Reviewerly/exHarmony huggingface dataset:
Heterogeneous Hyperedge Convolutional Network

Graph convolutional networks (GCNs) have been developed as a general and powerful tool to handle various tasks related to graph data. However, current methods mainly consider homogeneous networks and ignore the rich semantics and multiple types of objects that are common in heterogeneous information networks (HINs). In this paper, we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph convolutional network architecture that operates on HINs. Specifically, we extract the rich semantics by different metastructures and adopt hyperedge to model the interactions among metastructure-based neighbors. Due to the powerful information extraction capabilities of metastructure and hyperedge, HHCN has the flexibility to model the complex relationships in HINs by setting different combinations of metastructures and hyperedges. Moreover, a metastructure attention layer is also designed to allow each node to select the metastructures based on their importance and provide potential interpretability for graph analysis. As a result, HHCN can encode node features, metastructure-based semantics and hyperedge information simultaneously by aggregating features from metastructure-based neighbors in a hierarchical manner. We evaluate HHCN by applying it to the semi-supervised node classification task. Experimental results show that HHCN outperforms state-of-the-art graph embedding models and recently proposed graph convolutional network models.
An Efficient Image Denoising Based on Weiner Filter and NeighSure Shrink

Weiner filter denoise the image using linear stochastic framework. It eliminates the noise by estimating optimal filter for noisy input image by minimizing the mean square error between the desired image and estimated image. The main drawback of this filter is the performance is reduced when the noise is random and unknown as it has fixed frequency response for all frequencies. The efficiency of this filter can be increased by incorporating method noise thresholding using NeighSure shrink. This paper presents a method which is a blend of Weiner filter and wavelet based NeighSure shrink thresholding. The results indicates that the proposed method is significantly superior than wavelet thresholding, Weiner filter and Gaussian filter with its method noise thresholding techniques in terms of visual quality, Peak Signal to noise ratio and image quality index.
Wearable XR-Technology: Literature Review, Conceptual Framework and Future Research Directions

Extended reality (XR) has revamped the way people experience the physical and the virtual environments, from observation to immersion. XR is an umbrella term that encompasses both augmented reality (AR) and virtual reality (VR), among others. Despite the promising outlook, this nascent technology has been shrouded by uncertain possibilities, making the adoption of XR technology much slower than expected. Moreover, the interdisciplinary applications of XR technology have led to scattered scholarly works and fragmentary insights to be translated into practice. Thus, there is a pressing need for a critical review and synthesis of prior XR research in order to strengthen this emerging field in IS. To accomplish this, the current study identifies and analyses a total of 45 articles through an extensive literature search. As a result, this study identifies the major drivers, barriers, and boundary conditions to XR adoption, classifies and map them into a holistic conceptual model.
Logistic regression for imbalanced learning based on clustering

Class-imbalance is very common in the real world. For the imbalanced class distribution, traditional state-of-the-art classifiers do not work well on imbalanced datasets. In this paper, we apply the well known statistical model logistic regression to imbalanced learning problem and, in order to improve its performance, we use cluster algorithms as the data pre-processing approach to partition majority class data to clusters. Then the logistic regression is learned on the corresponding rebalanced datasets. Experimental results show that, compared with other state-of-the art methods, the proposed one shows significantly better performance on measures of recall, g-mean, f-measure, AUC and accuracy.
Role of Scaling Factor in Robust Digital Watermarking

Digital watermarking has been identified as an efficient tool to provide copyright authentication and protection of digital data. Robustness and Imperceptibility are the two key parameters in designing of watermarking system. It is always desired to design a watermarking system that provides excellent robustness to malicious attacks and the watermark is invisible to human eyes. The robustness and imperceptibility can be controlled by choosing an effective scaling factor. Scaling factor decides on the amount of insertion of watermark in the host image. Higher the value of scaling factor more visible the watermark will be in watermarked image, this leads to poor imperceptibility. However the increase in scaling factor leads to a more robust watermarking system. Finding a trade-off among both is the key issue of research in this area. This paper aims at describing the effect of selection of scaling factor on robustness and imperceptibility by taking different values of scaling factor during each watermarking process. The empirical analysis done in this work shows robustness can be improved at the cost of imperceptibility and vice-versa.
Performance analysis of Lyapunov stability-based and ANFIS-based MRAC

Analysis of two adaptive controller parameter adjustment laws for a model reference adaptive controller has been discussed in this paper. The comparison has been done about applying Lyapunov stability rule and using adaptive neuro fuzzy inference system (ANFIS) to adjust parameter for model reference adaptive control. Discussion of the nature of system, adaptive controller, basic block diagram and control law has been presented. For intense analysis two case studies have been considered. Simulation of two bench-mark process control applications, level control in coupled tank and concentration control in biochemical reactor (BCR) has been discussed. Comparative results have been plotted and discussed for each proposed algorithm. Considered systems have mutual parameter interaction and nonlinear parameter dynamics. Introduction part discusses literature survey, development of the topic and importance of the work. Initially Lyapunov rule-based technique has been applied for control in both cases. With ANFIS-based algorithm, new values of adjustment parameter have been generated. Results shows that performance of ANFIS-based model reference adaptive control (MRAC) gives improved results in presence of system uncertainties.
Effective Video Saliency Mapping for Object Detection using Deep Learning Neural Networks

In this paper, we propose a new novel DNN-based video saliency prediction method.The main aim of this project is to separating foreground and background images and also finding the motion of the objects.The methodogy used in this project is to seperating the foreground images using background modelling techniques(Pixel searching algorithm).The another technique is Homography based search algorithm for block based motion estimation is used to finding the movement of the objects.Saliency object detection is also used to highlighting the particular region in the images.In our base paper[1],context subtraction algorithm is introduced.But in this paper,Convolutional neural networks is used to classifying the foreground objects from background images.We propose an deep convolutional neural network (CNN) Classifier is used to predict intra-frame salience by exploring object and object movement details .The CNN classifier is also used to separating the new objects from background. We also find from our database that a temporary correlation of human attention exists, with a smooth transition of saliency across video frames.Therefore, in our DNN-based approach we create a two-layer convolutionary long-term memory (2C-LSTM) network, using the extracted CNN features as the data.The fuzzy clustering is used to store the information as the processed images in this project.This project can be implemented in real time applications such as defence,forest and miltrary area.Saliency Mapping is also used to reduce the Zipper effect,it mainly occurs in edges.It gives High resolution too. Finally, the proposed system is developed using matlab simulation.The PSNR value is attained above 95%.
An Earth mover's distance-based undersampling approach for handling class-imbalanced data

Imbalanced datasets typically make prediction accuracy difficult. Most of the real-world data are imbalanced in nature. The traditional classifiers assume a well-balanced class distribution for training data but in practical datasets show up an imbalance, thus obscure a classifier and degrade its capability to learn from such imbalanced datasets. Data pre-processing approaches address this concern by using either random undersampling or oversampling techniques. In this paper, we introduce Earth mover's distance (EMD), as a similarity measure, to find the samples similar in nature and eliminate them as redundant from the dataset. Earth mover's distance has received a lot of attention in wide areas such as computer vision, image retrieval, machine learning, etc. The Earth mover's distance-based undersampling approach provides a solution at the data level to eliminate the redundant instances in majority samples without any loss of valuable information. This method is implemented with five conventional classifiers and one ensemble technique respectively, like C4.5 decision tree (DT), k-nearest neighbour (k-NN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB) and AdaBoost technique. The proposed method yields a superior performance on 21 datasets from Keel repository.
Efficient Fuzzy Based Real Time Scheduling of Workflows on Cloud

The technological advancements in service oriented architectures have given rise to Cloud computing as a new platform in the area of distributed computing. Virtualization is a prominent technology adopted by cloud while providing pay as per usage model. Cloud provides the computing resources in terms of different type of virtual machines. VM migration is a fundamental phenomena in cloud systems while providing uninterrupted service and load balancing to its users according to service level agreement. The virtualization and migration poses many challenges and complexities because of many uncertainties. Uncertainties are the inaccurate data in the form of task parameters, variation in the VM performance etc. As the Fuzzy logic can be employed effectively in decision making with inaccurate data we propose fuzzy logic based real time scheduling algorithm to deal with the uncertainties. The proposed algorithm in this paper performs in a better way in meeting of deadlines of real time workflows under uncertain data and VM migrations.
Digital Watermarking for Image Authentication using Spatial-Scale Domain based Techniques

This paper aims in presenting a thorough comparison of performance and usefulness of concept of spatial-scale domain based techniques in digital watermarking in order to sustain the ownership, security and true authentication. Spatial-scale based image watermarking techniques provides the information of 2-Dimensional (2-D) signal at different scales and levels, which is desirable for image watermarking. Further, these techniques emerged as a powerful and efficient tool to overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques. The spatial-scale based watermarking techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) have been selected for watermarking process. Further, the comparison of performance of the selected watermarking techniques have been carried out in terms of different metrics, such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Tamper Assessment Factor (TAF) and Mean Structural Similarity Index (MSSIM). Analysis of result shows that multi-directional and shift-invariant NSCT technique outperforms the SWT and DWT based image watermarking techniques in terms of authentication, better capture quality, and tampering resistance, subjective and objective evaluation.
A Feasible Surface Patterning Method for SEM-DIC: Achieving High-Resolution <i>In Situ</i> Mapping of Local Strain and Microstructure to Reveal the Effect of Slip Transfer on Shear Strain Near Grain Boundaries

This paper exploited an alternative approach to prepare high-quality speckle patterns by uniformly dispersing nano-silica particles onto sample surfaces, helping digital image correlation (DIC) acquire the maximum spatial resolution of local strain up to 92 nm. A case study was carried out by combining this speckle pattern fabrication method with SEM-DIC and electron backscattering diffraction (EBSD). Thus, in situ mapping of local strain with ultra-high spatial resolution and microstructure in commercially pure titanium during plastic deformation could be achieved, which favored revealing the effect of slip transfer on shear strain near grain boundaries. Moreover, the slip systems could be easily identified via the combination of the SEM-DIC and EBSD techniques even though no obvious deformation trace was captured in secondary electron images. Additionally, the complex geometric compatibility factor $( {m}^{\prime}_c)$ relating to geometric compatibility factors (mʹ) and Schmid factors was proposed to predict the shear strain (εxy) at grain boundaries.
Federated Learning Stability Under Byzantine Attacks

Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of model update transparency which makes it vulnerable to several types of attacks. In particular, based on detailed convergence analyses, we show in this paper that when the traditional model-combining scheme is used, even a single Byzantine node that keeps sending random reports will cause the whole FL model to diverge to non-useful solutions. A low complexity model combining approach is also proposed to stabilize the FL system and make it converge to a suboptimal solution just by controlling the model norm. The Physikalisch-Technische Bundesanstalt extra-large electrocardiogram (PTB-XL ECG) dataset is used to validate the findings of this paper and show the efficiency of the proposed approach in identifying heart anomalies.
Emergency learning during the COVID-19 pandemic: experiences and reflections from a virtual community of practice

Purpose This study aims to reflect on the effects of the COVID-19 pandemic on education and draw some tentative assumptions from the information and experiences gathered from participants in this community about their possible impact on the future of education. Design/methodology/approach This study uses a single-case descriptive case study that analyses some indicators of changes in teaching and learning that have taken place to date in the pandemic period as seen from Docentes en línea, a virtual community of practice of the Universidad Nacional de La Plata, Argentina. Findings A number of shortcomings in the educational system, technological infrastructure and teacher formation in many of our members’ countries. Concurrently, rising interest in the use of information and communication technology (ICT) in education and a new awareness of possible changes. Research limitations/implications Part of the data comes from informal sources; therefore, they cannot be completely systematised or quantified. Besides, the results are provisional because the pandemic has not ended. Originality/value This analysis was conducted on the basis of first-hand data collected in the activity developed by the members of this virtual community of practice. There is little research on this type of activity at the moment.
A Chaos-Based Image Encryption Scheme Using the Hamming Distance and DNA Sequence Operation

In this study, we introduced a new memristive chaotic system with the rich dynamic behavior, and then we proposed a chaotic-based image encryption scheme which is based on the permutation–confusion–substitution structure. In our scheme, the Hamming distance is used to design a plain-related chaotic system initial condition, and the generated chaotic sequences are assigned to permutation, diffusion, and substitution stages. In the permutation stage, an effect pixel confusion is implemented through a new permutation approach, which is a double-ended select-swap scrambling strategy. In the diffusion stage, DNA XOR operation is implemented followed by DNA triploid mutation which is introduced to enhance the strength of our encryption system. A number of experiments and extensive safety analysis have been carried out and the results fully justify that our scheme not only ensures desirable security but also has superior efficiency.
Interpretability of Deep Learning

Deep Learning achieves surprising performance in many real-world tasks. However, on a black-box approach, computational techniques have been applied without a strong critical understanding of their properties. In this paper, we review the current methodologies and techniques about improving the interpretability of Deep Learning from different research directions. Some works are based on analysis of the learning process, some lay more emphasis on interpreted network architecture, and others intend to design self-interpretable Deep Learning models. This article analyzes the popular and advanced works in these fields and provides a future look for Deep Learning researchers.
Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others, which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.
A Multifactor Multilevel and Interaction Based (M2I) Authentication Framework for Internet of Things (IoT) Applications

Existing authentication solutions proposed for Internet of Things (IoT) provide a single Level of Assurance (LoA) regardless of the sensitivity levels of the resources or interactions between IoT devices being protected. For effective (with adequate level of protection) and efficient (with as low overhead costs as possible) protections, it may be desirable to tailor the protection level in response to the sensitivity level of the resources, as a stronger protection level typically imposes a higher level of overhead costs. In this paper, we investigate how to facilitate multi-LoA authentication for IoT by proposing a multi-factor multi-level and interaction based (M2I) authentication framework. The framework implements LoA linked and interaction based authentication. Two interaction modes, P2P (Peer-to-Peer) and O2M (One-to-Many), are investigated via the design of two corresponding protocols. Evaluation results show that adopting the O2M interaction mode in authentication in the related use-case scenarios can cut communication cost significantly; compared with that of the Kerberos protocol, the O2M protocol reduces the communication cost by 42&#x0025;&#x007E;45&#x0025;. The protocols are also more efficient. The P2P and O2M protocol, respectively, reduce the computational cost by 70&#x0025;&#x007E;72&#x0025; and 81&#x0025;&#x007E;82&#x0025;, in comparison with that of Kerberos. The evaluation results also show that the two-factor authentication option costs twice as much as that of the one-factor option.
Understanding the factors that influence software testing through moments of translation

Purpose Organisations make use of different tools and methods in testing software to ensure quality and appropriateness for business needs. Despite the efforts, many organisations continue to have challenges with the quality of their software. This has impacted the service that the organisation provides to their customers and business partners. Software quality challenges persist because of two main reasons: some organisations do not conduct testing and evaluation of their software, and many organisations pay less attention to the non-technical activities in the testing and evaluation of software in their organisations. Thus, the purpose of this paper is to understand the factors that influence software testing through empirical study. Design/methodology/approach This study used actor–network theory (ANT) to assess the factors that influence the testing and evaluation of software, using a South African organisation as case in the study. Qualitative data was collected from 14 participants using semi-structured interview techniques. The interpretivist approach was applied in the analysis of the data. Findings From the analysis, six factors, software evaluation, process oriented, implementation policy, change management, power relationship and organisational structure, were found to be of influence on software testing. Based on the factors, a framework was developed to help software testers and managers understand how to customise their testing tools. Originality/value The originality of this study comes from its empirical nature, through which it contributes to practice by proposing a model for practical actions towards improving software testing in organisations. Also, this study contributes to ANT usefulness from the point of investigative effectiveness.
Fast evolution of single qubit gate in non-adiabatic geometric quantum computing

We implemented arbitrarythe arbitrary single qubit gates of geometric quantum computing for a three-level system in a single-shot manner. The evolution time of the gate has been minimized by considering the shortest trajectory of the state on the Bloch sphere. The duration of gates grows from zero with the rotation angle γ, and the tested T gate time can be reduced to ∼40% of those in the traditional orange-sliced-shaped path non-adiabatic holonomic quantum computing (NHQC) scheme by the parametrization of Rabi frequency. We also demonstrated that certain pulses are robust against static detuning errors and Rabi errors. The time-dependent detuning and Rabi frequency are found to be proportional to each other by a constant which is determined by the geometric phase. In this way, some previous NHQC schemes can be treated as special cases in our generalized model.