Reviewerly/exHarmony huggingface dataset:
Instructor presence, interactive tools, student engagement, and satisfaction in online education during the COVID-19 Mexican lockdown

This paper aims to present empirical evidence that supports and quantifies the relationships among the following features in a Mexican university: interactive collaboration software tools used in online classes, the perceived presence of instructors, student engagement, and student satisfaction with online classes during the COVID-19 lockdown. We collected data from 1417 participants from five different academic programs using a survey method. Data were analyzed using SPSS v25 and WarpPLS. Findings show that the interactive communication tool has a positive impact on perceived instructor presence, which also positively impacts student engagement and satisfaction. These relationships were also found to be true after analyzing each academic program. Therefore, both the interactive communication tool and the instructor play important roles in the online learning process because they positively influence student engagement and satisfaction. Our results are consistent with previous work conducted before the pandemic and contribute to understanding the teaching and learning process during the COVID-19 pandemic.
The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.
Enhancing trust through digital Islamic finance and blockchain technology

Purpose This paper aims to provide a reflective discussion on the different avenues of blockchain application in Islamic finance in promoting trust and transparency for increased accountability between parties involved in the delivery of Sharīʿah-compliant products and services. Design/methodology/approach This paper discusses on blockchain benefits in Islamic finance while providing an illustration with smart Sukuk. Having identified the advantages of the development of Islamic financial technology (i-FinTech), this study ends by debating a couple of challenges (computational codification of Sharīʿah principles and environmental impact) that have to be addressed to promote the development of a real sustainable Islamic FinTech. Findings This paper also identifies two challenges in using blockchain in i-Fintech. The first challenge refers to the extent to which Sharīʿah principles can be computationally encoded. Blockchain makes public all transactions that ease Sharīʿah compliance checks and determine if these transactions are Islamic in nature but this check can be done only after their operation. The second challenge is related to the algorithmic protocol used to validate smart contracts (including smart Sukuk). This situation calls into question the principles of Maqasid al-Sharīʿah according to which transactions should not harm society. Originality/value In the current debates related to the development of Islamic FinTech, this paper also identifies two challenges in using blockchain in i-Fintech.
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personalized to each different client. While several methods have been proposed to achieve personalization, they are typically limited to a single local device, which may incur bias or overfitting since data in a single device is extremely limited. In this paper, we attempt to realize personalization beyond a single client. The motivation is that during the FL process, there may exist many clients with similar data distribution, and thus the personalization performance could be significantly boosted if these similar clients can cooperate with each other. Inspired by this, this paper introduces a new concept called federated adaptation, targeting at adapting the trained model in a federated manner to achieve better personalization results. However, the key challenge for federated adaptation is that we could not outsource any raw data from the client during adaptation, due to privacy concerns. In this paper, we propose PFA, a framework to accomplish Privacy-preserving Federated Adaptation. PFA leverages the sparsity property of neural networks to generate privacy-preserving representations and uses them to efficiently identify clients with similar data distributions. Based on the grouping results, PFA conducts an FL process in a group-wise way on the federated model to accomplish the adaptation. For evaluation, we manually construct several practical FL datasets based on public datasets in order to simulate both the class-imbalance and background-difference conditions. Extensive experiments on these datasets and popular model architectures demonstrate the effectiveness of PFA, outperforming other state-of-the-art methods by a large margin while ensuring user privacy. We will release our code at: https://github.com/lebyni/PFA.
Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms

Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The Moth-flame optimization (MFO) algorithm is one of the swarm intelligence algorithms and it can be used with constrained and unknown search spaces. However, there are still some defects in its performance, such as low solution accuracy, slow convergence, and insufficient exploration capability. This study improves the basic MFO algorithm from the perspective of improving exploration capability and proposes a hybrid swarm-based algorithm called SMFO. The essential notion is to further explore and scan the feature space with taking advantages of the sine cosine strategy. We methodically investigated the efficacy, solutions, and optimization compensations of the developed SMFO using more than a few demonstrative benchmark tests, together with unimodal, multimodal, hybrid and composition tasks, and two widely applied engineering test problems. The simulations point towards this fact that the diversification and intensification inclinations of the original MFO and its convergence traits are fortunately upgraded. The findings and remarks show that the suggested SMFO is a favourable algorithm and it can show superior efficacy compared to other techniques.
A Novel Color Image Encryption Algorithm Based on Three-Dimensional Chaotic Maps and Reconstruction Techniques

In the paper, a novel color image encryption scheme is proposed by use of the three-dimensional chaotic maps and some data reconstruction techniques.In the diffusion process of the encryption algorithm, the three-dimensional chaotic Logistic map is introduced to changes the pixel value of the plain image.Meanwhile, in the confusion process, the three-dimensional chaotic Cat map is applied to deal with the position of the image pixels.Moreover, some data reconstruction techniques are employed for the better encryption result.Furthermore, the corresponding decryption algorithm is also given.The significant advantages of our method are the effectiveness and simplicity, and our proposed method breaks the correlation between image pixel layers, offers a large key space and against differential and statistical attacks effectively.Finally, the superiority of the proposed algorithm will be shown by the experimental results, security analysis, and the corresponding comparisons with literature.
BlockFLA

Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets with each other, or a third-party. This makes FL particularly suitable for settings where data privacy is desired.
One decade of quantum optimal control in the chopped random basis

The chopped random basis (CRAB) ansatz for quantum optimal control has been proven to be a versatile tool to enable quantum technology applications such as quantum computing, quantum simulation, quantum sensing, and quantum communication. Its capability to encompass experimental constraints-while maintaining an access to the usually trap-free control landscape-and to switch from open-loop to closed-loop optimization (including with remote access-or RedCRAB) is contributing to the development of quantum technology on many different physical platforms. In this review article we present the development, the theoretical basis and the toolbox for this optimization algorithm, as well as an overview of the broad range of different theoretical and experimental applications that exploit this powerful technique.
Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

Background: Electronic Health Record (EHR) systems are used as an efficient and effective technique for sharing patient's health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally. However, the existing EHR systems mostly lack in providing appropriate security, entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures. Objective: To solve this delicate problem, we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems. Methodology: In our EHR blockchain system, Peer nodes from various organizations (stakeholders) create a ledger network, where channels are created to enable secure and private communication between different stakeholders on the ledger network. Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider (MSP) component of the fabric architecture. Results: We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network. The proposed fabric architecture provides a secure, transparent and immutable mechanism to store, share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders. It ensures interoperability, scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.
A novel hybrid gravitational search particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a member of the swarm computational family and widely used for solving nonlinear optimization problems. But, it tends to suffer from premature stagnation, trapped in the local minimum and loses exploration capability as the iteration progresses. On the contrary, Gravitational Search Algorithm (GSA) is proficient for searching global optimum, however, its drawback is its slow searching speed in the final phase. To overcome these problems in this paper a novel Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO) is presented. The key concept behind the proposed method is to merge the local search ability of GSA with the capability for social thinking (gbest) of PSO. To examine the effectiveness of these methods in solving the abovementioned issues of slow convergence rate and trapping in local minima five standard and some modern CEC benchmark functions are used to ensure the efficacy of the presented method. Additionally, a DNA sequence problem is also solved to confirm the proficiency of the proposed method. Different parameters such as Hairpin, Continuity, H-measure, and Similarity are employed as objective functions. A hierarchal approach was used to solve this multi-objective problem where a single objective function is first obtained through a weighted sum method and the results were then empirically validated. The proposed algorithm has demonstrated an extraordinary performance per solution stability and convergence.
A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.
Noise-Assisted Quantum Autoencoder

Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum autoencoders fail to compress and recover high-rank mixed states. In this work, we discuss the fundamental properties and limitations of the standard quantum autoencoder model in more depth, and provide an information-theoretic solution to its recovering fidelity. Based on this understanding, we present a noise-assisted quantum autoencoder algorithm to go beyond the limitations, our model can achieve high recovering fidelity for general input states. Appropriate noise channels are used to make the input mixedness and output mixedness consistent, the noise setup is determined by measurement results of the trash system. Compared with the original quantum autoencoder model, the measurement information is fully used in our algorithm. In addition to the circuit model, we design a (noise-assisted) adiabatic model of quantum autoencoder that can be implemented on quantum annealers. We verified the validity of our methods through compressing the thermal states of transverse field Ising model and Werner states. For pure state ensemble compression, we also introduce a projected quantum autoencoder algorithm.
Device-independent quantum key distribution with asymmetric CHSH inequalities

The simplest device-independent quantum key distribution protocol is based on the Clauser-Horne-Shimony-Holt (CHSH) Bell inequality and allows two users, Alice and Bob, to generate a secret key if they observe sufficiently strong correlations. There is, however, a mismatch between the protocol, in which only one of Alice's measurements is used to generate the key, and the CHSH expression, which is symmetric with respect to Alice's two measurements. We therefore investigate the impact of using an extended family of Bell expressions where we give different weights to Alice's measurements. Using this family of asymmetric Bell expressions improves the robustness of the key distribution protocol for certain experimentally-relevant correlations. As an example, the tolerable error rate improves from 7.15% to about 7.42% for the depolarising channel. Adding random noise to Alice's key before the postprocessing pushes the threshold further to more than 8.34%. The main technical result of our work is a tight bound on the von Neumann entropy of one of Alice's measurement outcomes conditioned on a quantum eavesdropper for the family of asymmetric CHSH expressions we consider and allowing for an arbitrary amount of noise preprocessing.
A Two-Phase Approach using Mask R-CNN and 3D U-Net for High-Accuracy Automatic Segmentation of Pancreas in CT Imaging

The size, shape, and position of the pancreas are affected by the patient characteristics such as age, sex, adiposity. Owing to more complex anatomical structures (size, shape, and position) of the pancreas, specialists have some difficulties in the analysis of pancreatic diseases (diabetes, pancreatic cancer, pancreatitis). Therefore, the treatment of the disease requires enormous time and depends on the experience of specialists. In order to decrease the rate of pancreatic disease deaths and to assist the specialist in the analysis of pancreatic diseases, automatic pancreas segmentation techniques have been actively developed in the research article for many years. Although the rapid growth of deep learning and proving satisfactory performance in many medical image processing and computer vision applications, the maximum Dice Similarity Coefficients (DSC) value of these techniques related to automatic pancreas segmentation is only around 85% due to complex structure of the pancreas and other factors. Contrary to previous techniques which are required significantly higher computational power and memory, this paper suggests a novel two-phase approach for high-accuracy automatic pancreas segmentation in computed tomography (CT) imaging. The proposed approach consists of two phases; (1) Pancreas Localization, where the rough pancreas position is detected on the 2D CT slice by adopting Mask R-CNN model, (2) Pancreas Segmentation, where the segmented pancreas region is produced by refining the candidate pancreas region with 3D U-Net on the 2D sub-CT slices generated in the first phase. Both qualitative and quantitative assessments of the proposed approach are performed on the NIH data set. In order to evaluate the achievement of the recommended approach, a total of 16 different automatic pancreas segmentation techniques reported in the literature are compared by utilizing performance assessment procedures which are Dice Similarity Coefficient (DSC), Jaccard Index (JI), Precision (PRE), Recall (REC), Pixel Accuracy (ACC), Specificity (SPE), Receiver Operating Characteristics (ROC) and Area under ROC curve (AUC). The average values of DSC, JI, REC and ACC are computed as 86.15%, 75.93%, 86.27%, 86.27% and 99.95% respectively, which are the best values among well-established studies for automatic pancreas segmentation. It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.
DGCL

Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics. Distributed GNN training is essential for handling large graphs and reducing the execution time. However, for distributed GNN training, a peer-to-peer communication strategy suffers from high communication overheads. Also, different GPUs require different remote vertex embeddings, which leads to an irregular communication pattern and renders existing communication planning solutions unsuitable. We propose the distributed graph communication library (DGCL) for efficient GNN training on multiple GPUs. At the heart of DGCL is a communication planning algorithm tailored for GNN training, which jointly considers fully utilizing fast links, fusing communication, avoiding contention and balancing loads on different links. DGCL can be easily adopted to extend existing single-GPU GNN systems to distributed training. We conducted extensive experiments on different datasets and network configurations to compare DGCL with alternative communication schemes. In our experiments, DGCL reduces the communication time of the peer-to-peer communication by 77.5% on average and the training time for an epoch by up to 47%.
VINS: Visual Search for Mobile User Interface Design

Searching for relative mobile user interface (UI) design examples can aid interface designers in gaining inspiration and comparing design alternatives. However, finding such design examples is challenging, especially as current search systems rely on only text-based queries and do not consider the UI structure and content into account. This paper introduces VINS, a visual search framework, that takes as input a UI image (wireframe, high-fidelity) and retrieves visually similar design examples. We first survey interface designers to better understand their example finding process. We then develop a large-scale UI dataset that provides an accurate specification of the interface’s view hierarchy (i.e., all the UI components and their specific location). By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure. The framework achieves a mean Average Precision of 76.39% for the UI detection and high performance in querying similar UI designs.
Feature Fusion for Online Mutual Knowledge Distillation

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks and generates meaningful feature maps. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each subnetwork, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial than other alternative methods because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performances of both sub-networks and the fused classifier, and the aspect of generating meaningful feature maps. The code is available at this link <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup>