tracks. Evaluations in the AVA dataset demonstrate that TrAD achieves SOTA performance with 29.7 mAP, while also facilitating a 58% reduction in overall computation compared to SlowFast.

Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers

Labeled data are fed to learning models of classification tasks to help them learn to classify unlabeled data. Massive papers are hinged by citations to a few influential papers, much smaller than the total size, which, if labeled, would cause the spread of label information to the most of the papers. We utilized the co-reference relationship between papers since the references cited by a paper dataset usually cannot be contained by the dataset. We stated the problem of optimal paper labeling as the problem of picking a given fraction of nodes from a co-reference network to maximize the number of their neighbors, which is a submodular maximization problem with a cardinality constraint, NP-hard for general networks. We approximately solved it by picking nodes according to the ranks of specific network centralities. We further proved that labeling papers according to the rank of degree, the lowest-complexity centrality, can give a near-optimal solution with specific constraints on the maximum degree of co-reference network and labeling proportion. Experimental results show that our method brings a significant improvement in the accuracy of classification.

Energy-Aware Adaptive Mixed-Criticality Scheduling with Semi-Clairvoyance and Graceful Degradation

The classic Mixed-Criticality System (MCS) task model is a non-clairvoyance model in which the change of the system behavior is based on the completion of high-criticality tasks while dropping low-criticality tasks in high-criticality mode. In this paper, we simultaneously consider graceful degradation and semi-clairvoyance in MCS. We first propose the analysis for adaptive mixed-criticality with semi-clairvoyance denoted as C-AMC-sem. The so-called semi-clairvoyance refers to the system’s behavior change being revealed at the time that jobs are released. Moreover, we propose a new algorithm based on C-AMC-sem to reduce energy consumption. Finally, we verify the performance of the proposed algorithms via experiments upon synthetically generated tasksets. The experimental results indicate that the proposed algorithms significantly outperform the existing algorithms.

Deadline-Aware Task Offloading for Vehicular Edge Computing Networks Using Traffic Light Data

As vehicles have become increasingly automated, novel vehicular applications have emerged to enhance the safety and security of the vehicles and improve user experience. This brings ever-increasing data and resource requirements for timely computation by the vehicle’s on-board computing systems. To meet these demands, prior work proposes deploying vehicular edge computing (VEC) resources in road-side units (RSUs) in the traffic infrastructure with which the vehicles can communicate and offload compute-intensive tasks. Due to the limited communication range of these RSUs, the communication link between the vehicles and the RSUs — and, therefore, the response times of the offloaded applications — are significantly impacted by vehicle mobility through road traffic. Existing task offloading strategies do not consider the influence of traffic lights on vehicular mobility while offloading workloads onto the RSUs. This causes deadline misses and quality-of-service (QoS) reduction for the offloaded tasks. In this article, we present a novel task model that captures time and location-specific requirements for vehicular applications. We then present a deadline-based strategy that incorporates traffic light data to opportunistically offload tasks. Our approach allows up to 33% more tasks to be offloaded onto RSUs compared with existing work without causing deadline misses, maximizing the resource utilization of RSUs.

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.

Integration of transport system management tasks

The article is devoted to the consideration of the actual problem of integration of transport system management tasks. The distribution of tasks on three levels of management – strategic, tactical and operational is considered. The purpose of the study is to present the results of structural modeling of transport system management tasks at different levels of management and to identify integrating elements. Structural modeling is carried out and five diagrams are presented, including two contextual ones. The schedule of regular passenger transport has been identified as an integrating element of the tactical level of transport system management. The developed structural diagrams show the feedback between the tactical and operational levels of transport system management. The conclusions and discussion of the results of the study are presented.

Dynamical Attention Hypergraph Convolutional Network for Group Activity Recognition.

Recently, group activity recognition (GAR) has drawn growing interests in video analysis and computer vision communities. The current models of GAR tasks are often impractical in that they suppose that all interactions between actors are pairwise, which only models and leverages part of the information in real entire interactions. Motivated by this, we design a distinct dynamical attention hypergraph convolutional network framework, referred to as DAHGCN, for precise GAR, modeling the entire interactions and capturing the high-order relationships among involved actors in a real-life scenario. Specifically, to learn complementary feature representations for fine-grained GAR, a multilevel feature descriptor (MLFD) module is proposed. Furthermore, for learning higher order interaction relationships, we construct a DAHGCN to accommodate complex group interactions, which can dynamically change the topology of the hypergraph and learn these key representations by virtue of the "similarity-based shared nearest-neighbor (SSNN) clustering" and "attention mechanisms" on hypergraph. Finally, a multiscale temporal convolution (MSTC) module is utilized to explore various long-range temporal dynamic correlations across different frames. In addition, comprehensive experiments on three commonly used GAR datasets clearly demonstrate that, when compared with the state-of-the-art methods, our proposed method can achieve the most optimal performance.