Reviewerly/exHarmony huggingface dataset:
A Covid 19: QR Code based Health Monitoring System

The epic Covid 2019 is likewise known as Coronavirus is a continuous infection that was spread rapidly around the world. It's the outbreak affects hundreds of thousands of people and it has a growing impact on daily lives. It affected the global economy and environment too. The main problem is that in the densely populated country it's very difficult to detect the positive cases earlier as much as possible and to prevent the rapid spread of this and to treat the patients quickly. This project is aimed at supplying healthcare providers with ways to use the “Health Monitoring System” to reduce such viral flues. This project is mainly focused on android application development. It is mainly directed to the people in quarantine. Their information will be collected from these people themselves. By this application collecting the details of people in a questionnaire mode by using a QR code who has gone for the test of covid‘19. This application provides physically contactless and wireless technology to help prevent the further spreading of this disease at the time of collecting the details of the people. Here the details and results of people are transferred to the applications are done by scanning the QR codes. The main advantages of this project are that there is no need of enquiring about health for health workers by contacting the patient's details directly. The significance and purpose of this project are to providing user-friendly support to reduce the number of infected people and helping to maintain social distance and also reduce physical contact and find the exact location of the patient.
Fish-lateral-inspired pressure sensing neural networks for underwater object identification

In the current project, we numerically study underwater object identification using a hydrofoil with pressure sensors. Using LilyPad, a boundary data immersion method (BDIM) viscous 2D solver, we simulate and generate a large dataset that describes pressure evolution over time around a constant speed moving foil passing by various objects at different distances (ellipses and rectangles of different geometries and orientations). A multivariate convolutional neural network is then constructed and trained on the dataset after the application of the POD dimensionality reduction, mapping the distinctive pressure information of the near body flow feature’s distinctive pressure information to the objects close to the passing foil.
Mars entry navigation under biases based on adaptive Huber divided difference filter

Summary To address the problems of unknown disturbances and measurement outliers at Mars entry phase, three integrated navigation schemes are proposed in this study by complying with divided difference filters, that is, integrated navigation schemes based on the Huber first‐order divided difference filter (HDDF1), adaptive Huber first‐order divided difference filter (AHDDF1), and adaptive Huber second‐order divided difference filter (AHDDF2). The novel versions of predictive state estimation error covariance (PSEEC) and measurement noise covariance (MNC) are deduced by adopting the function of the Huber case to modify the cost function of the standard Kalman filter. To be specific, the HDDF1 is derived by embedding the novel PSEEC and MNC in the general first‐order divided difference filter. To more specifically enhance the performance of the filter under significant biases, an adaptive forgetting factor is introduced in the HDDF1. On that basis, the AHDDF1 is derived. Likewise, the AHDDF2 is yielded. The numerical simulations are presented to verify the effectiveness of the proposed navigation schemes.
Quality Assessment of CFD Software Using Workflows and Decision Trees

The present research focuses on the new efforts for leveraging so-called workflows for the management of dozens of CFD validation cases. Proposed workflows are meta-algorithms built on top of the free open-source Snakemake library. They allow manipulation of a growing database that currently contains 66 OpenFOAM cases representing various pipe and bubble column multiphase flows with supplied experimental data. The curated case set also provides documentation files. The proposed approach relies on the utilization of human-provided &#x201C;keywords&#x201D; for designing a featured dataset. We also use the method developed earlier for quantification of the fitness of simulated to experimental results. It leverages Fuzzy Logic for combining fitness metrics across various physical fields and produces single performance metric called &#x201C;Goodness&#x201D;. By transforming metrics into a single crisp output value, the algorithm can assign then and additional categorized property to the simulation result of each individual case such that &#x201C;degraded&#x201D;, &#x201C;neutral&#x201D; or &#x201C;improved&#x201D; with regard to available experimental data. At the top level, we demonstrate how successes and failures of case sets may be highlighted and analyzed with Decision Trees. The resulting tree is useful in the quality assessment of the CFD software in the final stages of solver development. Decision Trees provide a transparent way for analyzing diverse CFD validation case sets when investigating top-level CFD model changes (for example, applying a new turbulence model).
Optimization for Logic Operation Problem in Stochastic Simulation of Dynamic Causality Uncertain Graph

Dynamic Uncertain Causality Graph (DUCG) is an efficient model for fault diagnoses of complex systems, it is an innovation of the Probability Graph model (PGM). The algorithms based on DUCG have been applied to many industrial systems such as nuclear power plants, chemical systems, and spacecraft. However, DUCG is still faced with the problem of combination explosion like Bayesian Network (BN). Therefore, a new algorithm based on conditional stochastic simulation is proposed to overcome the problem and boot the inference of DUCG. The combination problem in DUCG is caused by two processes. One process is the traversal of the graph structure, it can be directly solved by the sampling scheme. The other process is the logical operation of the evidence expression during inference. To solve the problem of logic operation, an intuitive algorithm has been proposed. This estimation algorithm uses two parameters based on the experience in previous experiments as the threshold of cutoff for logic expression. In this paper, an optimization for this estimation algorithm is discussed for better performance and accuracy. The basic idea is to separate the graph into subgraphs with the new parameter IGlayer, then calculate and apply the cut-off estimation on the independent par and multiply them together after their numerical value in the sampling cycles. This new method can break the long logic expression into several small ones, so it can mitigate composite explosion problems significantly. An example is presented to show the detail of this new algorithm and shows a simple comparison to the older algorithm.
Norway, Sweden, and Finland as forerunners in open ecosystems and openEHR

Norway, Sweden, and Finland may be considered as forerunners in adopting openEHR and moving towards open ecosystems. Cross-organisational data sharing, or data exchange, has historically been the objective in all Nordic countries. The needs to share data finally resulted in national document-based data sharing infrastructures in all Nordic countries. Document-based sharing is, however, somewhat limited, and the need for complementary structured sharing has started to emerge. openEHR projects typically start as data exchange projects and gradually develop into a modular EHR. In the Nordics, however, the dominant EHR market leaders chose openEHR as their own data model and started to modernise their solutions as their own internal decision. This is not necessarily the approach taken in other countries. The Nordic experience shows that in order to build a true ecosystem, it is important to increase awareness amongst all stakeholders. A monolithic solution may seem lucrative unless there is sufficient understanding of the benefits of modularity and openEHR.
A Dynamic nDES Model for Hiding Datasets for Machine Learning

The Data Encryption Standard (DES) is a block cipher for converting plain text into ciphertext. It is a symmetric key algorithm grounded on the Feistel network. The DES uses 56-bit keys to complete encryption in 16 rounds. However, on its own, the DES alone maybe brute force attacked. Using the DES model multiple times (number of times) may strengthen the key size. Improvement of the DES model for hiding the datasets used for machine learning purposes is the theme of this paper. A depth parameter is introduced which triggers the generation of hard to predict sequencing, giving rise to the nDES model. We investigate successful application of the depth parameter, and the sequencing approach in an effort to improve the DES model for hiding datasets. Results indicate that depth parameters between 1 and 8 are commonly influenced by the DES’s key size of 8 characters. In addition, the selected depth determines the complexity of the sequencing thereto. However, the depth does not influence the CPU time required to execute the proposed nDES model. Hence, these are inexpensive improvements to the traditional DES model.
Animation Character Action Feature Extraction Based on Pyramid LK Optical Flow Algorithm

Driven by network communication technology and mobile software and hardware technology, China's animation industry has been developing rapidly in recent years and is full of promising prospects for development. As a good animated work generates great economic value, there are many cases of animation infringement. In recent years, China has attached great importance to the protection of the intellectual property economy, of which animation is an important part. In order to apply this in infringement situations where the animated characters are inconsistent but the actions of the characters are consistent. In this paper, the Pyramid LK optical flow method is used to extract the motion characteristics of animated characters in simple scenes. Combined with the image pyramid method can be a good solution to the aperture problem caused by the expression exaggeration effect.
A Video Scene Segmentation Optimization Algorithm Based on Convolutional Neural Network

Currently, video scene segmentation is an important part of realizing content-based video retrieval (CBVR). Aiming at the problem that low efficiency of video scene segmentation in CBVR, this paper proposed a multi-modal video scene segmentation optimization algorithm based on feature extraction of convolutional neural network (CNN). According to the large amount of information contained in the multi-modal data of video, the VGG19 network has been improved in a targeted manner and the underlying features and semantic features of various modes are extracted from each video shots. By forming these features into vectors and using the method such as triplet loss learning and shot similarity calculation, scene segmentation task is converted to a binary classification problem for shot boundary. Then the scoring mechanism is established to optimize the results, finally the scene segmentation task is completed. Experimental results show that the algorithm can be effective in video scene segmentation, and the overall recall and precision can reach 85.77% and 87.01%, respectively. Compared with the shot similarity graph method, two indicators have increased by 10% and 9% respectively. Compared with the DeepSSS method that also uses the deep learning network model, the comprehensive metric F-messure has increased by 8%.
Image Classification and Semantic Segmentation with Deep Learning

Deep Learning (DL) in convolutional networking systems (CNNs) is resourceful and revolutionizing in artificial intelligence (AI) and it will gradually change the world industrially and globally based on computer vision that will allow people to input Machine Learning (ML) models and output their given images and classify them based on different datasets and results since DL is a type of AI that imitates the way humans gain certain types of knowledge much easier and faster with neural networks having representation learning. However, there will be many challenging tasks such as image detection, recognition, and segmentation of objects in an unbounded environment that will be efficiently addressed by many types and methods of deep neural networks. In this paper, image classification is used to classify different images while segmentation is used to cluster (or bind) together parts of an image from the same object class to distinguish common patterns or results. Therefore, the aim is to train and test different DL models that will be able to produce images given from a modeled dataset, classify each part (whether it is for training or testing), and segment each of them in real time and compare them to see if they can be safely used in a technological environment. These methods will divide a dataset into groups or clusters to see if any similarities are minimized or maximized results produced in the form of line graphs that will showcase the accuracy and the losses for each modeled dataset.
Recursive Bayesian inference and learning for target tracking with unknown maneuvers

Summary This paper addresses the problem of target tracking under completely unknown maneuvering behavior to cope with the complicated target maneuvers. The actual but unknown motion model is formulated as a linear stochastic model with unknown transition matrix and modeling error covariance. Considering the diversity and time‐varying of maneuvers, both the transition matrix and covariance are random. Accordingly, the tracker design amounts to recursively updating the joint posterior of target state and unknown parameters (i.e., the transition matrix and covariance). Since it is difficult to obtain the analytical joint posterior, a Bayesian inference and learning scheme is proposed in the sequential Monte Carlo (SMC) framework to alternatively update the state posterior and the unknown parameters posterior. Particularly, it is revealed that the state prediction density used in the SMC obeys a Student's t ‐distribution, thus ensuring the effective sampling of particle trajectories. Finally, a maneuvering benchmark shows that the proposed tracker can significantly reduce the peak tracking error, which is one of the most important performance indices for preventing tracking failure.
Analysis of Crossref Reports in Order to Improve the Quality of Metadata of Scientific Publications

Issues related to improving the quality of metadata of scientific publications placed in the Crossref bibliographic database are considered. All information contained in metadata obtained from publishers of scientific publications is analyzed by Crossref and displayed in various reports. Analysis of these reports gives publishers an idea of the completeness and correctness of the bibliographic data presented. The quality of metadata directly or indirectly affects the number of views and links to a publication, respectively, on the ratings of scientific publications, authors and organizations.
Complaint electricity customer clustering method based on electricity big data

Abstract In the fierce domestic market competition, the improvement of service quality is an important goal of electricity companies. This paper proposes the k-means algorithm based on the improved wolf pack algorithm, which is suitable for the power company complains, there are many factors that affect user complaints. Firstly, the PCA method is used to reduce the dimension of the complaint user’s behavior influencing factors, so as to improve the accuracy of clustering. Secondly, due to the shortcomings of falling into local optimal solution and low clustering accuracy, this paper proposes an improved wolf pack algorithm, which proposes the interactive strategy of walking behavior, calling behavior and an adaptive siege strategy for behavior, the clustering accuracy and convergence speed are improved.This algorithm overcomes the shortcomings of the original algorithm. Moreover, it improves the efficiency and clustering quality of the algorithm and it can realize the accurate classification of users.
Phase-based accelerated motion magnification using image pyramid

This paper proposes a method for motion magnification that accelerates phase extraction using a Complex Steerable Pyramid (CSP). Motion magnification is a task to visualize minute fluctuations in a video that are difficult for humans to perceive. The conventional method amplifies the phase change of the CSP between frames in a video to emphasize the small fluctuations. These responses are calculated by the product of the transfer function of the complex steerable filters and the frequency components of each frame. However, the fast Fourier transform and its inverse transform required for this computation occupies about 30 &#x0025; of the computation time, and is a computational bottleneck. We have accelerated the conventional method by generating the CSP by convolution in the spatial domain focusing on the hierarchical structure of image pyramids.
Learning Knowledge Graph Embeddings by Multi-Attention Mechanism for Link Prediction

Knowledge graphs (KGs) are being widely utilised in many fields of artificial intelligence. However, the lack of relations has become a huge obstacle to their real-life application. Therefore, recent researches in this field tend to focus on link/relation prediction techniques on KGs, but most of them merely learn KG embeddings from the central nodes’ neighbourhood in an absolute or unconditional way, fusing a great deal of weak or even useless information. To tackle this issue, we propose a novel end-to-end KG embedding model named Relational Gated Graph Attention neTwork (R-GGAT), which learns embeddings by supervising and controlling the node aggregation process by selecting and filtering the aggregated information through its multi-attention mechanism (MAM), in an attempt to fully and effectively mine the underlying semantic information of KGs. Evaluation experiments on several datasets have been performed, where our proposed R-GGAT achieved the elevated performance in link prediction tasks compared to several state-of-the-art baselines. We also carried out ablation study and analysed the changing of node embeddings filtered by the MAM to demonstrate the effectiveness of our model.
Intelligent Patrol Robot Based on Visual Machine Deep Learning

The purpose of this study was to investigate the ability to achieve self-navigation by robots in an unknown environment. An experiment was set up to collect the data. By installing a visual sensor on the robot, environment data and other crucial information were collected and analyzed efficiently. The results reveal that robots cannot recognize the environmental perception stage due to the limitation of existing computing and storage capabilities. The study findings may serve as a guide for further research on robotic intelligence.
Migration node selection strategy based on gray prediction algorithm in trusted environment

This paper studies the selection strategy of task migration nodes. Considering the security of nodes, this paper analyzes the trusted security model of each node of migration candidates. Security credibility includes three aspects: time credibility, behavior credibility and resource credibility. By calculating the trusted values of migration nodes in these three aspects, Three nodes with the lowest trust value are obtained to preliminarily screen the migration nodes, and the final nodes are determined in the remaining nodes. Because the gray prediction algorithm requires a small number of original data models and has a strong discrete type, it is more suitable for the model selected by the task migration nodes. This paper uses the gray prediction algorithm to finally determine the migration nodes, In the experiment, the cloud simulation platform is used to compare the algorithm and random algorithm, and the results show the effectiveness of the algorithm.
Demystifying Graph Neural Network Explanations

Graph neural networks (GNNs) are quickly becoming the standard approach for learning on graph structured data across several domains, but they lack transparency in their decision-making. Several perturbation-based approaches have been developed to provide insights into the decision making process of GNNs. As this is an early research area, the methods and data used to evaluate the generated explanations lack maturity. We explore these existing approaches and identify common pitfalls in three main areas: (1) synthetic data generation process, (2) evaluation metrics, and (3) the final presentation of the explanation. For this purpose, we perform an empirical study to explore these pitfalls along with their unintended consequences and propose remedies to mitigate their effects.
Improving document representation using KPCA and clustered word embeddings

Text mining approaches have shown to be an efficient way to examine publicly available online information on a variety of topics. However, because of the lack of context, online texts are difficult to represent. Word embedding techniques, such as word2vec, capture semantic relationships between words particularly well when trained on large text collections; but they fail when trained on small datasets. This paper proposes a document representation method based on word clusters. In this approach, Word embeddings created by word2vec are supplemented with morphological information collected from kernel principal component analysis (KPCA) computed on word similarity. After running KPCA, a spherical k-means algorithm is applied, with centroid representing the topic of that cluster. Next, the document is vectorized using the frequencies of these clusters. The proposed approach successfully includes the effects of semantically and morphologically related terms on document proximity through these data-driven notions. When combined with an appropriate weighting system, the proposed approach enables improved document representation and interpretability of the resulted document vectors.
Navigation in Hypertext

The freedom given by hypertext offers a wide spectrum of instructional articulations. Already typographies and for instance highlighting textual passages have an impact on how readers spread their attention and start jumping from one paragraph to another. Even more exciting is when due to readers’ interest a strictly individual reading sequence is allowed.
Pipeline Based Big Data Sketching

Big Data pipeline is an architecture for big data applications where all the sub-modules are composed in a sequential manner. In this architecture, output of one stage forwarded as an input to the next stage of the pipeline. Although, each stage of the pipeline has relevant information, but the data present at intermediate stages is not sufficient to predict inference due to uncertainty present at that stage. Therefore, a probabilistic inference based method is proposed for making strong local inference. For sketching probabilistic inference of Big Data pipeline, this work contributes probabilistic pipeline sketching of inference methods using variable elimination and bucket elimination inference.
Logic and The Lexicon

Abstract This chapter focuses on a special instance of logical vocabulary, namely modal words, like “might” or “must,” which express possibility and necessity. Modal statements involve a complex interplay of morphology, syntax, semantics, and pragmatics, which make it particularly challenging to identify what lexical meanings the modal words encode. This chapter surveys how possibilities and necessities are expressed in natural language, with an eye toward cross-linguistic similarity and variation, and introduces the framework that formal semantics inherits from modal logic to analyze modal statements. It then turns to the challenges—for both the semanticist and for the child learner—of figuring out the right division of labor between semantics and pragmatics for modal statements, and the exact lexical contributions of the modal words themselves.