Reviewerly/exHarmony huggingface dataset:
Self-Sovereign Identity as a Service: Architecture in Practice

Self-sovereign identity (SSI) has gained a large amount of interest. It enables physical entities to retain ownership and control of their digital identities, which naturally forms a conceptual decentralized architecture. With the support of the distributed ledger technology (DLT), it is possible to implement this conceptual decentralized architecture in practice and further bring technical advantages such as privacy protection, security enhancement, high availability. However, developing such a relatively new identity model has high costs and risks with uncertainty. To facilitate the use of the DLT-based SSI in practice, we formulate Self-Sovereign Identity as a Service (SSIaaS), a concept that enables a system, especially a system cluster, to readily adopt SSI as its identity model for identification, authentication, and authorization. We propose a practical architecture by elaborating the service concept, SSI, and DLT to implement SSIaaS platforms and SSI services. Besides, we present an architecture for constructing and customizing SSI services with a set of architectural patterns and provide corresponding evaluations. Furthermore, we demonstrate the feasibility of our proposed architecture in practice with Selfid, an SSIaaS platform based on our proposed architecture.
MKD: Mixup-Based Knowledge Distillation for Mandarin End-to-End Speech Recognition

Large-scale automatic speech recognition model has achieved impressive performance. However, huge computational resources and massive amount of data are required to train an ASR model. Knowledge distillation is a prevalent model compression method which transfers the knowledge from large model to small model. To improve the efficiency of knowledge distillation for end-to-end speech recognition especially in the low-resource setting, a Mixup-based Knowledge Distillation (MKD) method is proposed which combines Mixup, a data-agnostic data augmentation method, with softmax-level knowledge distillation. A loss-level mixture is presented to address the problem caused by the non-linearity of label in the KL-divergence when adopting Mixup to the teacher–student framework. It is mathematically shown that optimizing the mixture of loss function is equivalent to optimize an upper bound of the original knowledge distillation loss. The proposed MKD takes the advantage of Mixup and brings robustness to the model even with a small amount of training data. The experiments on Aishell-1 show that MKD obtains a 15.6% and 3.3% relative improvement on two student models with different parameter scales compared with the existing methods. Experiments on data efficiency demonstrate MKD achieves similar results with only half of the original dataset.
Exploiting Arbitrary Paths for the Simulation of Quantum Circuits with Decision Diagrams

The classical simulation of quantum circuits is essential in the development and testing of quantum algorithms. Methods based on tensor networks or decision diagrams have proven to alleviate the inevitable exponential growth of the underlying complexity in many cases. But the complexity of these methods is very sensitive to so-called contraction plans or simulation paths, respectively, which define the order in which respective operations are applied. While, for tensor networks, a plethora of strategies has been developed, simulation based on decision diagrams is mostly conducted in a straight-forward fashion thus far. In this work, we envision a flow that allows to translate strategies from the domain of tensor networks to decision diagrams. Preliminary results indicate that a substantial advantage may be gained by employing suitable simulation paths-motivating a thorough consideration.
ENCORE Compression: Exploiting Narrow-width Values for Quantized Deep Neural Networks

Deep Neural Networks (DNNs) become a practical machine learning algorithm running on various Neural Processing Units (NPUs). For higher performance and lower hardware overheads, DNN datatype reduction through quantization is proposed. Moreover, to solve the memory bottleneck caused by large data size in DNNs, several zero value-aware compression algorithms are used. However, these compression algorithms do not compress modern quantized DNNs well because of decreased zero values. We find that the latest quantized DNNs have data redundancy due to frequent narrow-width values. Because low-precision quantization reduces DNN datatypes to a simple datatype with less bits, scattered DNN data are gathered to a small number of discrete values and incur a biased data distribution. Narrow-width values occupy a large proportion of the biased distribution. Moreover, an appropriate zero run-length bits can be dynamically changed according to DNN sparsity. Based on this observation, we propose a compression algorithm that exploits narrow-width values and variable zero run-length for quantized DNNs. In experiments with three quantized DNNs, our proposed scheme yields an average compression ratio of 2.99.
Effects of augmented reality game-based learning on students’ engagement

This study is to examine the effects of AR Game-based science learning on students' engagement, learning performance, and satisfaction towards physics learning. Based on the marker-based AR technology, an Augmented Reality Game-based Science Learning (ARGSL) system was designed and developed to support students' physics learning. A quasi-experimental design with two experimental groups and a control group was implemented. The sample of the study included 155 seventh graders from a middle school in eastern China. Results supported that the ARGSL enhanced students learning interaction, performance, and satisfaction compared with the game-based and book-based learning methods. The semi-structured interviews found that the AR technology was widely accepted by students for its usefulness, realness, interest, and interactivity. The AR visualized the virtual magnetic field which makes the difficult physics concept easy to understand. The challenges caused by the AR also give us some implications for AR-supported science education.
Human-guided auto-labeling for network traffic data: The GELM approach

Data labeling is crucial in various areas, including network security, and a prerequisite for applying statistical-based classification and supervised learning techniques. Therefore, developing labeling methods that ensure good performance is important. We propose a human-guided auto-labeling algorithm involving the self-supervised learning concept, with the purpose of labeling data quickly, accurately, and consistently. It consists of three processes: auto-labeling, validation, and update. A labeling scheme is proposed by considering weighted features in the auto-labeling, while the generalized extreme learning machine (GELM) enabling fast training is applied to validate assigned labels. Two different approaches are considered in the update to label new data to investigate labeling speed and accuracy. We experiment to verify the suitability and accuracy of the algorithm for network traffic, applying the algorithm to five traffic datasets, some including distributed denial of service (DDoS), DoS, BruteForce, and PortScan attacks. Numerical results show the algorithm labels unlabeled datasets quickly, accurately, and consistently and the GELM's learning speed enables labeling data in real-time. It also shows that the performances between auto- and conventional labels are nearly identical on datasets containing only DDoS attacks, which implies the algorithm is quite suitable for such datasets. However, the performance differences between the two labels are not negligible on datasets, including various attacks. Several reasons that require further investigation can be considered, including the selected features and the reliability of conventional labels. Even with this limitation of the current study, the algorithm will provide a criterion for labeling data in real-time occurring in many areas.
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

Abstract FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.
The effectiveness of special apps for online piano lessons

ABSTRACTThe paper examines the effectiveness of special apps for online piano lessons and measures the apps' impact compared to the control group that studied without the use of additional software. The study involved 350 students. The effect of Skoove and Simply Piano apps on the learning of piano skills has been analyzed. First-year students from small control group A received the lowest scores, with the mean score of 72.4. Students from small groups B, who used Skoove app, received the mean score of 78.9, which is 8.2% higher compared to the control group. Students from small groups C, who used Simply Piano app, had the mean score of 78.3, which was 7.5% higher compared to the control group. Among fifth-year students, the lowest scores were also obtained by small groups A, with the mean score of 74.7. Small groups B and C performed better: 81.5, or 8.3% higher score than in the control group, and 81.4, or 8.2% higher than the control group. These data reveal the effectiveness of apps for online piano lessons. Attention should be drawn to the fact that both apps were approximately equally effective among students.KEYWORDS: Learning to play pianomusic educationonline learning toolsinteractive environmentcutting-edge technologyonline lessons Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialData will be available on request.Additional informationNotes on contributorsKun LeiKun Lei holds Master's degree, currently is lecturer at Academy of Music, Science and Technology College, Jiangxi Normal University, Nanchang, China. Research interests: music education, online learning tools, interactive environment.
An Actionable Framework for Understanding and Improving Developer Experience

Developer experience is an important concern for software organizations as enhancing developer experience improves productivity, satisfaction, engagement and retention. We set out to understand what affects developer experience through semi-structured interviews with 21 developers from industry, which we transcribed and iteratively coded. Our findings elucidate factors that affect developer experience and characteristics that influence their respective importance to individual developers. We also identify strategies employed by individuals and teams to improve developer experience and the barriers that stand in their way. Lastly, we describe the coping mechanisms of developers when developer experience cannot be sufficiently improved. Our findings result in the DX Framework, an actionable conceptual framework for understanding and improving developer experience. The DX Framework provides a go-to reference for organizations that want to enable more productive and effective work environments for their developers.
Flipped classroom in the meaningful learning of the students of the Basic Education Career: Case study Technical University of Ambato

Quality academic training at different educational levels is consistent with the meaningful learning of students. Faced with the new requirements due to the global pandemic, education put its work into the use of information and communication technologies as well as the application of new pedagogical models. This research article aims to describe and analyze the contribution of the &#x201C;flipped classroom&#x201D; in the meaningful learning of students of the Basic Education Career through the use and design of web 3.0 digital tools. The methodology implemented is experimental-exploratory type research, through a structured questionnaire of 17 questions on a Likert scale which was validated by Cronbach&#x2019;s alpha statistic with a result of 0.846, the TAM model was applied to measure acceptance of digital resources in virtual learning, the ADDIE methodology was applied in the execution and application of the technological tools of the web 3.0 for the students. Favorable results were obtained where it is stated that the flipped classroom contributes to significant learning in university students, allowing greater development of students in daily educational activities, in addition, it was possible to evidence the increase in the frequency of the use of digital documents, multimedia resources and especially web 3.0 resources developed by the teacher to improve virtual teaching, guaranteeing optimal and flexible learning within a social environment, with the help and application of web 3.0 tools.
An Improved Deep Persistent Memory Network for Rician Noise Reduction in MR Images

Magnetic Resonance Imaging (MRI) is extensively employed in medical, scientific and investigative contexts today. Noise on the other hand, restricts the diagnostic utility of MR images by deteriorating their quality during acquisition. The noise in single coil magnitude MRI has stationary Rician distribution and images reconstructed with parallel MR-imaging techniques have non-stationary noise levels. Recently, deep learning models are finding ubiquitous employment in image restoration tasks, owing to their powerful capabilities in learning and solving inverse-problems. Nonetheless, only a few such techniques have been reported to suppress noise in MRI. In this paper, we propose a robust MR image denoising approach based on the concept of memory persistence. Accordingly, we improvised and optimized the deep model of memory networks by introducing a data sensitive activation function and a robust cost function, resulting in a compact design with improved noise filtering, feature preservation and enhanced performance. Experiments on real and synthetic data reveal that the proposed method outperformed state-of-the-art (SoTA) methods.
Semi-supervised constrained graph convolutional network for cross-modal retrieval

Exploiting relationship among samples in cross-modal data plays a key role in the task of cross-modal retrieval, but most of existing methods only extract the correlation from pairwise samples and ignore the relations of unpaired samples. Some graph regularization methods proposed a reasonable paradigm to exploit the correlation from multiple samples. However, limited by the traditional framework, the performance has much room to improve. Moreover, although some existing DNN-based methods achieve excellent performance, the requirement of massive labeled data is also a shortcoming. In this paper, we propose a novel semi-supervised method, named Semi-supervised Constrained Graph Convolutional Network (SCGCN), which adopts graph convolutional network to exploit correlation from batch samples of data with different modalities. For reducing the requirement of labeled data, we design a two stage training procedure: deep supervised learning stage and unsupervised learning stage. In deep supervised learning stage, we integrate two DNN-based semantic encoding networks and a shared classifier into Deep Cross-modal Semantic Encoding (DCSE) module which is trained by supervised learning with labeled data. From DCSE module, we learn a temporary modality-invariant space where the semantic embeddings of samples with different modalities are modality-invariant, and we also learn a classifier which can generate predicted label from the unlabeled data. In unsupervised learning stage, for fully exploiting the correlation from cross-modal data, we design a Constrained Graph Convolutional Network (CGCN) module which utilizes GCN to exploit the correlation and adopts both intra-modal discriminative loss and inter-modal pairwise similar loss to ensure the generated common representation modality-invariant and semantical discriminative. We perform extensive experiments on four conventional datasets and a large scale dataset to demonstrate the effectiveness of proposed approach.
End-to-End Convolutional Neural Network Framework for Breast Ultrasound Analysis Using Multiple Parametric Images Generated from Radiofrequency Signals

Breast ultrasound (BUS) is an effective clinical modality for diagnosing breast abnormalities in women. Deep-learning techniques based on convolutional neural networks (CNN) have been widely used to analyze BUS images. However, the low quality of B-mode images owing to speckle noise and a lack of training datasets makes BUS analysis challenging in clinical applications. In this study, we proposed an end-to-end CNN framework for BUS analysis using multiple parametric images generated from radiofrequency (RF) signals. The entropy and phase images, which represent the microstructural and anatomical information, respectively, and the traditional B-mode images were used as parametric images in the time domain. In addition, the attenuation image, estimated from the frequency domain using RF signals, was used for the spectral features. Because one set of RF signals from one patient produced multiple images as CNN inputs, the proposed framework overcame the limitation of datasets in a broad sense of data augmentation while providing complementary information to compensate for the low quality of the B-mode images. The experimental results showed that the proposed architecture improved the classification accuracy and recall by 5.5% and 11.6%, respectively, compared with the traditional approach using only B-mode images. The proposed framework can be extended to various other parametric images in both the time and frequency domains using deep neural networks to improve its performance.
Universality verification for a set of quantum gates

We provide two simple universality criteria. Our first criterion states that $\mathcal{S}\subset G_d:=U(d)$ is universal if and only if $\mathcal{S}$ forms a $\delta$-approximate $t(d)$-design, where $t(2)=6$ and $t(d)=4$ for $d\geq3$. Our second universality criterion says that $\mathcal{S}\subset G_d$ is universal if and only if the centralizer of $\mathcal{S}^{t(d),t(d)}=\{U^{\otimes t(d)}\otimes \bar{U}^{\otimes t(d)}|U\in \mathcal{S}\}$ is equal to the centralizer of $G_d^{t(d),t(d)}=\{U^{\otimes t(d)}\otimes \bar{U}^{\otimes t(d)}|U\in G_d\}$, where $t(2)=3$, and $t(d)=2$ for $d\geq 3$. The equality of the centralizers can be verified by comparing their dimensions.
BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people's lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 [Formula: see text] and 0.012 MAPE at world level with 31-step forecast and up to 0.99 [Formula: see text] and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.
Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

Abstract Recently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching‐learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi‐objective problem, which we call VMP‐TLBO. VMP‐TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP‐TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP‐TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.