Reviewerly/exHarmony huggingface dataset:
ALBERT: An automatic learning based execution and resource management system for optimizing Hadoop workload in clouds

Hadoop is a popular computing framework designed to deliver timely and cost-effective data processing on a large cluster of commodity machines. It relieves the burden of the programmers dealing with distributed programming, and an ecosystem of Big Data solutions has developed around it. However, Hadoop's job execution time can greatly depend on its runtime configurations and resource selections. Given the more than 100 job configuration settings provided by Hadoop, and diverse resource instance options in a cloud or virtualized computing environment, running Hadoop jobs still requires a substantial amount of expertise and experience. To address this challenge, we apply a deep neural network to predict Hadoop's job time based on historical execution data, and propose optimization methods to reduce job execution time and cost. The results show that our prediction method achieves almost 90% time prediction accuracy and clearly outperforms three other state-of-the-art regression-based prediction methods. Based on the time prediction, our proposed configuration search method and job scheduling algorithm successfully shorten the execution time of a single Hadoop job by more than a factor of 2 and reduce the time of processing a batch of Hadoop jobs by 40%∼65%. • We aim to optimize the performance and cost of running Hadoop MapReduce workload in a cloud environment with on-demand resources. • We propose a deep learning approach to predict the execution time of MapReduce jobs under any given resource allocation and Hadoop configuration setting. • We proposed a 2-step approximate search algorithm for finding the optimal resource allocation and execution configuration of individual job, and a 2D variable size bin packing algorithm for maximize overall resource utilization. • The experiment results show that our prediction method achieved almost 90% of time prediction accuracy and clearly out-performed three other state-of-art machine learning prediction methods. • Our optimization strategies successfully shorten the execution time of a single Hadoop job by more than a factor of 2 and reduce the time of processing a batch of Hadoop jobs by 40%∼65%.
Evaluation of user response by using visual cues designed to direct the viewer’s attention to the main scene in an immersive environment

Abstract Today the visualization of 360-degree videos has become a means to live immersive experiences.. However, an important challenge to overcome is how to guide the viewer’s attention to the video’s main scene, without interrupting the immersion experience and the narrative thread. To meet this challenge, we have developed a software prototype to assess three approaches: Arrows, Radar and Auto Focus. These are based on visual guidance cues used in first person shooter games such as: Radar-Sonar, Radar-Compass and Arrows. In the study a questionnaire was made to evaluate the comprehension of the narrative, the user’s perspective with respect to the design of the visual cues and the usability of the system. In addition, data was collected on the movement of the user’s head, in order to analyze the focus of attention. The study used statistical methods to perform the analysis, the results show that the participants who used some visual cue (any of these) showed significant improvements compared to the control group (without using visual cues) in finding the main scene. With respect to narrative compression, significant improvements were obtained in the user group that used Radar and Auto Focus compared to the control group.
Risk sensitive filtering with randomly delayed measurements

Conventional Bayesian estimation requires an accurate stochastic model of a system. However, this requirement is not always met in many practical cases where the system is not completely known or may differ from the assumed model. For such a system, we consider a scenario where the measurements are transmitted to a remote location using a common communication network and due to which, a delay is introduced while receiving the measurements. The delay that we consider here is random and one step maximum at a given time instant. For such a scenario, this paper develops a robust estimator for a linear Gaussian system by minimizing the risk sensitive error criterion that is defined as an expectation of the accumulated exponential quadratic error. The criteria for the stability of the risk sensitive Kalman filter (RSKF) are derived and the results are used to study the stability of the developed filter. Further, it is assumed that the latency probability related to delay is not known and it is estimated by maximizing the likelihood function. Simulation results suggest that the proposed filter shows acceptable performance under the nominal conditions, and it performs better than the Kalman filter for randomly delayed measurements and the RSKF in presence of both the model uncertainty and random delays.
Evaluation Model of College English Education Effect Based on Big Data Analysis

The evaluation system of education effect is an important part of the whole teaching process, and the establishment of the evaluation system of college English teaching effect is an important work to test the effect of college English teaching. The traditional evaluation model is widely used and cannot be applied to a variety of teaching situations. Therefore, this paper proposes an evaluation model of college English education effect based on big data analysis. This paper determines the selection principle of the evaluation index of college English education effect, and on this basis, selects the evaluation index factors of college English education effect (experts, students and teachers), calculates the weight and membership matrix of the evaluation index, and outputs the comprehensive evaluation results of college English education effect, which realizes the construction of the evaluation model of college English education effect. The results show that: under the background of the experimental subjects (senior one and senior two), the evaluation errors of English education effect meet the needs of colleges and universities, which proves that the construction model is effective and feasible, and provides the basis and support for the reform of college English education. The range of assessment errors is between 0.78% and 1.44%, all consistent with the demands of the evaluation of the English education effect which demonstrates that the model is successful.
Perceptions and behavior of clinical researchers and research support staff regarding data FAIRification

The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness and attitudes of clinical researchers and research support staff regarding data FAIRification. A questionnaire was distributed to researchers and support staff in six Dutch University Medical Centers and Electronic Data Capture platform users. 164 researchers and 21 support staff members completed the questionnaire. 62.8% of the researchers and 81.0% of the support staff are currently undertaking at least some effort to achieve any aspect of FAIR, 11.0% and 23.8%, respectively, address all aspects. Only 46.6% of the researchers add metadata to their datasets, 39.7% add metadata to data elements, and 35.9% deposit their data in a repository. 94.7% of the researchers are aware of the usefulness of their data being FAIR for others and 89.3% are, given the right resources and support, willing to FAIRify their data. Institutions and funders should, therefore, develop FAIRification training and tools and should (financially) support researchers and staff throughout the process.
Collision-aware interactive simulation using graph neural networks

Deep simulations have gained widespread attention owing to their excellent acceleration performances. However, these methods cannot provide effective collision detection and response strategies. We propose a deep interactive physical simulation framework that can effectively address tool-object collisions. The framework can predict the dynamic information by considering the collision state. In particular, the graph neural network is chosen as the base model, and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests. Additionally, a novel self-supervised collision term is introduced to provide a more compact collision response. This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.
University Estates and Postdigital Higher Education: Space, Place, and Being a University

Estates are a key interest for universities. In an era of postdigital higher education dominated by cost, benefits, and a pursuit of value, the relations between the estate and the university are again in question. Drawing from literature on postdigital higher education, university facilities, and study of university built environments, this paper extends current debate on university estates and facilities using a space and place lens. An introduction outlines the key texts and questions for this paper. After, space and place are conceptualized and defined. These concepts are then used to revisit university estates and facilities a literature, primarily focusing on the United Kingdom and drawing on complimentary and contrasting work from the United States. The influence of material space(s) for placemaking in universities is a key focus. Subsequently, the paper challenges the influence of performance based discourse and performativity for the forms, function and stated purpose of university estates and facilities. A concluding note highlights university estates and facilities as key to concepts and practices of being a university.
Active learning-assisted semi-supervised learning for fault detection and diagnostics with imbalanced dataset

Data-driven Fault Detection and Diagnostics (FDD) methods often assume that sufficient labeled samples are class-balanced and faulty classes in testing are precedent or seen previously during model training. When monitoring a large fleet of assets at scale, these assumptions may be violated: (I) only a limited number of samples can be manually labeled due to constraints of time and/or cost; (II) most of the samples collected in the engineering systems are under normal conditions, leading to a highly imbalanced class distribution and a biased prediction model. This work presents a robust and cost-effective FDD framework that integrates active learning and semi-supervised learning methods to detect both known and unknown failure modes iteratively. This framework allows to strategically select the samples to be annotated from a fully unlabeled dataset, while labeling cost is minimal. Specifically, a novel graph-based semi-supervised classifier with adaptive graph construction is developed to predict labels with imbalanced data and detect novel classes. We designed a multi-criteria active learning sampling strategy to select the most informative samples from unlabeled data in order to query minimal number of labels for classification. We tested the framework and algorithms in three synthetic datasets and one real-world dataset of vehicle air intake systems, and demonstrated the superior performance compared to the state-of-the-art methods for fleet-level FDD.
Survival analysis using Dirichlet process mixture model with three-parameter Burr XII distribution as kernel

Modeling multimodal survival data using parametric distributions is yet a challenging task. Mixture distributions in a nonparametric Bayesian framework can be applied as a sophisticated tool. Dirichlet process mixture model (DPMM) is a suitable asset in this approach. Choosing a kernel for the mixture model is an essential issue in this context. Survival data analysis using the DPMM with a three-parameter Burr XII kernel is the objective of this article. The Monte Carlo Markov Chain algorithms, like Gibbs sampling, are developed to fit the three-parameter Burr XII mixture model. A Simulation study based on right-censored data illustrates the performance of this kind of mixture model. We compare the proposed model results with other DPMM with different kernels like Weibull, Lognormal, and two-parameter Burr XII distributions. The flexibility of the proposed mixture model is demonstrated using three real datasets. In some instances, DPMM with Weibull or lognormal kernels perform well; meanwhile, these kernels may not outperform in the clustering or hazard rate estimation context. In these cases, a three parameters BurXII distribution might be a suitable kernel for Dirichlet process mixture model.
Photocounting statistics of superconducting nanowire single-photon detectors

Superconducting nanowire single-photon detectors (SNSPDs) are efficient measurement devices used for counting single photons. The field of their applications covers experimental quantum-optical studies, optical quantum computing, quantum communication, and others. After registering a photon by such a detector, the next one cannot be registered during the dead time and after that this ability is smoothly restored. We have included this feature into the photodetection theory and introduced the corresponding photocounting formula. In the regime of continuous-wave detection, the photocounting statistics nonlinearly depends on the density operator due to a memory effect of previous measurement time windows. The considered examples demonstrate the strong influence of the relaxation process and the memory effect on the resulting photocounting statistics of the SNSPDs.
Multi-classification for high-dimensional data using probabilistic neural networks

Multi-classification tasks need sufficient information provided by the input data, whereas the input data lying in the high-dimensional space presents too sparse distributions to afford rich information, which creates trouble for multi-classification tasks. To address this, this paper proposed a probabilistic neural network approach. Firstly, the classification probability of high-dimensional data is calculated by using a novel Equation for Naïve Bayesian in order to reduce misclassification probability caused by the high dimension of the input data. Then, the calculated classification probability is fused into the cost function of the neural networks, forming the probabilistic neural networks. Results on high-dimensional real-world datasets and the National Surface Water Quality data provided by Environmental Monitoring Station, China show that the proposed method outperforms comparison methods in both classification ability and resisting the curse of dimensionality. We find that neural networks hardly obtain those advanced classification results upon a high-dimensional space because of the curse of dimensionality, but calculating classification probability of the input data can be confident to help neural networks to be resistance to the negative effects caused by high dimension of the input data.
Construction of Mathematics Teaching Environment Based on Big Data on the Wisdom Cloud Platform of Higher Vocational Education

As a public basic course in the system of higher vocational colleges, mathematics has received more and more attention from education administrators. However, there are still problems in higher education that mathematics teaching resources are scatter and data are difficult to integrate. Big data (DT) and smart clouds are the burgeon of intelligent technology, which has provided support for teaching informatization and teaching data integration in colleges and universities. This article aims to solve the problems of mathematics teaching in higher vocational education, such as more content and less class hours, difficult, higher vocational education due to the expansion of enrollment, and the decline in mathematics teaching quality, and build a smart cloud platform based on big data mathematics instructing environment system and mathematics wisdom instructing. The reconstruction of the environment is the deep integration of information technology and mathematics instructing, which can promote the deep integration of information technology with teaching, management, and environment. The experiments in this article show that the application of the smart cloud platform to mathematics teaching can greatly increase students' enthusiasm for learning and improve the school's mathematics teaching level, and the improvement rate of students' performance can reach 99.89%. The research in this article is of great significance to the adjustment of mathematics teaching environment in higher professional education.
Cervical cancer classification using efficient net and fuzzy extreme learning machine

Cervical cancer is the most common and deadly malignancy affecting women worldwide. The prediction and treatment of this malignancy are necessary in order to avoid serious complications. In recent days, deep learning has enhanced the accuracy of cervical cancer prediction in its early stages. In this study, a deep learning based EN-FELM approach is proposed to detect and classify the cervical cells. Initially, the pap smear images are pre-processed to eliminate the background distortions. The EfficientNet is a reversed bottleneck MBConv used for feature extraction. Consequently, fuzzy extreme learning machine (FELM) is used to classify the healthy, benign, low squamous intraepithelial lesions (LSIL) and high squamous intraepithelial lesions (HSIL). The proposed model acquires the best classification accuracy on Herlev and SIPaKMeD datasets range of 99.6% and 98.5% respectively. As a result, the classification using FELM produces more efficient and accurate result which is significantly high compared to the traditional classifiers. The proposed EN-FELM improves the overall accuracy of 0.2%, 0.13% and 14.6% better than Autoencoder, LSTM and KNN with CNN respectively.
Budget Distributed Support Vector Machine for Non-ID Federated Learning Scenarios

In recent years, there has been remarkable growth in Federated Learning (FL) approaches because they have proven to be very effective in training large Machine Learning (ML) models and also serve to preserve data confidentiality, as recommended by the GDPR or other business confidentiality restrictions that may apply. Despite the success of FL, performance is greatly reduced when data is not distributed identically (non-ID) across participants, as local model updates tend to diverge from the optimal global solution and thus the model averaging procedure in the aggregator is less effective. Kernel methods such as Support Vector Machines (SVMs) have not seen an equivalent evolution in the area of privacy preserving edge computing because they suffer from inherent computational, privacy and scalability issues. Furthermore, non-linear SVMs do not naturally lead to federated schemes, since locally trained models cannot be passed to the aggregator because they reveal training data (they are built on Support Vectors), and the global model cannot be updated at every worker using gradient descent. In this article, we explore the use of a particular controlled complexity (“Budget”) Distributed SVM (BDSVM) in the FL scenario with non-ID data, which is the least favorable situation, but very common in practice. The proposed BDSVM algorithm is as follows: model weights are broadcasted to workers, which locally update some kernel Gram matrices computed according to a common architectural base and send them back to the aggregator, which finally combines them, updates the global model, and repeats the procedure until a convergence criterion is met. Experimental results using synthetic 2D datasets show that the proposed method can obtain maximal margin decision boundaries even when the data is non-ID distributed. Further experiments using real-world datasets with non-ID data distribution show that the proposed algorithm provides better performance with less communication requirements than a comparable Multilayer Perceptron (MLP) trained using FedAvg. The advantage is more remarkable for a larger number of edge devices. We have also demonstrated the robustness of the proposed method against information leakage, membership inference attacks, and situations with dropout or straggler participants. Finally, in experiments run on separate processes/machines interconnected via the cloud messaging service developed in the context of the EU-H2020 MUSKETEER project, BDSVM is able to train better models than FedAvg in about half the time.
A Study: Hate Speech and Offensive Language Detection in Textual Data by Using RNN, CNN, LSTM and BERT Model

The issue of offensive speech on social networking platforms is widespread, or like face book and twitter website is dealing with it. Several approaches for intent-based text categorization have been investigated. Each technique has advantages and disadvantages depending on the type of goal, the dimension of the facts collection, the highest reach of content, and so on. Various ways for detecting dislike and incitement to hatred have been published in the literature. The primary objective of paper is to give a approximate investigation of several method for detecting hateful content and abusive content. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional encoder representations from transformers are among the approaches used (BERT). The impact of class weighting methods on the efficacy of deep learning techniques was investigated. Our research shows that the or before BERT model exceed another method including both un weighted and weighted offensive prose allocation. In terms of foul language categorization, the RNN and CNN models beat every alternate methods in both un weighted and weighted cases. It was discovered that the class weighting strategy significantly improved the grading precision of all four hate speech models.
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction

Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extracting a good representation space is critical to address the curse of dimensionality, improve model generalization, overcome data sparsity, and increase the availability of classic models. Existing literature, such as feature engineering and representation learning, is limited in achieving full automation (e.g., over heavy reliance on intensive labor and empirical experiences), explainable explicitness (e.g., traceable reconstruction process and explainable new features), and flexible optimal (e.g., optimal feature space reconstruction is not embedded into downstream tasks). Can we simultaneously address the automation, explicitness, and optimal challenges in representation space reconstruction for a machine learning task? To answer this question, we propose a group-wise reinforcement generation perspective. We reformulate representation space reconstruction into an interactive process of nested feature generation and selection, where feature generation is to generate new meaningful and explicit features, and feature selection is to eliminate redundant features to control feature sizes. We develop a cascading reinforcement learning method that leverages three cascading Markov Decision Processes to learn optimal generation policies to automate the selection of features and operations and the feature crossing. We design a group-wise generation strategy to cross a feature group, an operation, and another feature group to generate new features and find the strategy that can enhance exploration efficiency and augment reward signals of cascading agents. Finally, we present extensive experiments to demonstrate the effectiveness, efficiency, traceability, and explicitness of our system.