Reviewerly/exHarmony huggingface dataset:
A fast self-calibration method of line laser sensors for on-machine measurement of honeycomb cores

Line laser scanning is an effective method used to measure the 3D shape of machined honeycomb core surfaces. The lack of laser-sensor calibrations in 3D space relative to the machine coordinate system restricts the industrial applications of line lasers for on-machine measurements. In this paper, a sensor calibration method based on scanning a honeycomb core itself is proposed. The sensor rotational errors are reversely calculated from the data dislocation originating from the sensor without calibration, and then calibrated by mechanical adjustment and numerical compensation. The sensor calibration parameters are determined once rather than performing data stitching one by one, greatly improving the operation efficiency. The sensor calibration effect is verified from the data seam analysis with and without sensor calibration as well as without stitching, and the comparison results demonstrate that the proposed method is effective in solving the misalignment of the measurement data after stitching. The experimental stitching error verified through a solid block is less than 8 μm. The proposed self-calibration stitching method possesses advantages in large-sized on-machine measurements of honeycomb cores.
Blockchain-based governance models for COVID-19 digital health certificates: A legal, technical, ethical and security requirements analysis

This paper analyses the requirements of a blockchain-based data governance model for COVID-19 digital health certificates. Recognizing a gap in the existing literature, this paper aims to answer the research question "To what extent does a blockchain-based governance model for COVID-19 digital health certificates in the EU meet the relevant legal, technical, ethical and security requirements?" This paper identifies the required standards and develops a novel framework to determine the viability of blockchain as a governance model. The results of our evaluation indicate that while a private permissioned blockchain can meet the requirements to some degree, the governance element is key to legal compliance; legal risks and ethical implications remain unresolved with the use of blockchain. The paper also found that this model comes with the loss of the main advantages of blockchain - decentralization and anonymity. This evaluation framework may be used in other contexts and for assessing other technologies.
Towards Automatic Job Description Generation with Capability-Aware Neural Networks

A job description shows the responsibilities of the job position and the skill requirements for the job. An effective job description will help employers to identify the right talents for the job, and give a clear understanding to candidates of what their duties and qualifications for a particular position would be. In this paper, we investigate how to automate the process to generate job descriptions with less human intervention. We propose an end-to-end capability-aware neural job description generation framework, namely Cajon, to facilitate the writing of job description. Specifically, we first propose a novel capability-aware neural topic model to distill the various capability information from the larger-scale recruitment data. Also, an encoder-decoder recurrent neural network is designed for enabling the job description generation. In particular, the capability-aware attention and copy mechanisms are proposed to guide the generation process to ensure the generated job descriptions can comprehensively cover relevant and representative capability requirements for the job. Moreover, we propose a capability-aware policy gradient training algorithm to further enhance the rationality of the generated job description. Finally, extensive experiments on real-world recruitment data clearly show our Cajon framework can help to generate more effective job descriptions in an interpretable way
Improved Human Activity Recognition Using Majority Combining of Reduced-Complexity Sensor Branch Classifiers

Human activity recognition (HAR) employs machine learning for the automated recognition of motion and has widespread applications across healthcare, daily-life and security spaces. High performances have especially been demonstrated using video cameras and intensive signal processing such as the convolutional neural network (CNN). However, lower complexity algorithms operating on low-rate inertial data is a promising approach for portable use-cases such as pairing with smart wearables. This work considers the performance benefits from combining HAR classification estimates from multiple sensors each with lower-complexity processing compared with a higher-complexity single-sensor classifier. We show that while the highest single-sensor classification accuracy of 91% can be achieved for seven activities with optimized number of hidden units and sample rate, the classification accuracy is reduced to 56% with a reduced-complexity 50-neuron classifier. However, by majority combining the predictions of three and four low-complexity classifiers, the average classification accuracy increased to 82.5% and 94.4%, respectively, demonstrating the efficacy of this approach.
A Layer-Wise Theoretical Framework for Deep Learning of Convolutional Neural Networks

As research attention in deep learning has been focusing on pushing empirical results to a higher peak, remarkable progress has been made in the performance race of machine learning applications in the past years.Yet deep learning based on artificial neural networks still remains difficult to understand as it is considered as a black-box approach.A lack of understanding of deep learning networks from the theoretical perspective would not only hinder the employment of them in applications where high-stakes decisions need to be made, but also limit their future development where artificial intelligence is expected to be robust, predictable and trustable.This paper aims to provide a theoretical methodology to investigate and train deep convolutional neural networks so as to ensure convergence.A mathematical model based on matrix representations for convolutional neural networks is first formulated and an analytic layer-wise learning framework for convolutional neural networks is then proposed and tested on several common benchmarking image datasets.The case studies show a reasonable trade-off between accuracy and analytic learning, and also highlight the potential of employing the proposed layer-wise learning method in finding the appropriate number of layers in actual implementations.
A Tapered Whisker-Based Physical Reservoir Computing System for Mobile Robot Terrain Identification in Unstructured Environments

In this letter, we present for the first time the use of tapered whisker-based reservoir computing (TWRC) system mounted on a mobile robot for terrain classification and roughness estimation of unknown terrain. Hall effect sensors captured the oscillations at different locations along a tapered spring that served as a reservoir to map time-domain vibrations signals caused by the interaction perturbations from the ground to frequency domain features directly. Three hall sensors are used to measure the whisker reservoir outputs and these temporal signals could be processed efficiently by the proposed TWRC system which can provide morphological computation power for data processing and reduce the model training cost compared to the convolutional neural network (CNN) approaches. To predict the unknown terrain properties, an extended TWRC method including a novel detector is proposed based on the Mahalanobis distance in the Eigen space, which has been experimentally demonstrated to be feasible and sufficiently accurate. We achieved a prediction success rate of 94.3% for six terrain surface classification experiments and 88.7% for roughness estimation of the unknown terrain surface.
Enlightening Low-Light Images With Dynamic Guidance for Context Enrichment

Images acquired in low-light conditions suffer from a series of visual quality degradations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , low visibility, degraded contrast, and intensive noise. These complicated degradations based on various contexts ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., noise in smooth regions, over-exposure in well-exposed regions and low contrast around edges) cast major challenges to the low-light image enhancement. Herein, we propose a new methodology by imposing a learnable guidance map from the signal and deep priors, making the deep neural network adaptively enhance low-light images in a region-dependent manner. The enhancement capability of the learnable guidance map is further exploited with the multi-scale dilated context collaboration, leading to contextually enriched feature representations extracted by the model with various receptive fields. Through assimilating the intrinsic perceptual information from the learned guidance map, richer and more realistic textures are generated. Extensive experiments on real low-light images demonstrate the effectiveness of our method, which delivers superior results quantitatively and qualitatively. The code is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/lingyzhu0101/GEMSC</uri></i> to facilitate future research.
Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine; however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers' dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider's services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention.
Effects of professional learning communities on teacher collaboration, feedback provision, job satisfaction and self-efficacy: Evidence from Korean PISA 2018 data

ABSTRACTABSTRACTAlthough much research has emphasised the effect of professional learning communities (PLCs) on teachers' collaboration, feedback provision, job satisfaction, and self-efficacy, few studies have focused on South Korea's unique context of government-led top-down policies to encourage PLCs. Using large-scale data representing all regions and sectors in Korea in the Programme of International Student Assessment (PISA) 2018, this study addressed the latent means differences in factors of teacher collaboration, feedback provision, job satisfaction, and self-efficacy between groups of teachers that had and had not participated in a PLC. All of the factors investigated were found to be higher among teachers that had participated in PLCs. Theoretical and policy implications are discussed.KEYWORDS: Professional learning communityteacher collaborationfeedback provisionjob satisfactionself-efficacy AcknowledgmentsThe authors thank Ksan Zoe Rubadeau for critical feedback and devoted editing. We also would like to thank the two anonymous reviewers and editor for helpful comments.Disclosure statementNo potential conflict of interest was reported by the author(s).
Performance Comparision of TPU, GPU, CPU on Google Colaboratory Over Distributed Deep Learning

Deep Learning models need massive amounts compute powers and tend to improve performance running on special purpose processors accelerators designed to speed up compute-intensive applications. The accelerators like Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs) are widely used as deep learning hardware platforms which can often achieve better performance than CPUs, with their massive parallel execution resources and high memory bandwidth. Google Colaboratory known as Colab is a cloud service based on Jupyter Notebook that allows the users to write and execute mostly Python in a browser and admits free access to TPUs and GPUs without extra configuration need, which are widely available cloud hardware platforms. In this paper, we present a through comparison of the hardware platforms on Google Colab that is benchmarked with Distributed Bidirectional Long Short-Term Memory (dBLSTM) models upon the number of layers, the number of units each layer, and the numbers of input and output units the datasets. Human Activity Recognition (HAR) data from UCI machine-learning library have been applied to the proposed distributed bidirectional LSTM model to find the performance, strengths, bottlenecks of the hardware platforms of TPU, GPU and CPU upon hyperparameters, execution time, and evaluation metrics: accuracy, precision, recall and F1 score.
Refining pseudo labels for unsupervised Domain Adaptive Re-Identification

Our paper focuses on the topic of unsupervised domain adaptation for person re-identification (UDA re-ID) in an end-to-end framework. Currently, most existing methods tackle the problem by mining pseudo labels, and they rely heavily on the reliability of these pseudo labels. However, the generation of these labels mainly depends on clustering-based algorithms, which may inevitably introduce noise into the framework. Such noise substantially hinders the capability of the model to further improve feature representations in the target domain. We called these clustering-based labels HARD pseudo labels. To decrease the impacts of these noisy hard labels, in this paper, we directly consider the relative relationships of sample pair distances as another kind of pseudo label instead of clustering results, called SOFT pseudo labels. Furthermore, considering that the constructed relative relationships are not always correct and the wrong relationships may cause negative effects, it is not appropriate to assign the same weight to each sample pair. To alleviate the negative influence caused by tiny distances and strengthen the positive effect due to large distances, we assign different weights according to the different distances to adjust these relationships. By integrating the HARD labels and SOFT labels, the proposed method achieves considerable improvements on several popular person re-ID datasets, e.g, Market1501-to-Duke, Duke-to-Market1501, Market-to-MSMT17 and Duke-to-MSMT17 UDA tasks.
Estimating the capacities of function-as-a-service functions

Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications follow this model, where the application is decomposed into a set of fine-grained Function-as-a-Service (FaaS) functions. However, the obscurities of the underlying system infrastructure and dependencies between FaaS functions within the application pose a challenge for estimating the performance of FaaS functions. To characterize the performance of a FaaS function that is relevant for the user, we define Function Capacity (FC) as the maximal number of concurrent invocations the function can serve in a time without violating the Service-Level Objective (SLO).
Monitoring and Analysis of English Classroom Teaching Quality Based on Big Data

In order to deal with the problems of low evaluation accuracy and long evaluation time in the traditional English classroom teaching quality monitoring and evaluation methods, this study proposes an English classroom teaching quality monitoring and evaluation method based on big data. According to the selection principle of English classroom teaching quality monitoring factors, this study analyzes the English classroom teaching quality monitoring factors, constructs the English classroom teaching quality monitoring index system, obtains the monitoring index data, filters and compresses the monitoring data, and takes the preprocessed English classroom teaching quality monitoring data as the evaluation index. This study uses the principal component analysis method to calculate the weight of the English classroom teaching quality evaluation index and constructs an English classroom teaching quality evaluation model. The simulation results show that the proposed method has high accuracy and short evaluation time.
A Method for Parameter Identification of Distribution Network Equipment Based on Sequential Model-Based Optimization

We present the first model-based parameter identification method in the power distribution network to successfully achieve parameter identification directly based on sequential model-based optimization. This method is building a model with a posteriori probability to optimize an objective function. Furthermore, to achieve an efficient exploration, three different acquisition functions, i.e., random search, tree-structured Parzen estimator approach, and simulated annealing, were proposed. We applied our three models and the conventional model-free method to the actual feeder data with no adjustment of the other conditions. The experiment shows that our method achieves at least 25% and 70% improvements in accuracy and convergence speed, respectively.
Single Image Reflection Removal Based on Knowledge-Distilling Content Disentanglement

When we shoot pictures through transparent media, such as glass, reflection can undesirably occur, obscuring the scene we intended to capture. Therefore, removing reflection is practical in image restoration. However, a reflective scene mixed with that behind the glass is challenging to be separated, considered significantly ill-posed. This letter addresses the single image reflection removal (SIRR) problem by proposing a knowledge-distilling-based content disentangling model that can effectively decompose the transmission and reflection layers. The experiments on benchmark SIRR datasets demonstrate that our method performs favorably against state-of-the-art SIRR methods.
Workflow performance prediction based on graph structure aware deep attention neural network

With the rapid growth of cloud computing, efficient operational optimization and resource scheduling of complex cloud business processes rely on real-time and accurate performance prediction. Previous research on cloud computing performance prediction focused on qualitative (heuristic rules), model-driven, or coarse-grained time-series prediction, which ignore the study of historical performance, resource allocation status and service sequence relationships of workflow services. There are even fewer studies on prediction for workflow graph data due to the lack of available public datasets. In this study, from Alibaba Cloud's Cluster-trace-v2018, we extract nearly one billion offline task instance records into a new dataset, which contains approximately one million workflows and their corresponding directed acyclic graph (DAG) matrices. We propose a novel workflow performance prediction model (DAG-Transformer) to address the aforementioned challenges. In DAG-Transformer, we design a customized position encoding matrix and an attention mask for workflows, which can make full use of workflow sequential and graph relations to improve the embedding representation and perception ability of the deep neural network. The experiments validate the necessity of integrating graph-structure information in workflow prediction. Compared with mainstream deep learning (DL) methods and several classic machine learning (ML) algorithms, the accuracy of DAG-Transformer is the highest. DAG-Transformer can achieve 85-92% CPU prediction accuracy and 94-98% memory prediction accuracy, while maintaining high efficiency and low overheads. This study establishes a new paradigm and baseline for workflow performance prediction and provides a new way for facilitating workflow scheduling.
A Segmentation-Based Multitask Learning Approach for Isolating Switch State Recognition in High-Speed Railway Traction Substation

Stable and reliable operation of high-speed railway requires continuous and reliable power supply of traction substation, and isolating switch is one of the most important electrical devices in high-speed railway traction substation. In this paper, to address the issue of isolating switch accurate localization and state recognition simultaneously, we present an automatic isolating switch segmentation and state recognition framework called ISSSR-Net using multitask learning that consists of two stages. First, an isolating switch segmentation network called ISS-Net is proposed for isolating switch pixel-level segmentation precisely, of which a new structure containing a strip pooling module, a channel attention and three pyramid pooling modules is designed to greatly improve the segmentation and recognition performance, even in complex conditions such as rain, snow and fog. Second, to improve state recognition accuracy, the segmentation map yielded from the ISS-Net and the feature map from the shared backbone are together fed into isolating switch recognition network called ISR-Net to recognize its three states. In addition, a global context block is integrated into ISR-Net to further improve state recognition accuracy. Extensive experimental results on a self-collected dataset from Heishan traction substation corroborate that this paper provides an effective and robust method to achieve isolating switch segmentation and state recognition simultaneously. The M <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU</i> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}~1$ </tex-math></inline-formula> -score of segmentation reach 0.93 and 0.94 respectively, which is better than U-Net and its variants, and other SOTA. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}~1$ </tex-math></inline-formula> -score of recognition reach 1.00, which is higher than HOG+SVM and other deep learning methods that have been experimented.