Reviewerly/exHarmony huggingface dataset:
The Effect of Fine-Grained Control of Workflow on Informal Learning: The Mediating Effect of Teacher Burnout

Workflow fine control is an important research tool for the mediating effect of teacher burnout. Based on the fine control theory of workflow, this paper constructs a mediating effect model of teacher burnout in primary and secondary school teachers from the perspective of informal learning. The model designs a measurement scale based on the results of fine control, and the scale is revised through informal learning pretests to form the logical frame analysis loop of this paper. The scale uses Likert 5-point scoring, from 1.5 to represent never, rarely, occasionally, often, and always, which solves the difficulty of quantitative analysis of teacher burnout data. During the simulation process, SPSS 20.0 and AMOS 7.0 were used to conduct empirical analysis, exploratory factor analysis and confirmatory factor analysis on the survey data, and the measurement scales were tested. The empirical analysis results show that this paper uses cluster sampling and random sampling to carry out a questionnaire survey on 1022 primary and secondary school teachers. The measurement scale includes 19 items. Through exploratory factor analysis and confirmatory factor analysis, the control method is determined. In two dimensions, the revised control style questionnaire has a reliability of 0.951, and items with factor loadings less than 0.5 are eliminated, which effectively improves the control performance of informal learning.
A new firefly algorithm-based superpixel clustering method for vehicle segmentation

The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithm-based superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision. The modified firefly algorithm is further used to obtain the optimal superpixel clusters. The modified firefly algorithm is compared against state-of-the-art meta-heuristic algorithms on IEEE CEC 2015 benchmark problems in terms of mean fitness value, Wilcoxon rank-sum test, convergence behaviour, and box plot. The proposed meta-heuristic algorithm performed superior on more than 80% of the considered benchmark problems. Moreover, the modified firefly algorithm is statistically better on more than 92% of the total problems during Wilcoxon test. Further, the proposed segmentation method is analysed on a traffic dataset to segment the auto-rickshaw. The performance of the proposed method has been compared with kmeans-based superpixel clustering method. The proposed method shows the highest mean value of 0.6242 for Dice coefficient. Both qualitative and quantitative results affirm the efficacy of the proposed method.
A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality

A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate the neural representation of the cognitive map defined by unique spatial relationship of three objects, as well as the neural dynamics of the cognitive map following the task demand from localizing self-location to remembering the target location relative to the self-body. The dataset, including both fMRI and MEG, was also used to investigate the neural networks involved in representing a target within and outside the visual field. The dataset included 19 and 12 university students at Peking University for fMRI and MEG experiments, respectively (fMRI: 12 women, 7 men; MEG: 4 women, 8 men). The average ages of those participants were 24.9 years (MRI: 18-30 years) and 22.5 years (MEG: 19-25 years), respectively. fMRI BOLD and T1-weighted images were acquired using a 3T Siemens Prisma scanner (Siemens, Erlangen, Germany) equipped with a 20-channel receiver head coil. MEG neuromagnetic data were acquired using a 275-channel MEG system (CTF MEG, Canada). The dataset could be further used to investigate a range of neural mechanisms involved in human spatial cognition or to develop a bioinspired deep neural network to enhance machines' abilities in spatial processing.
Online Passive-Aggressive Active Learning for Trapezoidal Data Streams

The idea of combining the active query strategy and the passive-aggressive (PA) update strategy in online learning can be credited to the PA active (PAA) algorithm, which has proven to be effective in learning linear classifiers from datasets with a fixed feature space. We propose a novel family of online active learning algorithms, named PAA learning for trapezoidal data streams (PAATS) and multiclass PAATS (MPAATS) (and their variants), for binary and multiclass online classification tasks on trapezoidal data streams where the feature space may expand over time. Under the context of an ever-changing feature space, we provide the theoretical analysis of the mistake bounds for both PAATS and MPAATS. Our experiments on a wide variety of benchmark datasets have confirm that the combination of the instance-regulated active query strategy and the PA update strategy is much more effective in learning from trapezoidal data streams. We have also compared PAATS with online learning with streaming features (OLSF)—the state-of-the-art approach in learning linear classifiers from trapezoidal data streams. PAATS could achieve much better classification accuracy, especially for large-scale real-world data streams.
IL2ATL: Design of a high efficiency load balancing model using augmented deep incremental transfer learning

Designing load balancing models for cloud deployments is a multi domain task, which involves task pattern prediction, capability analysis of virtual machines, deadline estimation, priority control, etc. A wide variety of task scheduling models are designed by researchers, and each of these models performs the mentioned tasks in one order or another for scheduling. Most of these models design a set of rules, and schedule tasks based on them, with minimal run time reconfiguration. This is an inherent lacuna which limits their real time scheduling capabilities. In order to improve scheduling capabilities during load balancing, this text proposes an augmented deep incremental transfer learning model. The model initially uses a fusion of Genetic Algorithm (GA) & particle swarm optimization (PSO) for deadline aware task scheduling. The hybrid GA PSO model is analyzed for every batch of scheduled tasks, and hyperparameter augmentation is performed using VGGNet19 convolutional neural network (CNN) model. These parameters are given to an incremental learning layer based on gated recurrent unit (GRU) for fine tuning and feedback learning. Finally, a Generative Adversarial Network (GAN) based autoencoder (AE) model is used for transfer learning, which assists in improving scheduling efficiency via usage of pre-trained weights from standard scheduling models. The proposed load balancer was tested on Google Cloud Jobs & IBM Telecom Scheduling datasets for performance evaluation. This efficiency improvement is found to be consistent across different datasets, and different cloud architectures, thereby making the proposed IL2ATL model applicable for a wide variety of scenarios.
Python and Its Future Scope

Python is an advanced programming language designed by Guido van Rossum, Dutch editor. Generally, "python programming language is widely used in web development, Application development, system management, game development etc. " But did you know that there is a technology for the future who rely on an anaconda? In fact, Python has become the dominant language to the success of these technology is concerned. Let's dive into technology that uses python as a core research, production and other developments. Artificial Intelligence (AI), Big Data, Network The future scope of the Python programming language too it depends on its competitors in the IT market. But, because the fact that it has become the language of the future technologies such as artificial intelligence, big data, etc., it will certainly go up and be able to overcome it rivals. Tiobe Index, PYPL index, Dismiss The top three Python competitors in India are listed below and their market shares and now websites. 1. ASP.NET Market Share- 39.53%, Current Webs- 41,052. 2. Java Market Share - 4.03%, Current Websites 4,186. 3. C # Market Share - 1.97%, Current 2,042 Websites
RETRACTED ARTICLE: Balanced scheduling method of network information resources for cloud storage

In the current balanced scheduling process of network communication shared resources, there are some problems, such as long time to complete the resource scheduling, high cost consumption and so on. A balanced scheduling method of cloud storage network information resources is proposed. By constructing the storage model of network communication shared resources, the network communication structure under big data is layered. Feature parameters are extracted and orthogonal weighted constraint balance ratio is introduced to obtain the fitness function of network communication shared resources. Fitness function and convergence factor are used to solve this problem. At the same time, according to the crossover factor and mutation factor, the efficiency of local resource search is improved, and the global optimal search is obtained, so as to complete the balanced scheduling of resources. The experimental results show that this method has shorter completion time and lower cost, and meets the research requirements.
A Data Driven Based Ultra Short PV Forecasting Method With Sky Images

With increasing levels of renewable energy in power systems, the coordination of different types of dispatchable resources, such as coal-fired power plants, hydropower plants, energy storage systems, and electric vehicles, has become more important than before. To optimally dispatch these operating units, the quality of the forecasting results becomes increasingly important for the operation of power systems. In this study, an ultra-short forecasting method was proposed for photovoltaic (PV) systems. It provided a forecast of the power output for the following 5 min using sky images obtained photographically in real time. The brightness of the key area was an important factor in determining the output power of the PV system. The output power was calculated using several different features extracted from the sky images. The brightness and other key features were then processed by a bidirectional long short-term memory network. The accuracy of the proposed PV forecasting method improved the accuracy of the forecast for the total power system. A testbed system was established to capture sky images in real time and verify the effectiveness of the proposed method.
An Alternative Approach to Polynomial Modular Number System Internal Reduction

The Polynomial Modular Number System (PMNS) is an alternative to the binary multi-precision representation that allows to transport the arithmetic of a finite field to a polynomial ring. The most important operation in that system is the internal reduction that follows any arithmetic operation. All recent works on the subject use the same algorithm derived from Montgomery's modular multiplications to perform this internal reduction. This paper designs and analyzes two alternative algorithms to perform the internal reduction, both based on Babai's Closest Vector algorithms. It allows to significantly reduce the number of additions needed to perform this operation. A comprehensive experimental analysis shows that one of those algorithms is also faster in practice. For that matter, a C code generation tool has been developed in order to produce implementations for any prime field.
Hybrid GMDH deep learning networks – analysis, optimization and applications in forecasting at financial sphere

In this paper, the new class of deep learning (DL) neural networks is considered and investigated — so-called hybrid DL networks based on self-organization method Group Method of Data Handling (GDMH). The application of GMDH enables not only to train neural weights, but also to construct the network structure as well. Different elementary neurons with two inputs may be used as nodes of this structure. So the advantage of such a structure is the small number of tuning parameters. In this paper, the optimization of parameters and the structure of hybrid neo-fuzzy networks was performed. The application of hybrid Dl networks for forecasting market indices was considered with various forecasting intervals: one day, one week, and one month. The experimental investigations of hybrid GMDH neo-fuzzy networks were carried out and comparison of its efficiency with FNN ANFIS in the forecasting problem was performed which enabled to estimate their efficiency and advantages.
Cryo-compatible opto-mechanical low-voltage phase-modulator integrated with superconducting single-photon detectors

Photonic integrated circuits (PICs) have enabled novel functionality in quantum optics, quantum information processing and quantum communication. PICs based on Silicon Nitride (Si3N4) provide low-loss passive components and are compatible with efficient superconducting nanowire single-photon detectors (SNSPDs). For realizing functional quantum photonic systems, the integration with active phase-shifters is needed which is challenging at the cryogenic temperatures needed for operating SNSPDs. Here we demonstrate a cryo-compatible phase shifter using a low-voltage opto-mechanical modulator and show joint operation with SNSPDs at 1.3 K. We achieve a half-wave voltage of 4.6 V, single-photon detection with 88% on-chip detection efficiency (OCDE) and a low timing jitter of 12.2 ps. Our approach allows for operating reconfigurable quantum photonic circuits with low dissipation in a cryogenic setting.
Quantum Computing: From Alice to Bob

Abstract Quantum Computing: From Alice to Bob provides a distinctive and accessible introduction to the rapidly growing fields of quantum information science (QIS) and quantum computing (QC). The book is designed for undergraduate students and upper-level secondary school students with little or no background in physics, computer science, or mathematics beyond secondary school algebra and trigonometry. While broadly accessible, the book provides a solid conceptual and formal understanding of quantum states and entanglement—the key ingredients in quantum computing. The authors give detailed treatments of many of the classic quantum algorithms that demonstrate how and when QC has an advantage over classical computers. The book provides a solid explanation of the physics of QC and QIS and then weds that knowledge to the mathematics of QC algorithms and how those algorithms deploy the principles of quantum physics to solve the problem. This book connects the physics concepts, the computer science vocabulary, and the mathematics, providing a complete picture of how QIS and QC work. The authors give multiple representations of the concept—textual, graphical, and symbolic (state vectors, matrices, and Dirac notation)—which are the lingua franca of QIS and QC. Those multiple representations allow the readers to develop a broader and deeper understanding of the fundamental concepts and their applications. In addition, the book provides examples of recent experimental demonstrations of quantum teleportation and the applications of quantum computational chemistry. The last chapter connects to the growing commercial world of QC and QIS and provides recommendations for further study.
Similarity‐based adversarial knowledge distillation using graph convolutional neural network

This letter presents an adversarial knowledge distillation based on graph convolutional neural network. For knowledge distillation, many methods have been proposed in which the student model individually and independently imitates the output of the teacher model on the input data. Our method suggests the application of a similarity matrix to consider the relationship among output vectors, compared to the other existing approaches. The similarity matrix of the output vectors is calculated and converted into a graph structure, and a generative adversarial network using graph convolutional neural network is applied. We suggest similarity-based knowledge distillation in which a student model simultaneously imitates both of output vector and similarity matrix of the teacher model. We evaluate our method on ResNet, MobileNet and Wide ResNet using CIFAR-10 and CIFAR-100 datasets, and our results outperform results of the baseline model and other existing knowledge distillations like KLD and DML.
Information Seeking behaviours of Orphans and Vulnerable Children in Selected Homes in Ogun State, Nigeria

The paper investigated information seeking behaviours of the orphans and vulnerable children in the selected homes in Ogun state, Nigeria. The study is a survey research anchored on post- positivism paradigm which combined both qualitative and quantitative methods employing both questionnaire and interview to collect data. The population of the study is 450 from two homes in Ogun state. The study adopted the snowballing sampling technique to locate the respondents for the study. The findings of the study revealed that the informal sources such as the Internet, television and friends are sources of information for OVC. The most preferred source of information is television and the main challenge facing the OVC in seeking information is basically the high cost of getting information such as subscriptions to the Internet and other information sources. Based on the findings of the study, the research recommends the followings; the management of the two homes should provide Internet services for the children and they should be able to access the internet services regularly. OVC program should be included in the television programs and television hours should be included in their timetables. In addition, government and various social organizations should provide financial assistance to the OVC to be able to subscribe to various forms of information sources. Ministry of Social Welfare and Development should assist the OVC in providing necessary information resources at subsidized rate.
A Fuzzy System for Identifying Partial Reduplication

Reduplication is a common feature more or less in almost all languages. It is a linguistic process that has been studied since back in both a morphological as well as a phonological process. Literary reduplication is the repetition of tokens in various forms like morpheme, phrase, word, etc. In most cases, it affects the syntactic or/and semantic meaning of the original words. But in several cases, it is not the exact reduplication rather partial reduplication. In the exact or total reduplication, it exactly reiterates a word or a phrase (e.g. fifty-fifty, bye-bye in English) while in partial reduplication reiteration partially (e.g. flip-flop in English). The morphological structure of a total or partial reduplication is relatively simple for English-like languages, but it is complex in the case of African, Austronesian, or South-Asian languages. Earlier researchers tried to address this problem using the various heuristic approaches. This paper presented a set of fuzzy-based approach to deal with partial reduplication in the Bengali language.
Bridges of hope: IBBY Ireland projects for a changing population

Migration and storytelling are defining and entwined features of humanity. Throughout history, people have travelled, in body and mind, exploring new places and ideas either by choice or necessity. Migration establishes cultural connections and imaginative possibilities, like new neural pathways in the collective human brain. This article explores workshops, designed and run in Ireland by children’s author Debbie Thomas and children’s author and illustrator Tatyana Feeney, who have used creative writing and illustration to build bridges between immigrants and Irish nationals, mainly children. We will describe cross-cultural workshops we have facilitated with children around Ireland and then focus on three major projects: Once upon a Folktale and Nation Creation (with children in mainstream Irish primary schools) and Connections (with Syrian refugees). In each project, Debbie oversaw the writing and Tatyana, the illustrations. All three projects have bridged cultures and enriched young participants, teachers and facilitators both culturally and creatively.
Three Level Graphical Password Authentication System with File Encryption

One of the major problems in today’s word is security. Security threats can be seen everywhere in the whole world. From the beginning we just use single level authentication but it is not giving much security. To provide more security, Three Level Graphical Password Authentication System (TLGPAS) used. This is the more secure idea to implement three level password authentications for real users. Usually, textual password is generally used for authentication and password is one of the most used techniques to recognize all computers and other communication devices. The process of graphical password is choosing images then come in to the textual characters. In image password, user have to use the image for password. Clicking in different places on image user set password. Picture password there - first user have to select an image in jpg format to use as a password and then user can set the password by clicking on the image in different places. We generally know that graphical password is not easy to predict and it is quite difficult.
Method for improving the measurement accuracy of binocular stereo vision in a scattering environment

In the scattering environment, binocular stereo vision measurement technology produces large errors due to the change of refractive index of the imaging light path and the decrease in target image contrast. To address this problem, this paper proposes a method for improving the measurement accuracy of binocular stereo vision in a scattering environment combined with polarization imaging theory. First, scattering images with different polarization directions are obtained and filtered by a Gaussian low-pass filter to calculate the degree of polarization and angle of polarization. Then, the scattered light intensity is calculated by using polarization information to obtain images after removing the scattering. Second, feature extraction and matching are carried out for the images after scattering removal. Finally, the target is measured based on the binocular stereo vision measurement model. The experimental results show that when the scattering concentration is high enough, the conventional method can no longer perform measurement, but the method proposed in this paper can still obtain the target parameters at this time, and can also improve measurement accuracy by at least 46.30%. In conclusion, the proposed method provides a valuable reference to improve the accuracy of binocular stereo vision measurement in a scattering environment by reducing the interference of scattering light.