Views from the Inside: Roles of Deputy Directors in Early Childhood Education in Finland

This study explored the perceptions of deputy directors about their leadership in Early Childhood Education (ECE) centres in Finland. Our aim was to look beyond task distribution and understand how deputy directors enacted leadership with their colleagues. Six deputy directors employed in one municipality in Finland participated in this study. Interviewed individually, the participants discussed how they themselves perceived being in a leadership position and what their leadership looked like in practice. The emphasis they placed on the various relationships highlight the importance of paying attention to the relational dynamics amongst staff within a centre, taking into account both formal and informal authority. Given the increasing global interest in understanding leadership enactment within ECE centres, and its connection with quality service provision, knowledge of the positional leadership roles of deputy directors is of importance to the ECE sector. This is one of the first studies dedicated to exploring the work of ECE deputy directors.

Using the Hopfield Neural Network to Select a Behaviour Strategy for the Group of Mobile Robots

The use of the Hopfield neural network for the task distribution problem solving in teams of mobile robots performing monosyllabic operations in a single workspace is considered. The study is a continuation of earlier works in which the same problem was solved by the authors using other heuristic algorithms – swarm and genetic. This article presents the problem statement and the model of the working space, distinguishes the goals of robotic operation. The quality indicator is the total distance traveled by each of the robots in the group. To enable the original problem to be solved using the Hopfield neural network, a graph representation of the Hopfield is made by switching from the VRP to the TSP problem. The results of computational experiments confirming the effectiveness of the chosen approach for choosing a strategy of behavior of a group of mobile robots are shown.

Automatic control of the distribution of tasks under conditions of uncertainty with the use of adaptive approach

The subject of this article is the development of an adaptive approach for the optimal distribution of problems among solvers in conditions of uncertainty. Despite the large amount of research related to the construction of solutions for automatic control of task distribution, this issue remains relevant. As an alternative approach, a multi-level adaptive algorithm is proposed, which at each level filters incoming tasks according to solution methods, thereby significantly reducing the computational load. A distinctive feature of this algorithm is taking into account the time of task preprocessing, in particular, related to the current load of solvers and the distribution of tasks by solvers, in accordance with the maximum load.

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.

Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various task-shared inductive bias (meta-knowledge) to solve such tasks, and achieve impressive performance. However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models. In this work, we aim to improve the robustness of the inductive bias through task augmentation. Concretely, we consider the worst-case problem around the source task distribution, and propose the adversarial task augmentation method which can generate the inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their cross-domain generalization capability. We conduct extensive experiments under the cross-domain setting, using nine few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and ChestX. Experimental results show that our method can effectively improve the few-shot classification performance of the meta-learning models under domain shift, and outperforms the existing works. Our code is available at https://github.com/Haoqing-Wang/CDFSL-ATA.

Computing Resource Allocation Scheme for DAG-Based IOTA Nodes

IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.