Reviewerly/exHarmony huggingface dataset:
Multi-dimensional support for development of visual literacy in engineering education

Visual literacy is becoming increasingly important in the digitalized economy, as it determines the ability to perform and communicate effectively. The need to close skills gap and provide engineers and engineering educators with practical, transferable skills based around visual literacy is a topic attracting growing interest at national and international levels. Numbers of reports lead to the conclusion, that not only a deep understanding of visual competences needs among people at technical education is required, but also mechanism of transferring them into ‘teachable’ innovative pedagogical strategies within traditional engineering courses, and mandating curricular improvements among stakeholders involved in education process. Thus, the article presents a case study that describes deeply an example of the initiative aimed at multi-dimensional support for development of visual literacy framework in engineering education. The VLEE initiative, undertaken by 6 organizations recruited in 5 European countries, was carefully designed to address studying and developing visual literacy simultaneously in various areas and formats, hence generating impact in numerous fields. The paper addresses the questions referring to measuring and developing visual literacy and to assess short-term and long-term initiatives aimed at achieving the desired development impact. The achieved results enriched the existing knowledge through proposing the complex approach to organize the appropriate visual literacy oriented education as a spectrum of interconnected analytical and action-focus activities required to be taken to achieve the proper impact on target group. The practical application of proposed approach in VLEE project was presented by indicating the role of related outputs in a process of developing the short and long-term impact strategy.
A Biometric Approach for Electronic Healthcare Database System using SAML - A Touchfree Technology

In Healthcare 4.0, the enhanced view is to analyze the highly affected people in one common disease. In 2021, the global pandemic scenario poses the greatest threat to human life owing to Covid-19. In this approach, medical records and their accessibility play an important role in dealing with illnesses and problems in electronic healthcare record systems (EHR). The proposed system provides a solution for tracking the affected person, who is not in control to isolate them during the high symptoms situation. Security Assertion Markup Language (SAML) is an authentication protocol that helps to prevent other human lives to be saved by providing mobile based health care systems. In such cloud access the credentials are turned in important credential authentication such that pass code allows authorizing doctors and caretakers to access it. Since SAML transactions are enabled with border control such that proof of Covid positive people can be recognized easily with the fingerprint scanning. Also proposed work using the SAML protocol for authentication which helps to provide environment for mobile applications for COVID-observed patients to identify the user who are remotely in access by applying the bio-id generated and updated in Cloud service provider. Also on boarding people who are available for supporting the patients. The main objective of the proposed work using healthcare 4-0 as authentication system is to provide Covid patient instant support for availability of necessary control through biometric border control such that the instant death can be avoided by finding the positive patients using their fingerprint and performance the least access from the huge database. Results from implementation using various affected patients from Covid strategies show the reliable data and secured protocol using SAML gives a better performance compared to other services.
Design and Implementation of Information System Based on Java Technology Platform

In order to solve the problem that the treatment plan, patient information, and treatment results (images and other data) of the hospital tumor center are all in the original independent system, it is very inconvenient for doctors to carry out treatment and query treatment results. The principle of the MIS system is adopted. The Java technology platform has constructed a set of hospital management information system with full process and full element management.
Face Recognition Based on Principal Component Analysis and Support Vector Machine Algorithms

In order to improve the efficiency of face recognition, a face recognition method based on principal component analysis and support vector machine is proposed. Principal component analysis is used to transform the face image into a new feature space, which can reduce the dimension of feature space and eliminate the correlation and noise between image features. Then, a classification algorithm is obtained by using support vector machine algorithm. The test set is classified, and the probability that the classification probability is greater than the given threshold is added to the training set as the true value to improve the prior information of the target. Through the iterative use of support vector machine, a better recognition effect is obtained. In the open face database, the detection accuracy is improved by 5% compared with the classical algorithm.
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition

Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work.
Prototypical Graph Neural Network for Few-Shot Learning

The graph neural network (GNN) can significantly improve the performance of few-shot learning due to its ability to automatically aggregate sample node information. However, many previous GNN works are sensitive to noise. In this paper, a few-shot image classification algorithm (Proto-GNN) based on the prototypical graph neural network is presented. First, convolutional neural network (CNN) is used to obtain the feature vectors of the support set samples, which can be used to calculate the prototype of each category. Then, the feature vectors of the support set, prototype vectors and the feature vectors of the query set are used to form a completely connected graph. Finally, Proto-GNN is built and trained. Due to the addition of the prototype vectors, the query set samples obtain more category information, which is more conducive to classification. The experimental results show that the model in this paper has a better classification performance on the Omniglot data set.
Cooperative Digital Humanities: A Methodology

Researchers in the digital humanities use visualization with ever increasing frequency to address their cultural data challenges. However, interdisciplinary and collaborative projects with visualization researchers are associated with various and common research challenges, such as cooperative communications and methodological differences. A number of strategies have been proposed to guide and steer general cooperative projects to realize the common team objectives. In this paper, we propose a methodological workflow for interdisciplinary digital humanities and visualization research based on our previous work and experience. Our methodological workflow consists of three spaces, three channels, and three criteria. The three spaces feature the main collaborative entities: problem, task, and solution spaces. The three channels illustrate the connections between spaces and include communication, pre-visualization, and evaluation channels. The three quality criteria include expressiveness, purposefulness, and trustfulness. These three criteria are included to ensure useful outcomes from each space. In each section of the workflow, we draw from our previous cooperations to demonstrate the effectiveness of the workflow.
Two shorter proofs on the inverse and differential spectrum of Bracken-Leander exponent

In this paper, we investigate the Bracken-Leander power function F(x)=x22k+2k+1 over F24k where k is an odd positive integer, and first give a much shorter proof on the binary representation of its inverse based on the Chinese Remainder Theorem. Besides, based on a known connection between the differential spectrum and Fourier spectrum of a function, we also give another shorter proof to determine the differential spectrum of F(x). These two results are solved recently with quite involved skills by Kölsch (2020) [7], Xiong and Yan (2017) [10], respectively. We hope that our work is helpful to have a better understanding of this function because of its importance in the construction of S-boxes in block ciphers.
Investigating the Effects of Temperature and Relative Humidity on Performance Ratio of a Grid Connected Photovoltaic System

The size and the cost of photovoltaic (PV) systems are dependent on the performance ratio of solar cells. Current studies in literature usually determines the size according to the total solar radiation received on the surface of solar modules. However, it is a fact that the energy output of a solar module is also effected by weather conditions of the location where the system is mounted. Thus for an efficient design, weather conditions must be taken into consideration to determine the size. In this paper the performance ratio of an existing photovoltaic system was established and the effect of weather conditions on the energy conversion was analyzed. For this purpose, the reference yield of the system was estimated in terms of solar radiation components received on the surface of solar modules for a specific period. Then the performance ratio was calculated by dividing the measured final yield to the estimated reference yield. In conclusion the change in performance ratio was discussed for different temperature and relative humidity values. Finally, the effect of meteorological inputs on PV system performance is investigated based on a back propagation artificial neural network approach. In conclusion, theoretical and computational results are evaluated.
One-shot learning for the long term: consolidation with an artificial hippocampal algorithm

Standard few-shot experiments involve learning to efficiently match unseen samples by class. We claim that few-shot learning should be long term, assimilating knowledge for the future, without forgetting previous concepts. In the mammalian brain, the hippocampus is understood to play a significant role in this process, by learning rapidly and consolidating knowledge to the neocortex incrementally over a short period. In this research we tested whether an artificial hippocampal algorithm (AHA), could be used with a conventional Machine Learning (ML) model that learns incrementally analogous to the neocortex, to achieve one-shot learning both short and long term. The results demonstrated that with the addition of AHA, the system could learn in one-shot and consolidate the knowledge for the long term without catastrophic forgetting. This study is one of the first examples of using a CLS model of hippocampus to consolidate memories, and it constitutes a step toward few-shot continual learning.
Graph-based Visual Manipulation Relationship Reasoning in Object-Stacking Scenes

In object-stacking scenes, robotic manipulation is one of the most important research topics in robotics. It is particularly significant to reason object relationships and obtain intelligent manipulation order for more advanced interaction between the robot and the environment. However, many existing methods focus on individual object features and ignore contextual information, leading to great challenges in efficiently reasoning manipulation relationship. In this paper, we introduce a novel graph-based visual manipulation relationship reasoning architecture that directly outputs object relationships and manipulation order. Our model first extracts features and detects objects from RGB images, and then adopts Graph Convolutional Network (GCN) to collect contextual information between objects. Moreover, a relationship filtering network is built to reduce object pairs before reasoning and improve the efficiency of relation reasoning. The experiments on the Visual Manipulation Relationship Dataset (VMRD) show that our model significantly outperforms previous methods on reasoning object relationships in obiect-stackina scenes.
Measurement-device-independent QSDC using multiple Swap Circuits

Quantum Secure Direct Communication (QSDC) is a distinctive protocol of quantum communication. It transmits confidential message straight through a quantum channel without the requirement of a secret key and ciphertext. It is also helpful in preventing eavesdropper during transmission. In this article, during the detailed study about QSDC, challenges faced while implementing the mitigation techniques w.r.t possible threats available in practical QSDC, have been studied and demonstrated. From the experiments and results it is observed that practical QSDC has inherent challenges. In actual quantum communication systems, flaws in measurement devices could give rise to disclosure of secret information to Eve without even being noticed. Measurement-device-independent QSDC protocol has been proposed using more than one swapping circuit as a mitigation technique. The efficacy of proposed protocol is verified using quantum simulator and quantum machine in the ideal scenario as well as possible threat scenarios. The results demonstrate its potential over other protocols of quantum communication and contribution towards enabling secure communication.
Stereo matching network based on AANet+ improved attention mechanism

The stereo matching method based on deep learning is developing rapidly. Before the AANet+ adaptive aggregate stereoscopic matching network was put forward, the 3D convolution-based deep learning network occupied the core position, but it had the disadvantage of large number of parameters and high memory consumption in cost volume and cost aggregation. With the advent of AANet+, it solves the defects of 3D convolution to some extent by virtue of deformable convolution and intra-scale aggregation modules and cross-scale aggregation modules. However, there are also shortcomings in AANet+, which uses hourglass stacking modules to start feature extraction from multiscale directions, but uses convolution to lose some of its information when fused. Therefore, this paper uses the channel attention module ECA to improve the feature extraction module of AANet+, and introduces the channel attention module in the traditional hourglass stacking module to extract more rich features. In addition, channel attention modules are introduced to obtain multi-scale features to reduce information loss and increase contextual connections. Experimental results show that the proposed improved network has achieved better results in synthetic data set Scene Flow and realworld scene data set KITTI2015 than AANet+.
Layerwise Approximate Inference for Bayesian Uncertainty Estimates on Deep Neural Networks

A proper representation of predictive uncertainty is vital for deep neural networks (DNNs) to be applied in safety-critical domains such as medical diagnosis and self-driving. State-of-the-art (SOTA) variational inference approximation techniques provide a theoretical framework for modeling uncertainty, however, they have not been proven to work on large and deep networks with practical computation. In this study, we develop a layerwise approximation with a local reparameterization technique to efficiently perform sophisticated variational Bayesian inference on very deep SOTA convolutional neural networks (CNNs) (VGG16, ResNet variants, DenseNet). Theoretical analysis is presented to justify that the layerwise approach remains a Bayesian neural network. We further derive a SOTA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -divergence objective function to work with the layerwise approximate inference, addressing the concern of underestimating uncertainties by the Kullback-Leibler divergence. Empirical evaluation using MNIST, CIFAR-10, and CIFAR-100 datasets consistently shows that with our proposal, deep CNN models can have a better quality of predictive uncertainty than Monte Carlo-dropout in detecting in-domain misclassification and excel in out-of-distribution detection.
Cryptosystem Recognition Scheme Based on Convolution Features

Cryptosystem recognition is an important link in cryptanalysis, and ciphertext feature extraction is very important in cryptosystem recognition. In this paper, a ciphertext feature extraction method based on linear transformation and convolution sampling is proposed. A large number of plaintext data are encrypted as the data set, and a random forest is used to construct a classifier to recognize the two-class classification and multi-class classification of four cryptographic systems, such as AES, 3DES, Blowfish and RSA. The experimental results show that the recognition performance of convolution features is better and the accuracy of recognition in multi-class classification scene is improved. The accuracy of two class classification recognition is more than 80% and that of multi-class classification recognition is more than 70%.
Monocular Depth Recovery Based on Self-Attention Mechanism and Transfer Learning

The task of depth recovery is to calculate the depth map using only the rgb images. The existing deep network greatly compresses the resolution in the process of feature extraction, resulting in serious loss of depth information in the edge area of the object with large depth changes and the area far away from the sensor. This paper proposes a convolutional neural network based on self-attention mechanism, which can use a single rgb image to calculate its corresponding high-resolution depth map. Specifically, we use the Fully Convolutional Network with jump connections and bilinear upsampling, and add a self-attention mechanism module inside it. The self-attention mechanism module aggregates the features at each location through the weighting and selective aggregation of the features at all locations. Compared with the baseline, our method has more obvious object edge information, and also has better results on some indicators.
UUV Trajectory Prediction Based on GRU Neural Network

Aiming at the problems of large simplification and low accuracy of traditional trajectory prediction models, combined with the characteristics of time sequence, continuity, and interactivity of UUV trajectories, an unknown UUV trajectory prediction method based on a gated unit recursive (GRU) neural network model is proposed. The MinMaxScaler method is used to normalize the trajectory data; in order to improve the prediction accuracy, the adaptive parameter adjustment algorithm and adam algorithm are used to optimize the network structure of the established GRU-based trajectory prediction model, without human intervention for parameter adjustment. Finally, the trajectory prediction accuracy of GRU trajectory prediction model and BP trajectory prediction model are analyzed through simulation experiments. The experimental results show that the root mean square error is about 3.96m on the x-axis and 1.00m on the y-axis. The trajectory prediction model based on GRU has faster prediction speed and higher prediction accuracy for unknown UUV trajectory after bearings-only detection.
Strong quantum nonlocality for multipartite entangled states

Recently, Halder et al. (in Phys Rev Lett 122:040403, 2019) present two sets of strong nonlocality of orthogonal product states based on the local irreducibility. However, for a set of locally indistinguishable orthogonal entangled states, the remaining question is whether the states can reveal strong quantum nonlocality. Here we present a general definition of strong quantum nonlocality based on the local indistinguishability. Then, in $$2 \otimes 2 \otimes 2$$ quantum system, we show that a set of orthogonal entangled states is locally reducible but locally indistinguishable in all bipartitions, which means the states have strong nonlocality. Furthermore, we generalize the result in N-qubit quantum system with $$N\geqslant 3$$ . Finally, we also construct a class of strong nonlocality of orthogonal entangled states in $$d\otimes d\otimes \cdots \otimes d, d\geqslant 3$$ . Our results extend the concept of strong nonlocality for entangled states.
A Log Analysis Technology Based on FP-growth Improved Algorithm

With the continuous popularity of the Internet, network security has received more and more attention. Compared with intrusions from outside the network, abnormal operations of internal users often pose a greater threat to system security. Audit log analysis can discover abnormal behaviors or illegal operations of internal users through technologies such as data mining and pattern comparison, thereby adjusting security policies to ensure system security. Based on the FP-growth algorithm, this paper proposes an improved algorithm NEFP (New Efficient FP-growth) that does not generate conditional frequent pattern tree, and proposes an implementation plan for audit log analysis based on NEFP algorithm. Experimental results show that NEFP algorithm can perform log audit analysis more efficiently.
A Study on the Mechanism of Blockchain Cryptocurrency Implementation

The education reform has been thirty years since 1990, and in recent years the learning enthusiasm and class participation of student was decrease by the internet popularity. With the borderless online communication becoming more widespread, there are many information and method to learn, however, sometimes this information is inaccurate or wrong. No one can deny that the world is different than it was when our current teaching method was changed. According to education scholar's experience, the main reason for a lack of learning motivation. Several educators have already indicated that innovative teaching and curriculum such as PaGamo and MPAS that was applied by National Taiwan University prof. Ping-Cheng Yeh and Taichung Municipal Shuang Wen Junior High School Teacher Alex Wang. The PaGamo and MPAS used for get back for learning enthusiasm and motivation for student.