Different Kinds Of Tasks Research Articles

МЕТОДИКА МУЗЫКАЛЬНО-РИТМИЧЕСКОГО ВОСПИТАНИЯ СЛАБОВИДЯЩИХ ДЕТЕЙ ДОШКОЛЬНОГО ВОЗРАСТА НА ОСНОВЕ ПЕДАГОГИЧЕСКОЙ СИСТЕМЫ КАРЛА ОРФА

The article reveals the methods of musical-rhythmic education of preschool children with visual disabilities on the basis of pedagogical system by Karl Orff in the process of musical lessons in kindergarten. This method includes the development of speech, figurative thinking, coordination of movements, the development of communication skills, emotionality and creativity of children with visual disabilities. The method provides different musical quests using simple literary text, that allows children to sing along with the teacher, thereby developing their speech; literary and musical images, displayed by various attributes, which children are able to understand, and also tasks for the development of children’s spatial coordination. The method involves different kinds of tasks, such as “Songs with replacement”, in which words are replaced by defined movements, aiming to develop coordination, attention and mind, as well as creative participation of all children in the execution of the musical composition. The tone of voice of the teacher, his manner of communication with children are of great importance along with the laws of perception: a) Tell me an I forget, b) Show me and I remember, c) Put me in the process and I understand.

Network attributes describe a similarity between deep neural networks and large scale brain networks

AbstractDespite the recent success of deep learning models in solving various problems, their ability is still limited compared with human intelligence, which has the flexibility to adapt to a changing environment. To obtain a model which achieves adaptability to a wide range of problems and tasks is a challenging problem. To achieve this, an issue that must be addressed is identification of the similarities and differences between the human brain and deep neural networks. In this article, inspired by the human flexibility which might suggest the existence of a common mechanism allowing solution of different kinds of tasks, we consider a general learning process in neural networks, on which no specific conditions and constraints are imposed. Subsequently, we theoretically show that, according to the learning progress, the network structure converges to the state, which is characterized by a unique distribution model with respect to network quantities such as the connection weight and node strength. Noting that the empirical data indicate that this state emerges in the large scale network in the human brain, we show that the same state can be reproduced in a simple example of deep learning models. Although further research is needed, our findings provide an insight into the common inherent mechanism underlying the human brain and deep learning. Thus, our findings provide suggestions for designing efficient learning algorithms for solving a wide variety of tasks in the future.

Cross-device task interaction framework between the smart watch and the smart phone

Along with the increasing emergence of smart watch devices, new interaction techniques between these devices and smart phones have been proposed to leverage their availability. However, there are few existing studies on enhancing smart watches’ current functions: working as an information processing system for users’ phones in terms of both browsing and handling short information. In this work, we introduce a framework for handling tasks between the smart phone and the smart watch, including task delegation and task management, that enables the smart watch to temporarily handle lightweight jobs from the smart phone. By utilizing this framework, people can save valuable phone screen space for more complicated or important tasks. Moreover, considering users’ multi-tasking behaviors, we also provide some example situations where users use both their phone and smart watch to handle different kinds of tasks simultaneously. The user study illustrates that lightweight jobs can be efficiently handled by users on the watch.

Judging the subjective difficulty of different kinds of tasks.

People judge the relative difficulty of different kinds of tasks all the time, yet little is known about how they do so. We asked university students to choose between tasks that taxed perceptual-motor control and memorization to different degrees. Our participants decided whether to carry a box through a wide (81 cm) or narrow (36 cm) gap after memorizing six, seven, or eight digits. The model that maximized the likelihood of observing the choice data treated the extra physical demand of passing through the narrow gap as functionally equivalent to memorizing an extra .55 digits. Substantively, the model suggested that participants judged the difficulty of the compound tasks in terms of separate resources. The approach introduced here may help interrelate different kinds of task difficulty. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Does task sustainability provide a unified measure of subjective task difficulty?

What accounts for the subjective difficulty of a task? It is easy to suggest ad hoc measures, such as how many individuals can do the task, how long it takes them to do it, how likely they are to complete it, how much attention it requires, and so on. But having such ad hoc measures may miss the point that it is possible to judge the relative difficulty of different kinds of tasks, suggesting that there may be a common basis for judging task difficulty. If there is such a common basis, it might be used to compare the difficulties of different kinds of task. We tested two hypotheses about what the common basis might be. One was that time serves this role. This hypothesis was attractive because time is amodal and previous studies have provided support for the hypothesis that time might be an index of task difficulty. The other hypothesis was new. According to the new hypothesis, the subjective difficulty of a task corresponds to its estimated sustainability. We obtained results consistent with the time hypothesis, but our data were less supportive of the sustainability hypothesis.

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle.

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios.

Children and adults successfully comprehend subject-only sentences online.

In many offline studies, children show selectively better comprehension of sentences with the focus particle only when it modifies the object argument (Jane only ate an apple) than they do when it modifies the subject argument (Only Jane ate an apple). Here we explore the nature of this asymmetry by examining performance in a different kind of task: the moment-to-moment comprehension of unambiguous sentences. If past errors reflect a fundamental difference in representation or complexity of computation, we would expect the same asymmetry in this task. We observed that adults were able to successfully predict the target referent for both types of only-sentences, as indicated by anticipatory looks, while 6- to 8-year-old children could do so only for subject-modifying only-sentences. These findings suggest that much of the asymmetry in past work may be due to task demands. We discuss the implications of these results for children’s syntactic and pragmatic development.

Optimization model and online task interleaving scheduling algorithm for MIMO radar

Abstract As the preemptive single-machine scheduling problem, a variety of tasks, each of which consists of three phases: the transmit interval, the wait interval and the receive interval, to be performed by a single multiple input multiple output (MIMO) radar within a finite time horizon needs the efficient scheduling algorithm. However, due to the special signal processing characteristics, traditional task scheduling algorithms for the phased array radar can’t be applied to the MIMO radar to release its full multifunctional potential. Additionally, the real-time demand of the radar system must be taken into consideration when designing scheduling algorithms. Therefore, the optimization task scheduling model is established and a novel heuristic task interleaving scheduling algorithm is proposed. The full MIMO radar task structure is considered in a way that the wait interval can be utilized to transmit or receive subtasks. In addition, in order to sufficiently make full use of each subtask, different transmit intervals are incorporated into a whole dwell and different receive intervals are overlapped. The proposed algorithm decomposes the task schedulability analysis into the time constraint analysis and the energy constraint analysis, and adaptively interleaves different kinds of tasks. The simulation results show that the proposed algorithm can provide better performance compared with the four existing scheduling algorithms while maintaining a reasonable runtime.

New Directions in Self-Regulation: The Role of Metamotivational Beliefs

Research on self-regulation has primarily focused on how people exert control over their thoughts, emotions, and behavior. Less attention has been paid to the ways in which people manage their motivational states in the service of achieving valued goals. In this article, we explore an emerging line of research that focuses on people’s beliefs about their own motivation (i.e., their metamotivational knowledge), as well as the influence these beliefs have on their selection of regulatory strategies. In particular, we review evidence showing that people are often quite sensitive to the fact that distinct motivational states (e.g., eagerness vs. vigilance) are adaptive for different kinds of tasks. We also discuss how other metamotivational beliefs are inaccurate on average (e.g., beliefs about how rewards affect intrinsic motivation). Finally, we consider the implications of metamotivation research for the field of self-regulation and discuss future directions.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This “failure” to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Building Ontology For Library Management System Using Dewey Decimal Classification Scheme

The internet is playing a very important role in communication and sharing knowledge today. Users of the Internet use web for different kinds of tasks. The current web mostly restricts processing of information to manual keyword searching and becomes the reason for irrelevant information retrieval. This problem can be overcome by using semantic web. Semantic web is intelligent and meaningful web. It describes the things in a way that the computer can understand. Ontology plays a vital role in achieving goal of semantic web i.e. how to use and reuse knowledge that can be communicated across applications systems. For the development of ontology, the most important thing is classes and the resulting class and subclass relationships. Classification schemes have been used persistently to represent various kinds of human knowledge. Dewey Decimal Classification (DDC) scheme provides list of classes for all kind of books. It was invented by Aristotle in the 4th century BC. Currently, this scheme is used by most of libraries in the world. In our work we are extracting the classes from DDC scheme for building ontology and converting the classes into ontology. We developed a hierarchical structure comprised of a class-subclass relationship as well as relation among hierarchy levels and properties. This paper presents the potential of ontologies applicable to semantic web. The proposed ontology has been used to develop a library management system (LMS) and produce excellent results in terms of efficiency and ease of use.

La Croce rossa italiana nel '15-'18 tra azione e rappresentazione

During the First World War the Italian Red Cross mobilised to an extent never seen before to meet the needs of a totally new type of war, however for some time the association had already been engaged in developing different kinds of tasks for peace time. The present document illustrates the activities carried out by the Italian Red Cross between 1915 and 1918 and gives account of the positive impact of its endeavour. However there were several difficulties and setbacks not only for the Italian Red Cross but also for the Military Health Service, in fact the then prime minister Giolitti prompted in 1920 a parliamentary enquiry on war expenses. Deputies of the opposition demanded that the enquiry be extended also to the Red Cross, that, besides the thousands of members adhering to its humanitarian goals, which were represented by its flag, received also the active support of writers, intellectuals and painters who dedicated to it some of their works. Beyond the aura of respect and sacredness that it inspired, supporters attached to it different connotations, while the governments that were in office in those years charged it with varied tasks and missions.

La reparación en la interacción oral de estudiantes de ELE: comparación entre interacciones de práctica en el aula e interacciones en contextos de evaluación

Abstract Based on the concept of interactional competence (Hellermann, 2008; Seedhouse, 2004; Walsh, 2011; Young, 2008), our study analyzes how learners of Spanish as a foreign language in a B2 level manage repair in oral interaction in language classrooms. We understand repair as “the treatment of trouble in talk-in-interaction” (Young, 2008, p. 49). A corpus of eleven interactions between students in the classroom is analyzed through the perspective of Conversation Analysis. The interactions were collected in different kinds of tasks in the language classroom. In our analysis we compare interactions produced in practice activities and interactions collected during assessment. The findings in this study show a tendency to manage repair in classroom oral interaction as it would be done in normal conversation. Regarding the different contexts of our corpus, we observe that, in interactions produced in assessment contexts, speakers try to protect their interlocutor’s face and their own face by avoiding to make repairs.

How are cognitive and physical difficulty compared?

Tasks that require less physical effort are generally preferred over more physically demanding alternatives. Similarly, tasks that require less mental effort are generally preferred over more mentally demanding alternatives. But what happens when one must choose between tasks that entail different kinds of effort, one mainly physical (e.g., carrying buckets) and the other mainly mental (e.g., counting)? We asked participants to choose between a bucket-carrying task and a counting task. Our participants were less likely to choose the bucket task when it required a long reach rather than a short reach, and our participants were also less likely to choose the bucket task the smaller the final count value. We tested the hypothesis that subjective task durations provided a common currency for comparing the difficulties of the two kinds of tasks. We found that this hypothesis provided a better account of the task choice data than did an account that relied on objective task durations. Our study opens the door to a new problem in the study of attention, perception, and psychophysics-judging the difficulty of different kinds of tasks. The approach we took to this problem, which relies on two-alternative forced choice along with modeling the basis for the choice, may prove useful in future investigations.

Dealing with On-Line Human-Robot Negotiations in Hierarchical Agent-based Task Planner

Collaboration between humans and robots to accomplish different kinds of tasks has been recently studied as a planning problem and several techniques have been developed to define and generate shared plans where humans and robots collaborate to achieve a common goal. However, current methods require the knowledge of the human about the plan under execution and an agreement between users and robots about their roles before the execution of the plan. In this paper, we propose an extension to the Hierarchical Agent-based Task Planner (HATP) that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan. The proposed method is based on the automatic generation of a conditional plan in which missing information is acquired at execution time by means of sensing actions. The proposed method has been fully implemented and tested on a real robot performing collaborative tasks in an office-like environment.

Saccades to Explicit and Virtual Features in the Poggendorff Figure Show Perceptual Biases.

Human participants made saccadic eye movements to various features in a modified vertical Poggendorff figure, to measure errors in the location of key geometrical features. In one task, subjects (n = 8) made saccades to the vertex of the oblique T-intersection between a diagonal pointer and a vertical line. Results showed both a small tendency to shift the saccade toward the interior of the angle, and a larger bias in the direction of a shorter saccade path to the landing line. In a different kind of task (visual extrapolation), the same subjects fixated the tip of a 45° pointer and made a saccade to the implicit point of intersection between pointer and a distant vertical line. Results showed large errors in the saccade landing positions and the saccade polar angle, in the direction predicted from the perceptual Poggendorff bias. Further experiments manipulated the position of the fixation point relative to the implicit target, such that the Poggendorff bias would be in the opposite direction from a bias toward taking the shortest path to the landing line. The bias was still significant. We conclude that the Poggendorff bias in eye movements is in part due to the mislocation of visible target features but also to biases in planning a saccade to a virtual target across a gap. The latter kind of error comprises both a tendency to take the shortest path to the landing line, and a perceptual error that overestimates the vector component orthogonal to the gap.

Information Based Universal Feature Extraction in Shallow Networks

In many real-world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, either the features are given by common sense like edges or corners in image analysis, or they are directly determined by expertise. They mostly remain task-specific, although human may learn the life time, and use different features too, although same features do help in recognition. It seems that a universal feature set exists, but it is not yet systematically found. In our contribution, we try to find such a universal image feature set that is valuable for most image related tasks. We trained a shallow neural network for recognition of natural and non-natural object images before different backgrounds, including pure texture and handwritten digits, using a Shannon information-based algorithm and learning constraints. In this context, the goal was to extract those features that give the most valuable information for classification of the visual objects, hand-written digits and texture datasets by a one layer network and then classify them by a second layer. This will give a good start and performance for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract unique features which are valid in all three different kinds of tasks. They give classification results that are about as good as the results reported by the corresponding literature for the specialized systems, or even better ones.

Формирование креативной компетентности будущего воспитателя дошкольной образовательной организации в рамках вариативного образования

The article is devoted to consideration of issues relating to the formation of creative competence of the future teacher of preschool educational organizations in the framework of the variable education, the need for which is attributable to the modern requirements of the profession. Variability of education actualizes the training of professionals involved in the education of future generations from the position of the development of their creative potential, empower personal development in various directions, ability to solve different kinds of tasks, organize educational activities in preschool educational organizations subject to the requirements of modern standards. The variability of the process of formation of creative competence of the future teacher is presented through the substantial and organizational aspects. The first aspect is characterized by research activity in the process of classroom and extracurricular work. The potentialities of scientific research in the process of the formation of competence. The second aspect is implemented using the interaction of actors in research activities and research collaboration, which involves the passing of phases: end-to-end (monitoring and dialogue) and serial ("meeting", understanding, thinking, activities) that are methodologically based on the concepts of the hermeneutic approach. Described in detail the nature and organization of each phase of a scientific collaboration with the aim of forming creative competence of future teacher of preschool educational institution.

Commentary: Promoting systems understanding

This commentary on the special issue, entitled Models and Tools for Systems Learning and Instruction, highlights the accomplishments of the papers in this collection. It also points to some avenues for further strengthening research on promoting systems understanding. Collectively, the papers make advances in our understanding of how to design learning environments to foster systems understanding, how to develop teacher adeptness at orchestrating learning in these environments, and what processes mediate learning within the environments. The papers represent a diversity of methods for measuring learners’ systems understanding, with different kinds of tasks and largely different coding categories for classifying responses; research could be advanced through systematic investigation of these different types of assessment and how they affect findings. Further progress could also be achieved through more detailed descriptions of instructional interventions and through incorporating more design-based research techniques to identify which features of learning environments are responsible for successes and failures in students’ learning.

Effects of Different Types of Virtual Reality Display on Presence and Learning in a Safety Training Scenario.

The increasing availability of head-mounted displays (HMDs) for home use motivates the study of the possible effects that adopting this new hardware might have on users. Moreover, while the impact of display type has been studied for different kinds of tasks, it has been scarcely explored in procedural training. Our study considered three different types of displays used by participants for training in aviation safety procedures with a serious game. The three displays were respectively representative of: (i) desktop VR (a standard desktop monitor), (ii) many setups for immersive VR used in the literature (an HMD with narrow field of view and a 3-DOF tracker), and (iii) new setups for immersive home VR (an HMD with wide field of view and 6-DOF tracker). We assessed effects on knowledge gain, and different self-reported measures (self-efficacy, engagement, presence). Unlike previous studies of display type that measured effects only immediately after the VR experience, we considered also a longer time span (2 weeks). Results indicated that the display type played a significant role in engagement and presence. The training benefits (increased knowledge and self-efficacy) were instead obtained, and maintained at two weeks, regardless of the display used. The paper discusses the implications of these results.