Reviewerly/exHarmony huggingface dataset:
COMMUNITY CIRCLE AS A TEACHING STRATEGY FOR THE DEVELOPMENT OF PROSOCIAL DIALOGUE IN A FAMILY- TYPE PRIMARY SCHOOL

Aims:The case exploratory study, the subject of which are the teaching strategies of four respondents, is focused on the analysis of the activity of the community circle supporting communicative skills at the 1st stage of a family-type primary school in the context of teaching prosociality.The prosociality program according to PROT is compared with the support of the acquisition of communicative skills.The aim of the case study was to characterize how the processes of communication development in the community circle are managed in the school in the teaching of ethical education based on the observations of the 4th and 5th grade and interviews with four teachers who teach in the classroom.The research is accompanied by a description of the environment in which the observation and data collection took place.Methods: Observation, semi-structured individual interviews with teachers and content analysis of teaching artifacts.The segmented data were analyzed with the intention of comparing the congruence of teachers' strategies with the implemented teaching.Conclusion: Teachers expressed that they strategically include the activity of communication in a circle, which in their opinion develops the ability to listen and share their knowledge with the class team with confidence.In accordance with the expressed teaching strategies with observation, teachers organize a space for the development of communicative skills in the context of prosociality.He tries to support mutual communication between classmates throughout the day.Targeted development of communication skills in the context of prosociality took place in community circle during the final evaluation, recommendations and peer learning.
Dynamic A* Algorithm to Improve Dynamic Path Planning of Unmanned Epidemic Prevention and Killing Vehicles

Since personnel in complex regions or contaminated areas cannot enter to achieve epidemic prevention and killing operations, the application requirements for self-propelled epidemic prevention robots are becoming more and more extensive. The path planning algorithm is a key technology for robots in the eradication and epidemic prevention, but some node information in the original map will change in real time during the eradication process, which greatly reduces the robot's ability to work in epidemic prevention. This article first designed a relatively complex 900×900 point map and implemented the dynamic path planning of the Dynamic A * (D*) algorithm using the python language. The simulation results show that the algorithm greatly shortens the time of secondary path planning after encountering obstacles, and improves the reaction speed of the robot in epidemic prevention. Preliminary verification of the feasibility of D* algorithm in the path planning of self-propelled anti-epidemic robots.
A Random Multi-channel Spectral Graph Convolutional Network

Graph neural networks (GNNs) have been widely used to process graph data. However, the ability of most traditional graph models to integrate node features and topology is not ideal. In this case, the Adaptive Multi-channel Graph Convolution Networks (AMGCN) model is proposed to redesign the model structure. It can greatly improve the fusion ability of topology and node features in complex graphs with rich information. Nevertheless, this model cannot solve the problems of over-smoothness and non-robustness as the depth of the model increases. To address this issue, we propose A Random Multi-channel Spectral Graph Convolution Network (RMSGCN). It adopts a random propagation strategy to reduce potential noise interference from high-order neighbors. Moreover, this model introduces simple spectral graph convolution (SSGC) to enhance the model's ability in deep convolution. A large number of experiments on five data sets show that our model has better performance. It has better expressive power in deep convolution.
Modeling a Cyber Defense Business Ecosystem of Ecosystems

Providing cyber defense in a country is complex. It involves ensuring the security of various products and services that are part of a global supply chain. In this complex scenario, the challenge is the development of a cyber defense business ecosystem that, reaching a minimum level of maturity, guarantees the security of products and services in cyberspace. This work proposes a cyber defense business ecosystem of ecosystems (BEoE) model with two ecosystems that must be created or fostered, the human resources training ecosystem and the product and service homologation and certification ecosystem. These two cyber defense ecosystems are key to the sustainable growth of an entire chain of production and sourcing of cyber defense goods and services. The proposed model allows the Cyber Defense BEoE to evolve, so that different actors (companies and government agencies) with different levels of maturity in defense and cybersecurity may emerge. In this way, a country's Cyber Defense BEoE may be able to provide products and services at different levels of security for its defense system.
Hidden Markov Model-Based Video Recognition for Sports

In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.
Identifying Learning Standards of Artificial Neural Networks: A Study

Artificial neural networks (ANNs) are now omnipresent, and there is continuous growth in excellent research to create new ones. ANN is capable of adaptive, dynamic learning. In ANN learning, we need to change the weights to enhance the input/output behavior. Therefore, with the help of which the weights can be adjusted, a method is required. These strategies are called learning laws, which are simply formulas or algorithms. Rules of learning are algorithms or mathematical logic that guides modifications is the weight of the network links. By employing the disparity between both the expected output and the real outcome to update its weights during training, they incorporate an error reduction mechanism. Learning laws improve the effectiveness of the artificial neural network and extend this rule to the network. Usually, the learning rule is repeatedly applied to the same set of training inputs over a large number of cycles, with error steadily decreasing over epochs. They are fine-tuned as the weights are. The present research strives, however, to assess the objective of the artificial neural network and the learning principles. In this, we analyzed ten different regulations studied by leading researchers that include rules based on supervised learning (perceptron, memory-based, delta, error correction, correlation, out star, supervised Hebbian) and unsupervised rules based on learning (competitive, competitive Hebbian, Hebbian); this defines how to adjust the weight of the nodes of a network.
AWP-GAC: central-controlled actor-critic for multi-agent dynamic game environment

We propose a deep reinforcement learning framework for dynamic game domains. We start from analyzing the difficulty of existing algorithms in the multi-agent dynamic game case: multiple agents will affect each other by the dynamic update process, resulting in the lack of a stable learning environment. We propose a distributed learning framework, in which a global network controls parallel actor-critic modules to adjust the learning and update process, thereby improve the environment stability. Additionally, we introduce a method to adaptive adjust the learning-rate, which also enhance the stability of the learning process. Furthermore, we show the strength of our approach compared to existing methods in a multi-agent dynamic game scenarios.
Comparative study of 3D reconstruction methods based on Fast-MVSNet and MVSNet

This paper introduces and compares the differences and connections between MVSNet(ECCV2018) and Fast-MVSNet(CVPR2020). The emergence of MVSNet is a pioneer in the field of 3D reconstruction based on depth estimation, but its disadvantage is that it occupies too much GPU and the processing time is not ideal. Compared with the reconstruction methods of MVSNet and Fast-MVSNet. Firstly, a coarse to thin frame is designed and a sparse Cost Volume is constructed for depth map estimation. Then, the propagation of the sparse high-resolution depth map is encrypted by joint bilateral upsampling of the original image.
Ontology Design of Distributed Computing System Based on Hierarchical Federated Learning

As a popular distributed learning framework, federated learning is widely used in the training process of distributed models. And as the intelligent core of the distributed system, the intelligent inference model of each computing node must have the ability to continuously evolve with the real scenario. Under the traditional federated learning framework, whether the model participates in the update process depends on whether it is selected by the server, which is contrary to reality. Therefore, focusing on real-world needs, this paper constructs a distributed intelligent computing ontology based on hierarchical federated learning, which regarding each computing device in hierarchically distributed systems as an ontology node, and on this basis builds a distributed computing node resource model. In view of the heterogeneous resources of each node, the ontology can adaptively choose whether to participate in the training process of federated learning according to the resource state of the node itself, which improves the model update and resource utilization efficiency.
A Token-based Authentication and Key Agreement Protocol for Cloud Computing

Cloud computing has been widely used as an effective way to effectively solve the problem that local devices cannot afford to store or process large amounts of data. However, cloud computing also faces many security issues, especially the issue of access rights. In order to solve this problem, a large number of authentication and key agreement protocols have been proposed. However, these protocols still have problems such as not being lightweight enough. Therefore, this paper proposes a token-based authentication and key agreement protocol. When the terminal device performs mutual authentication with the cloud server during the validity period of the token, the computational cost can be greatly reduced. The comparison with other related protocols also shows that the computational cost of our protocol is lower.
An Interpretation of Convolutional Neural Networks for Motif Finding from the View of Probability

The powerful learning ability of a convolutional neural network to perform functional classification provides valuable clues for the discovery of biological problems and image recognition. However, the mechanism of deep learning still lacks satisfying interpretation and the convolutional neural network is usually regarded as a black-box model. Consequently, it is challenging to design deep learning models and adjust the hyper-parameters for specific tasks without theoretical guidance. We raise a new task of deep learning – 2-dimensional logo detecting for computer vision and develop a novel model of convolutional neural networks to solve the task. Furthermore, we interpret this specific model from the point of view of statistical learning. With the guidance of statistical interpretation, we can design the model structure more efficiently and obtain better performances.
Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images

BACKGROUND: Nuclear cardiology uses to diagnose the cardiac disorders such as ischemic and inflammation disorders. In cardiac scintigraphy, unraveling closely adjacent tissues in the image are challenging issue. AIM: The aim of the study is to detect of cardiac tissues using K-means analysis methods in nuclear medicine images. This study also aimed to reduce the existence of fleck noise that disturbs the contrast and make its analysis more difficult. METHODS: Thus, digital image processing uses to increase the detection rate of myocardium easily using its color-based algorithms. In this study, color-based K-means was used. The scintographs were converted into color space presentation. Then, each pixel in the image was segmented using color analysis algorithms. RESULTS: The segmented scintograph was displayed in distinct fresh image. The proposed technique defines the myocardial tissues and borders precisely. Both exactness rate and recall reckoning were calculated. The results were 97.3 + 8.46 (p &gt; 0.05). CONCLUSION: The proposed technique offered recognition of the heart tissue with high exactness amount.
Multi-task Scale Adaptive Ladder Network for Crowd Counting

As the population increases, problems such as crowds and traffic jams have emerged one after another. How to effectively achieve accurate human flow monitoring has become an urgent problem of today’s society. This paper proposes a multi-task scale adaptive ladder network (MT-SALN) for generating high-accuracy crowd density maps. This network, based on VGG-16 network, consists of several sets of Adaptive Dilated-Convolution Module (ADCM), a Position Recalibration Branch (PRB) and a Density Estimation Branch (DEB). We employ ADCM in different stages to broaden the width of the network and introduce weights for each channel parameter through an attention mechanism. The residual structure enables the network model to have a back propagation ability even though the number of network layers is large. In addition, transposed convolution is used to upsample the features so that they can be merged with other layers’ features to generate a more refined density map with high resolution. The existence of PRB can effectively guide the network to generate crowd density at the correct location and accelerate network convergence. The ladder architecture is beneficial to produce high-quality density maps. Extensive experiments on challenging crowd counting datasets (UCF_CC_50, Shanghaitech) demonstrate the effectiveness of the proposed approach.
Measuring the Impact of Memory Replay in Training Pacman Agents using Reinforcement Learning

Reinforcement Learning has been widely applied to play classic games where the agents learn the rules by playing the game by themselves. Recent works in general Reinforcement Learning use many improvements such as memory replay to boost the results and training time but we have not found research that focuses on the impact of memory replay in agents that play simple classic video games. In this research, we present an analysis of the impact of three different techniques of memory replay in the performance of a Deep Q-Learning model using different levels of difficulty of the Pacman video game. Also, we propose a multi-channel image - a novel way to create input tensors for training the model - inspired by one-hot encoding, and we show in the experiment section that the performance is improved by using this idea. We find that our model is able to learn faster than previous work and is even able to learn how to consistently win on the mediumClassic board after only 3,000 training episodes, previously thought to take much longer.
Hierarchical Cluster-Based Model to Evaluate Accuracy Metrics Based on Cluster Efficiency

Clustering is a crucial step in data analysis refining process. Predictive analytics is carried out using a variety of clustering approaches, including partitioning clustering, hierarchical clustering, grid-based clustering, model-based, graph-based clustering, and intensity clustering, among others. The hierarchical approach allows one to cluster data objects into a tree-like structure. The cluster is the name given to each node in the hierarchy. There are two types of hierarchical clustering: agglomerative clustering and divisive clustering. Clustering by agglomeration is often desirable. The performance of the clusters ought to be good for a successful cluster analysis. In this document, we will be using three components to assess cluster reliability: cohesion calculation, silhouette index, and length of time.
Exploring the Life-Political of Elementary School Students

The purpose of this study is to explore the life-political characteristics of elementary school students through practice cases of decision-making processes in schools. To this end, 29 sixth-grade students attending ○○ Elementary School in Paju, Gyeonggi-do, filled out pre-question and post-question papers. and, the researcher conducted an interview to understand the characteristics of elementary school students in the decision-making process. The researcher analyzed the data collected in the research process from a life-political perspective. The results are as follows. First, the position of elementary school students in the decision-making process expressed to solve problems that have occurred in school and the resulting practice patterns may appear differently. Second, students considered the elements of
Apollo

This paper provides a first presentation of Apollo, an orchestration framework for serverless function compositions distributed across the cloud-edge continuum. Apollo has a modular design that enables a fine-grained decomposition of the runtime orchestration (scheduling, data transmission, etc.) of applications, so that each of the numerous orchestration decisions can be optimized separately, fully exploiting the potential for the optimization of performance and costs. Apollo features (a) a flexible model of the application and the available resources and (b) a decentralized orchestration scheme carried out by independent agents. This flexible structure enables distributing not only the processing but also the orchestration process itself across a large number of resources, each running an independent Apollo instance. In combination with the ability to execute parts of the application directly on the host of each Apollo instance, this unleashes a significant potential for cost and performance optimization by leveraging data locality. Apollo's efficiency and its potential for application performance improvement are demonstrated in a series of experiments---for both synthetic and real function compositions---where Apollo's capability for flexible distribution of tasks between local containers and serverless functions enables a significant application speedup (up to 20X).
MeLOD

In the last few years, many applications for mobile devices have been developed to support learning experiences both in formal and informal contexts. One of the main limits of these applications concerns the development of learning materials suitable for mobile learning contexts. In fact, learning content must be usually prepared in advance by teachers and maintained during the whole lifespan of the application. In this paper, we present MeLOD, a mobile learning environment, which exploits the huge amount of dataset in the Linked Open Data (LOD) cloud to overcome the previous issue, and provides contextualized and continuously updated information based on students’ location. The position of the student sent by the mobile device is used to interlink Geonames DBpedia and Europeana datasets to provide information about all the interesting cultural heritage sites close to the student. Moreover, students social activities like voting and commenting are used to enhance the knowledge base of the environment and to provide recommendations for next students’ visits.
Understanding Bias and Variance of Learning-to-Rank Algorithms: An Empirical Framework

Learning-to-rank (LtR) algorithms are at the heart of modern day information retrieval systems. While a good number of LtR algorithms have been developed and scrutinized over the past decade, theoretical underpinnings of these algorithms are not thoroughly investigated so far. Amongst the theoretical aspects of a supervised learning algorithm, the bias-variance profiles are of utmost importance. In this article we aim to better understand the bias-variance profiles of rank-learning algorithms. Firstly, we formalize the bias and variance from a pointwise perspective where each query-document pair is treated as an individual training example. Secondly, we develop a framework to analyze the variability and systematic error of a rank-learner in terms of its ranking error, i.e., we analyze the bias-variance from a listwise perspective where a query and all of its associated documents are treated as a single training example. After developing the theoretical framework, we move on to test its applicability in practice. In particular, we choose a promising algorithm, namely random forest-based rank-learning algorithms for our investigation. We study the effect of varying an important parameter of the algorithm, namely the sub-sample size used to learn each tree, on bias and variance. Our hypothesis is that as the sub-sample size (per tree) increases, classical bias-variance tradeoff should be observed. On two large LtR benchmark datasets, experimental results show that our hypothesis holds true. We also explain the relative performance of two of the top performing LtR algorithms using their bias and variance profiles. To the best of our knowledge, this article presents the first thorough investigation into bias and variance analysis of rank-learning algorithms.
Multi-resource Power Efficient Virtual Machine Placement in Cloud Computing

Virtual machine placement (VMP) has become a primary concern of researchers in cloud computing environments. VMP algorithms can significantly affect the power consumption in cloud data centers. In this paper, an efficient heuristic VMP algorithm is designed. The proposed algorithm is based on multi-recourse virtual machine demands. The concept of placement is to calculate the total normalized resource demands of a single VM termed as capacity resource and use it with heuristic algorithms. As a result, a balance resource usage is preserved, and an efficient placement is achieved. The conventional first fit decreasing (FFD) and best fit decreasing (BFD) heuristic algorithms are modified using the new capacity resource factor. The new proposed algorithms are termed first fit decreasing capacity (FFDC) and best fit decreasing capacity (BFDC) algorithms. In the simulation, the proposed FFDC and BFDC algorithms are evaluated and compared with the classical FF, BF, FFD, BFD algorithms. Moreover, a comparison with two recently proposed metaheuristic algorithms is achieved. The simulation results show the effectiveness of the proposed algorithms in minimizing the total power consumption.