Set Of Tasks Research Articles

NARRATIVE TECHNOLOGY FOR THE DEVELOPMENT OF PERSONAL LIFE COMPETENCE

The article addresses the issue of developing personal life competence within the framework of the psychological-hermeneutic, specifically narrative, approach. The feasibility of using narrative technologies in the context of developing personal life competence is substantiated: by constructive problem-solving, effective setting and solving of life tasks, strengthening psychological resilience, enhancing self-awareness and reflection, and building productive adaptive strategies during the prolonged war in Ukraine. The article characterizes the author's narrative technology as "Core Personal Questions," aimed at developing personal life competence through self-reflection, defining personal values, motives, and goals, and forming a holistic self-image. The proposed technology helps individuals understand their own experiences, current resources, beliefs, behavioral strategies, self-presentations, and life and personal projects, which is of significant importance during the war. The narrative technology "Core Personal Questions" is directed towards constructing retrospective, current, and prospective self-narratives, through which the understanding of life and personal experiences, determination of meaningful priorities and prospects, and adaptation to the current crisis conditions occur. The use of this technology involves the development of personal life competence in three dimensions: routine-selective, adaptive, and innovative. The methodological tools are based on key questions, the search for answers to which fosters self-reflection, independent setting, clarification, and redefinition of the main life tasks by the individual. The article illustrates the possibilities of applying the proposed narrative technology in working with vulnerable populations during the war in Ukraine. Keywords: personality, life competence, development, dimensions of development, reflection, narrative, narrative technologies, personal questions, war in Ukraine

SEMANTIC SPACE OF YOUTH AS A FACTOR IN THE FORMATION OF LIFE COMPETENCE IN THE CONDITIONS OF WAR

The study aims to define the semantic fields of the "life competence" concept and clarify the nature of the relationship between understanding this concept and the setting of life tasks by young people in war. The goal was achieved by solving the following tasks in turn: clarifying the etymology of the "life competence" concept and its content and structural components as semantic fields of understanding this concept in everyday and scientific contexts; studying the semantic space of understanding life competence by young people in war conditions; determining the impact of the peculiarities of understanding the "life competence" concept by young people on their life tasks. The tasks were solved with the help of theoretical (theoretical and methodological analysis, comparison and generalization of results), empirical (semantic differential method, narrative survey method), and mathematical (factor and correlation analyses) methods. The conducted theoretical and empirical research has shown a sufficient cognitive complexity of the "life competence" concept young people's understanding and concepts closely related to it: "life experience", "life task", "difficult life situation", as well as the closeness of semantics to scientific contexts. It has been found that the semantic fields of these concepts are in close direct relationship, which are logically interpreted in the context of everyday thinking and modern psychological science. Defined positive correlations of the criteria for understanding the "life competence" concept by young people with the life tasks that young people set during the war: development, education, family mutual assistance, helping others, solving problems of material support, preserving psychological health, which allowed us to characterize the features of young people semantic space by their life tasks priorities Keywords: life competence, semantic space, difficult life situation, life experience, life task, war, youth

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent's behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

Родительская зрелость: содержание и структура концепта в разрезе отечественных и зарубежных исследований

<p>The author analyzes the concept of parental maturity with the aim to operationalize it and define the structure. The construct is based on maturity understanding in the context of parenthood. Parental maturity is a complex, diversified form that reflects personal maturity. The structure of the construct is presented as a set of instrumental, motivational, and value components, integrating in the internal position of a parent in life choices regarding the birth, upbringing of children, and responsibility for the decisions made. The development of parental maturity is facilitated by the development of the parent’s internal position, including self-reflection. It helps to assess the level of personal maturity and analyze parental position, resiliency, involvement, control of a child’s development, and taking risks associated with children growing up. Within parenthood, the development of semantic and life-meaning orientations activates the setting of new self-development tasks by the parents themselves.</p>

Effectiveness of Structured teaching program on Knowledge and Practice of Blood and Blood components Transfusion among Staff Nurses

Background: Blood is a collection of cells that have been specialized to perform a set oftasks within an organism. It is described as the fluid connective tissue that carriesoxygen and nutrients to the body. It is straw in colour, the average volume of blood inthe human body is 5 litres generally, the PH of blood is 7.4, which is slightly alkaline. The blood comprises 8% of our total body weight. It is composed of plasma, about 55% and 45% cells, respectively. Blood transfusion is when apatient receives a blood product through an intravenous line or the introduction of newmatching blood into the bloodstream or/and of any specific blood components. Objectives: The study aimed to assess the knowledge and practice of a structured teaching programme on blood and blood components among staff nurses. Methods: 50 Staff nurses were selected by a non-probabilityconvenient sampling technique. Quasi-experimental design with one grouppre-test and post-test design was used. The tools used to collect the data consisted of a structuredknowledge questionnaireand structured practice scale on blood and blood component transfusion. The reliability was checked using test-retest method showed that the tools were highly reliable. Results: The good knowledge score was increased from pretest (12%) to posttest (32%). Moderate practice score was increased from pretest (50%) to posttest (78%). There was a significant difference (p<0.05) between mean pretest knowledge and practice scores with mean post test knowledge and practice scores. So, the structured teaching program was effective in improving the knowledge and practice of staff nurses regarding blood and blood components. The Chi-square test showed that there was a significant association with knowledge and gender, practice and marital status, education. The other variables are not showing association. Conclusion: The findings revealed thatstaff nurses’ knowledge and practice regarding bloodand blood components transfusion wasimproved after the teaching program.

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making.

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.

The characteristics of medical technical task working out as a factor of successful implementation of project of public-private partnership in health care

The application of mathematical modeling approaches based on factual demand of the population of territories in setting of medical and technical tasks makes it possible to significantly optimize costs of construction and equipping primary health care objects. This is confirmed by both corresponding calculations and results of analysis. This operation is oriented both on structural divisions of regional executive authorities responsible for setting of medical and technical tasks, and on investment companies implementing projects in health care.

Algorithm of Development for Stabilization of Socio-Ecological Situation in Karakalpakstan

The relevance of developing a scientific framework for stabilizing the socio-ecological situation of the Republic of Karakalpakstan is determined by the need to address the environmental, social and economic challenges faced by the region. It is designed to improve the population’s quality of life, sustainable development and balanced interaction with the environment. The purpose of the research was to establish the relationship between social and environmental factors, to establish a mathematical model based on them, which will enable predicting future development and establishing a set of recommendations to improve the situation in the Republic of Karakalpakstan. The methods of theoretical analysis and mathematical modelling were used. The research outlines modern ideas about modelling the dynamics of socio-ecological processes in the example of the Republic of Karakalpakstan. It is established that the Republic has several problems, the solution of which should be started shortly. Considerable attention is paid to the setting of tasks and using for this purpose methods of system analysis of socio-ecological factors. In this work, the analysis of factors affecting the social and environmental environment of the Republic of Karakalpakstan using regression analysis is considered. The parameters of the developed mathematical model and their adequacy are determined. The developed mathematical model will allow identification of the social and ecological situation of the republic. At the end of the work, ten priority points for the next 5-10 years to improve the socio-ecological situation were presented. The practical value of the results is implemented in promoting the identification of new powerful reserves for further improvement of the management of social processes. This approach allows for increasing the national economic efficiency of the region and ensuring a balance between economic, social and environmental aspects of development, which is an essential step towards a sustainable future.

A Study on the Incorporation of Ideological-Political Elements in Public Speaking from the Perspective of Production-Oriented Approach

This study is a four-year, three-round study on the incorporation of ideological-political elements in the activity of public speaking in Business English: An Integrated Course, based on a production-oriented approach (POA). With the overall result of questionnaires, semi-structured interviews, reflection logs, and classroom observation, it is found that the six teachers in this course and all the students who participated in the study all agree with the online and offline blended teaching model, which, when used in this study, can help to strengthen students’ business-related overall ability. Besides, vivid and brief teaching cases of the public speaking task design shown in this article demonstrate the implementation approach of this study, which includes the setting of POA tasks, multiple evaluations, and blended teaching. It is hoped that this study can serve as an example of the incorporation of ideological-political elements in the courses of foreign language.

A comparative study of biomechanical assessments in laboratory and field settings for manual material handling tasks using extractor tools and exoskeletons.

To enhance physical capabilities of workers who regularly perform physically demanding tasks involving heavy lifting and awkward postures, various tools and occupational exoskeletons can be used. Most of the studies aiming to explore the efficiency of these tools and exoskeletons have been performed in confined and controlled laboratory spaces, which do not represent the real-world work environment. This study aimed to compare the outcome of biomechanical assessment of using a back support exoskeleton and assistive tools (Lever and Jake) in the procedure of a high demanding manual material handling task versus the results found by performing the same task in a laboratory. Ten able-bodied participants and ten able-bodied utility workers performed the same manhole removal task in-lab and in-field, respectively, with the aid of an exoskeleton and Lever and Jake tools. Muscle activity and Rapid Entire Body Assessment (REBA) scores were recorded using surface electromyography and inertial measurement units, respectively and compared between in-lab and in-field trials. The field experiments indicated significant differences (p < 0.05) in normalized muscle activity across most muscles when compared to laboratory data. These results revealed how muscle activity is affected by the controlled lab setting compared to real-world field conditions. However, REBA scores indicate similar ergonomic implications regardless of the utilization of exoskeletons or tools. These findings underscore that real-world field assessments are crucial for evaluating ergonomic risks and effects of occupational exoskeletons and tools to account for environmental factors and workers' skills in ergonomic evaluations of this nature.

MPSketch: Message Passing Networks via Randomized Hashing for Efficient Attributed Network Embedding.

Given a network, it is well recognized that attributed network embedding represents each node of the network in a low-dimensional space, and, thus, brings considerable benefits for numerous graph mining tasks. In practice, a diverse set of graph tasks can be processed efficiently via the compact representation that preserves content and structure information. The majority of attributed network embedding approaches, especially, the graph neural network (GNN) algorithms, are substantially costly in either time or space due to the expensive learning process, while the randomized hashing technique, locality-sensitive hashing (LSH), which does not need learning, can speedup the embedding process at the expense of losing some accuracy. In this article, we propose the MPSketch model, which bridges the performance gap between the GNN framework and the LSH framework by adopting the LSH technique to pass messages and capture high-order proximity in a larger aggregated information pool from the neighborhood. The extensive experimental results confirm that in node classification and link prediction, the proposed MPSketch algorithm enjoys performance comparable to the state-of-the-art learning-based algorithms and outperforms the existing LSH algorithms, while running faster than the GNN algorithms by 3-4 orders of magnitude. More precisely, MPSketch runs 2121, 1167, and 1155 times faster than GraphSAGE, GraphZoom, and FATNet on average, respectively.

A Transferability-Based Method for Evaluating the Protein Representation Learning.

Self-supervised pre-trained language models have recently risen as a powerful approach in learning protein representations, showing exceptional effectiveness in various biological tasks, such as drug discovery. Amidst the evolving trend in protein language model development, there is an observable shift towards employing large-scale multimodal and multitask models. However, the predominant reliance on empirical assessments using specific benchmark datasets for evaluating these models raises concerns about the comprehensiveness and efficiency of current evaluation methods. Addressing this gap, our study introduces a novel quantitative approach for estimating the performance of transferring multi-task pre-trained protein representations to downstream tasks. This transferability-based method is designed to quantify the similarities in latent space distributions between pre-trained features and those fine-tuned for downstream tasks. It encompasses a broad spectrum, covering multiple domains and a variety of heterogeneous tasks. To validate this method, we constructed a diverse set of protein-specific pre-training tasks. The resulting protein representations were then evaluated across several downstream biological tasks. Our experimental results demonstrate a robust correlation between the transferability scores obtained using our method and the actual transfer performance observed. This significant correlation highlights the potential of our method as a more comprehensive and efficient tool for evaluating protein representation learning.

Impact of white noise in artificial neural networks trained for classification: Performance and noise mitigation strategies.

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.

One-shot skill assessment in high-stakes domains with limited data via meta learning

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5 % in one-shot and 99.9 % in few-shot settings for simulated tasks and 89.7 % for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

BackgroundThe timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).MethodsThis study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).ResultsThe DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.ConclusionsThe US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Temporal Implicit Multimodal Networks for Investment and Risk Management

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.

H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer

Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and model details is limited in the era of commercial models, giving rise to the setting of multi-source-free (MSF) transfer learning that aims to leverage source domain knowledge without such access. As a newly defined problem paradigm, MSF transfer learning remains largely underexplored and not clearly formulated. In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression. The ensemble weights are optimized by maximizing an information theoretic metric for transferability. Compared to previous works, H-ensemble is characterized by: 1) its adaptability to a novel and realistic MSF setting for few-shot target tasks, 2) theoretical reliability, 3) a lightweight structure easy to interpret and adapt. Our method is empirically validated by ablation studies, along with extensive comparative analysis with other task ensemble and transfer learning methods. We show that the H-ensemble can successfully learn the optimal task ensemble, as well as outperform prior arts.

Not All Tasks Are Equally Difficult: Multi-Task Deep Reinforcement Learning with Dynamic Depth Routing

Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a single policy. To enhance data efficiency by sharing parameters across multiple tasks, a common practice segments the network into distinct modules and trains a routing network to recombine these modules into task-specific policies. However, existing routing approaches employ a fixed number of modules for all tasks, neglecting that tasks with varying difficulties commonly require varying amounts of knowledge. This work presents a Dynamic Depth Routing (D2R) framework, which learns strategic skipping of certain intermediate modules, thereby flexibly choosing different numbers of modules for each task. Under this framework, we further introduce a ResRouting method to address the issue of disparate routing paths between behavior and target policies during off-policy training. In addition, we design an automatic route-balancing mechanism to encourage continued routing exploration for unmastered tasks without disturbing the routing of mastered ones. We conduct extensive experiments on various robotics manipulation tasks in the Meta-World benchmark, where D2R achieves state-of-the-art performance with significantly improved learning efficiency.

Fast Shapley Value Computation in Data Assemblage Tasks as Cooperative Simple Games

In this paper, we tackle the challenging problem of Shapley value computation in data markets in a novel setting of data assemblage tasks with binary utility functions among data owners. By modeling these scenarios as cooperative simple games, we leverage pivotal probabilities to transform the computation into a problem of counting beneficiaries. Moreover, we make an insightful observation that the Shapley values can be computed using subsets of minimal syntheses within the inclusion-exclusion framework in combinatorics. Based on this insight, we develop a game decomposition approach and utilize techniques in Boolean function decomposition into disjunctive normal form. One interesting property of our method is that the time complexity depends only on the data owners participating in those minimal syntheses, rather than all the data owners. Extensive experiments with real data sets demonstrate a significant efficiency improvement for computing the Shapley values in data assemblage tasks modeled as simple games.

Resource Allocation Scheduling Optimization Based on Internet of Things under Cloud Platforms

In this paper, we explore how to optimize IoT-based resource allocation and scheduling in a cloud platform environment, focusing on improving computing resource utilization and quality of service, while reducing latency and packet loss. a model is adopted, which contains a number of edge servers and randomly generated computational tasks, taking into account the network conditions between the servers and the tasks. an objective function is established, aiming to maximize the computational resource utilization and QoS, and the corresponding constraints are proposed. Simulations are conducted using CloudSim, and the experimental results show that the total number of VoCS increases from 243.63 to 1397.71 when the scheduling demand is increased from 8 to 64, demonstrating the adaptability and efficiency of the algorithm under different demands. In addition, the algorithm is effective in dealing with both small-scale (200 tasks) and large-scale (6000 tasks) tasks. In addition, the algorithm demonstrates low load imbalance and short task completion time when dealing with both small-scale (200 tasks) and large-scale (6000 tasks) task sets, which proves its effectiveness. Ultimately, the scheduling method proposed in this study not only improves resource utilization and quality of service, but also reduces task completion time and cost.