Tasks Of Different Complexity Research Articles

FEATURES OF THE IMPLEMENTATION OF INFORMATION ANDCOMMUNICATION TECHNOLOGIES IN THE EDUCATIONAL PROCESS OFPRIMARY GRADE PUPILS

The article analyzes the essence and relationship of the concepts «learning technology», «information and communication technology of learning», «environment», «educational environment of primary school». The impact of the use of ICT in the learning process on the development of mental processes of primary school students is analyzed. It has been established that the use of information and communication technologies in the process of teaching primary school students provides a significant impact on the development of thinking, expression, attention, perception, memory, by including schoolchildren in new types of activities: algorithms, modeling, visualization, abstraction, game imagination. It has been established that the introduction of information and communication technologies into the educational process affects the environment where primary school students learn. It was determined that the digital educational environment in educational institutions should include: information support of the educational institution, the formation of the teacher's information and communication competence, computer-oriented learning tools for primary school students. Features of the use of information and communication technologies in computer science lessons are characterized.The experience of using game, project, multimedia and web technologies in the process of studying of the subject «Computer Science» is highlighted. It was established that the use of information and communication technologies allows: to increase the cognitive activity of students due to the brightness, variety in form, visibility of the educational material; to individualize the learning process using tasks of different complexity; to form the ability to work on a personal computer by using game types of tasks; to improve self-control skills, the ability to independently adjust one's activities by performing project tasks; to independently conduct educational and practical activities, developing creative activity among schoolchildren.

Phase transitions in the mini-batch size for sparse and dense two-layer neural networks

The use of mini-batches of data in training artificial neural networks is nowadays very common. Despite its broad usage, theories explaining quantitatively how large or small the optimal mini-batch size should be are missing. This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks. Working in the teacher-student scenario, with a sparse teacher, and focusing on tasks of different complexity, we quantify the effects of changing the mini-batch size m. We find that often the generalization performances of the student strongly depend on m and may undergo sharp phase transitions at a critical value mc , such that for m<mc the training process fails, while for m>mc the student learns perfectly or generalizes very well the teacher. Phase transitions are induced by collective phenomena firstly discovered in statistical mechanics and later observed in many fields of science. Observing a phase transition by varying the mini-batch size across different architectures raises several questions about the role of this hyperparameter in the neural network learning process.

Cooperative target allocation for air-sea heterogeneous unmanned vehicles against saturation attacks

This paper proposes a cooperative target allocation method for efficiently utilizing air-sea heterogeneous unmanned vehicles to counter saturation attacks by low-cost UAVs. Specifically, a bilevel model that combines the advantages of heuristic algorithms and the coalition formation game (CFG) is constructed by integrating unmanned vehicles' motion and attack attributes. The upper layer model aims to utilize the advantages of the heuristic algorithm to generate a feasible target allocation scheme in a shorter time. A genetic algorithm constrained by inheritance conditions (GAC) is proposed to solve the problem of the low efficiency of traditional genetic algorithms in solving the upper model. The lower layer model is established based on a coalition formation game, which aims to continuously and stably optimize the target allocation scheme. A search method based on mobile players is designed to form a stable coalition structure faster when the number of unmanned vehicles is enormous. Simulation results show the feasibility and superiority of the algorithm proposed in this paper in dealing with allocation tasks of different complexity.

An Individualized Approach to Skin Conductance Assessment during Execution of Tasks of Different Complexities.

Researchers use different approaches to analyze changes in skin conductance. At the same time, individual differences observed in both the initial values of skin conductance and response to stimuli do not allow comparing the responses of different subjects to the same impact. An individualized approach to the assessment of psycho-emotional stress has been developed using the real-time skin conductance index during continuous performance of a task with different levels of complexity. The participants, 18 second-year students at the actors' high school, performed a simple task (expressive reading aloud of a simple text) and a difficult task (expressive reading aloud of the text inverted 180°). During the task, the skin conductance in the participants was continuously recorded. We revealed a change in the parameters of skin conductivity depending on the complexity of the task, both in comparison with resting state (baseline) and with simple and complex stages of the study. However, the individual skin conduction curves in the subjects differed greatly. A method for assessing the individual differences in parameters of skin conductance was proposed based on ranking by categories of values during continuous performance of tasks with different levels of complexity. This method allows comparing the levels of cognitive load in participants with different initial levels of skin conductance due to transition from the absolute to relative values at time intervals that are important for the researcher.

Quantitative comparison of the computational complexity of optical, digital and hybrid neural network architectures for image classification tasks.

By implementing neuromorphic paradigms in processing visual information, machine learning became crucial in an ever-increasing number of applications of our everyday lives, ever more performing but also computationally demanding. While a pre-processing of the information passively in the optical domain, before optical-electronic conversion, can reduce the computational requirements for a machine learning task, a comprehensive analysis of computational requirements for hybrid optical-digital neural networks is thus far missing. In this work we critically compare and analyze the performance of different optical, digital and hybrid neural network architectures with respect to their classification accuracy and computational requirements for analog classification tasks of different complexity. We show that certain hybrid architectures exhibit a reduction of computational requirements of a factor >10 while maintaining their performance. This may inspire a new generation of co-designed optical-digital neural network architectures, aimed for applications that require low power consumption like remote sensing devices.

Multi-contact planning and control for humanoid robots: Design and validation of a complete framework

In this paper, we consider the problem of generating appropriate motions for a torque-controlled humanoid robot that is assigned a multi-contact loco-manipulation task, i.e., a task that requires the robot to move within the environment by repeatedly establishing and breaking multiple, non-coplanar contacts. To this end, we present a complete multi-contact planning and control framework for multi-limbed robotic systems, such as humanoids. The planning layer works offline and consists of two sequential modules: first, a stance planner computes a sequence of feasible contact combinations; then, a whole-body planner finds the sequence of collision-free humanoid motions that realize them while respecting the physical limitations of the robot. For the challenging problem posed by the first stage, we propose a novel randomized approach that does not require the specification of pre-designed potential contacts or any kind of pre-computation. The control layer produces online torque commands that enable the humanoid to execute the planned motions while guaranteeing closed-loop balance. It relies on two modules, i.e., the stance switching and reactive balancing module; their combined action allows it to withstand possible execution inaccuracies, external disturbances, and modeling uncertainties. Numerical and experimental results obtained on COMAN+, a torque-controlled humanoid robot designed at Istituto Italiano di Tecnologia, validate our framework for loco-manipulation tasks of different complexity.

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

The geometry of cortical representations of touch in rodents.

Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.

Why increasing the number of raters only helps sometimes: Reliability and validity of peer assessment across tasks of different complexity

Number of raters is theoretically central to peer assessment reliability and validity, yet rarely studied. Further, requiring each student to assess more peers’ documents both increases the number of evaluations per document but also assessor workload, which can decline performance. Moreover, task complexity is likely a moderating factor, influencing both workload and validity. This study examined whether changing the number of required peer assessments per student / number of raters per document affected peer assessment reliability and validity for tasks at different levels of task complexity. 181 students completed and provided peer assessments for tasks at three levels of task complexity: low complexity (dictation), medium complexity (oral imitation), and high complexity (writing). Adequate validity of peer assessments was observed for all three task complexities at low reviewing loads. However, the impacts of increasing reviewing load varied by reliability vs. validity outcomes and by task complexity.

Challenging neurovascular coupling through complex and variable duration cognitive paradigms: A subcomponent analysis.

A similar pattern of cerebral blood velocity (CBv) response has been observed for neurovascular coupling (NVC) assessment with cognitive tasks of varying complexity and duration. This lack of specificity could result from parallel changes in arterial blood pressure (BP) and PaCO2, which could confound the estimates of NVC integrity. Healthy participants (n=16) underwent recordings at rest (5min sitting) and during randomized paradigms of different complexity (naming words (NW) beginning with P-, R-, V- words and serial subtractions (SS) of 100-2, 100-7, 1000-17, with durations of 5, 30 and 60s). Bilateral CBv (middle cerebral arteries, transcranial Doppler), end-tidal CO2 (EtCO2, capnography), blood pressure (BP, Finapres) and heart rate (HR, ECG) were recorded continuously. The bilateral CBv response to all paradigms was classified under objective criteria to select only responders, then the repeated data were averaged between visits. Bilateral CBv change to tasks was decomposed into the relative contributions (subcomponents) of arterial BP (VBP; neurogenic), critical closing pressure (VCrCP; metabolic) and resistance area product (VRAP; myogenic). A temporal effect was demonstrated in bilateral VBP and VRAP during all tasks (p<0.002), increased VBP early (between 0 and 10s) and followed by decreases of VRAP late (25-35s) in the response. VCrCP varied by complexity and duration (p<0.046). The main contributions to CBv responses to cognitive tasks of different complexity and duration were VBP and VRAP, whilst a smaller contribution from VCrCP would suggest sensitivity to metabolic demands. Further studies are needed to assess the influence of different paradigms, ageing and cerebrovascular conditions.

Athletes with high knee abduction moments show increased vertical center of mass excursions and knee valgus angles across sport-specific fake-and-cut tasks of different complexities.

Young female handball players represent a high-risk population for anterior cruciate ligament (ACL) injuries. While the external knee abduction moment (KAM) is known to be a risk factor, it is unclear how cutting technique affects KAMs in sport-specific cutting maneuvers. Further, the effect of added game specificity (e.g., catching a ball or faking defenders) on KAMs and cutting technique remains unknown. Therefore, this study aimed: (i) to test if athletes grouped into different clusters of peak KAMs produced during three sport-specific fake-and-cut tasks of different complexities differ in cutting technique, and (ii) to test whether technique variables change with task complexity. Fifty-one female handball players (67.0 ± 7.7 kg, 1.70 ± 0.06 m, 19.2 ± 3.4 years) were recruited. Athletes performed at least five successful handball-specific sidestep cuts of three different complexities ranging from simple pre-planned fake-and-cut maneuvers to catching a ball and performing an unanticipated fake-and-cut maneuver with dynamic defenders. A k-means cluster algorithm with squared Euclidean distance metric was applied to the KAMs of all three tasks. The optimal cluster number of koptimal = 2 was calculated using the average silhouette width. Statistical differences in technique variables between the two clusters and the tasks were analyzed using repeated-measures ANOVAs (task complexity) with nested groupings (clusters). KAMs differed by 64.5%, on average, between clusters. When pooling all tasks, athletes with high KAMs showed 3.4° more knee valgus, 16.9% higher downward and 8.4% higher resultant velocity at initial ground contact, and 20.5% higher vertical ground reaction forces at peak KAM. Unlike most other variables, knee valgus angle was not affected by task complexity, likely due to it being part of inherent movement strategies and partly determined by anatomy. Since the high KAM cluster showed higher vertical center of mass excursions and knee valgus angles in all tasks, it is likely that this is part of an automated motor program developed over the players' careers. Based on these results, reducing knee valgus and downward velocity bears the potential to mitigate knee joint loading and therefore ACL injury risk.

Measurement of the Cognitive Potential Based on Performance of Tasks of Different Complexity

The present article is devoted to measuring a person’s cognitive potential on the obtained experimental data to reveal its potentialities, as well as to monitor their dynamics, for example, for the diagnosis of recovery after an illness. This objective is divided in this study into two problems. Namely, estimation of the cognitive potential requires two algorithms to be developed: first, for assessment of the cognitive-complexity level of tasks and, second, for the system of cognitive-potential levels for an individual. The methods rely on a set of experimental techniques, including tailor-made proprietary ones, as well as on mathematical methods for data processing and calculation of introduced specific parameters for formalization of the cognitive potential. On the basis of these methods, methods (and specific formulas) are proposed for calculation of the cognitive potential of an individual using experimental data and tasks of different levels of complexity. As a part of this study, a methodology for determination of the cognitive potential is designed based on the theory of information patterns/representations. For objectification of cognitive skills (including so-called “soft skills”), a special-purpose web toolkit was developed. The obtained values allow one to study how social, genetic, and pathogenetic factors influence the dynamics of cognitive features. A new theoretical and technological platform for digital mapping and optimization of cognitive functions is proposed.

Recast, Task Complexity and Child Learners’ L2 Development

This paper investigated how production task combined with teacher feedback (in the form of recasts) affects child second language development, and the effects of task complexity on their production performance. 92 child learners of English in three intact classes were assigned to three tasks of different complexity (simple, +complex, ++complex). In response to errors in the use of the present third person singular verb forms, participants received the feedback of recasts. L2 development was measured through oral production and written tests. Results show that recasts are effective for learning the target form, and there is a negative correlation between task complexity and child learners&amp;rsquo; acquisition of linguistic target in their written production, but no significant correlation was found between task complexity and participants&amp;rsquo; oral production. The results confirm Skehan&amp;rsquo;s limited attention capacity prediction, and can be explained from the cognitive development level of the participants and their English learning habits. Implications for pedagogy and future research are discussed.

Effects of the Task Complexity on the Single Movement Response Time of Upper and Lower Limbs in Police Officers.

Police officers occasionally encounter belligerents resisting or even physically assaulting them without or with objects. The self-defense or legal utilization of use of force to disable the offender from harming an officer or others may depend on a single movement speed of hands and legs. This study investigated the effects of task complexity on a single movement response time of the upper and lower limbs in police officers. The sample consisted of 32 male police officers aged between 23 and 50 years. They performed a single movement as fast as possible with their upper and lower limb in three incrementally more complex tasks. In the first task, participants acted on a light signal and with their dominant limb they had to turn off the signal as fast as possible. In the second task, on the light signal, participants could turn off the light with free choice of the upper limb in a hand task or lower limb in a leg task. In the third task, participants had to turn the light off with the right limb if the light turned red and with the left limb if the light turned blue. The BlazePod device was used to assess the movement response time. The results show that there was a significant effect of task complexity on the single movement response time of the hand (F = 24.5, p < 0.001) and leg (F = 46.2, p < 0.001). The training of police officers should utilize specific and situational tasks to improve movement response time by improving the redundancy in decision-making processes during work-specific tasks of different complexity.

The Relationship between Task Complexity, Task Difficulty, and Speaking Performance: The Case of Chinese EFL Learners

In recent years, task-based language teaching (TBLT) has become a prevalent pedagogy in Chinese universities. Much research has focused on task complexity. Nevertheless, task difficulty, another crucial element of TBLT, has gained little attention from researchers. With twenty Chinese first-year undergraduates majoring in English participating in the present research, this study explored the relationship between task complexity, difficulty, and Chinese EFL learners’ English speaking performance, hoping to shed light on the design and implementation of tasks in L2 teaching. The results suggested that learners’ grammatical complexity, lexical sophistication, and accuracy varied significantly in the two tasks of different complexity. Only speaking speed was found to be positively correlated with task difficulty. When learners’ confidence to fulfill a task was bolstered, they would respond to the task at a faster speaking speed. Hence, teachers can design tasks of different complexity flexibly and fine-tune the complexity of the tasks to meet different teaching goals. Teachers can also strengthen the students’ confidence in accomplishing tasks to enhance their speaking fluency. Keywords: Chinese EFL learners, task-based language teaching, task difficulty, task complexity, linguistic complexity, accuracy, fluency DOI: 10.7176/JEP/13-20-08 Publication date: July 31 st 2022

Inhibitory Control and Brain-Heart Interaction: An HRV-EEG Study.

Background: Motor inhibition is a complex cognitive function regulated by specific brain regions and influenced by the activity of the Central Autonomic Network. We investigate the two-way Brain–Heart interaction during a Go/NoGo task. Spectral EEG ϑ, α powerbands, and HRV parameters (Complexity Index (CI), Low Frequency (LF) and High Frequency (HF) powers) were recorded. Methods: Fourteen healthy volunteers were enrolled. We used a modified version of the classical Go/NoGo task, based on Rule Shift Cards, characterized by a baseline and two different tasks of different complexity. The participants were divided into subjects with Good (GP) and Poor (PP) performances. Results: In the baseline, CI was negatively correlated with α/ϑ. In task 1, the CI was negatively correlated with the errors and α/ϑ, while the errors were positively correlated with α/ϑ. In task 2, CI was negatively correlated with the Reaction Time and positively with α, and the errors were negatively correlated with the Reaction Time and positively correlated with α/ϑ. The GP group showed, at baseline, a negative correlation between CI and α/ϑ. Conclusions: We provide a new combined Brain–Heart model underlying inhibitory control abilities. The results are consistent with the complementary role of α and ϑ oscillations in cognitive control.

Toward a Brain-Inspired Developmental Neural Network Based on Dendritic Spine Dynamics.

Neural networks with a large number of parameters are prone to overfitting problems when trained on a relatively small training set. Introducing weight penalties of regularization is a promising technique for solving this problem. Taking inspiration from the dynamic plasticity of dendritic spines, which plays an important role in the maintenance of memory, this letter proposes a brain-inspired developmental neural network based on dendritic spine dynamics (BDNN-dsd). The dynamic structure changes of dendritic spines include appearing, enlarging, shrinking, and disappearing. Such spine plasticity depends on synaptic activity and can be modulated by experiences-in particular, long-lasting synaptic enhancement/suppression (LTP/LTD), coupled with synapse formation (or enlargement)/elimination (or shrinkage), respectively. Subsequently, spine density characterizes an approximate estimate of the total number of synapses between neurons. Motivated by this, we constrain the weight to a tunable bound that can be adaptively modulated based on synaptic activity. Dynamic weight bound could limit the relatively redundant synapses and facilitate the contributing synapses. Extensive experiments demonstrate the effectiveness of our method on classification tasks of different complexity with the MNIST, Fashion MNIST, and CIFAR-10 data sets. Furthermore, compared to dropout and L2 regularization algorithms, our method can improve the network convergence rate and classification performance even for a compact network.

Classification and Volume Coordination Tasks of Organization Activity and Behavior

Based on the process approach, the article classifes the types of coordination tasks of different complexity and forms. Factors of influence on labor intensity of coordination tasks of activities and behavior are identifed. It is proposed to group factors of influence on the standard of manageability in connection with the solution of coordination tasks. The need and composition of the tasks of using informal relations and structure in coordination are justifed.

Evidence of cognitive specialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees.

Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help them to respond flexibly in scenarios with different resource availability. Individual learning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. Determining whether learning ability correlates between tasks of different complexity or between sensory modalities is of high interest for research on brain modularity and task-dependent specialization of neural circuits. The honeybee Apis mellifera constitutes an attractive model to address this question because of its capacity to successfully learn a large range of tasks in various sensory domains. Here, we studied whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relationships between objects) or an elemental olfactory task (a discrimination between two odorants). We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialization within the hive, which may contribute to ecological social success.

Cerebellar Dysfunction in Adults with Prader Willi Syndrome.

Severe hypotonia during infancy is a hallmark feature of Prader Willi syndrome (PWS). Despite its transient expression, moto development is delayed and deficiencies in motor coordination are present at older ages, with no clear pathophysiological mechanism yet identified. The diverse motor coordination symptoms present in adult PWS patients could be, in part, the result of a common alteration(s) in basic motor control systems. We aimed to examine the motor system in PWS using functional MRI (fMRI) during motor challenge. Twenty-three adults with PWS and 22 matched healthy subjects participated in the study. fMRI testing involved three hand motor tasks of different complexity. Additional behavioral measurements of motor function were obtained by evaluating hand grip strength, functional mobility, and balance. Whole brain activation maps were compared between groups and correlated with behavioral measurements. Performance of the motor tasks in PWS engaged the neural elements typically involved in motor processing. While our data showed no group differences in the simplest task, increasing task demands evoked significantly weaker activation in patients in the cerebellum. Significant interaction between group and correlation pattern with measures of motor function were also observed. Our study provides novel insights into the neural substrates of motor control in PWS by demonstrating reduced cerebellar activation during movement coordination.