Reviewerly/exHarmony huggingface dataset:
Distance based kernels for video tensors on product of Riemannian matrix manifolds

In this paper, we explore the inherent geometry of video tensors by modeling them as points in product of Riemannian matrix manifolds. A video tensor is decomposed into three modes (factors) using matrix unfolding operation and each mode is represented as a point in a product space of Grassmannian and symmetric positive definite (SPD) matrix manifold. Hence a video is represented as a point in the Cartesian product of three such product spaces. Being a manifold valued (non-Euclidean) representation, application of several state-of-the-art Euclidean machine learning algorithms lead to inferior results. To overcome this, we propose positive definite kernels which map the points from product manifold space to Hilbert space. The proposed kernel functions implicitly make use of geodesic distance on product manifold to obtain a similarity measure and generate a kernel-gram matrix. In addition, we generate a discriminative feature representation for each manifold valued point using kernel-gram matrix diagonalization. Classification is performed in a sparse framework. The proposed methodology is tested over three publicly available datasets for hand gesture, traffic signal and sign language recognition. Experimentation performed over these datasets show that the proposed methodology is powerful in terms of classification accuracy in comparison with the state-of-the-art methods.
Image Forgery Detection &amp; Localization Using Regularized U-Net

With the rise in digital media and popular image sharing platforms there has been an increase in the manipulation of images through image editing software. Image editing has never been easier because of the readily available and easy-to-use software. This has led to a wave of tampered images flooding the Internet. Traditionally, the human eye could distinguish between an original image and a tampered one, but with editing software developed recently, it has become significantly harder. Broadly Image Forgery can be either Copy-Move, where a region of an image is copied and pasted on another location in the same Image or Image Splicing, here, a section within a specific region of the image is copied and pasted on another region in a different Image. Most of the current methods and algorithms for Image Forgery detection use manually chosen features to identify and localize manipulated portions of the image with some moving towards Deep Learning models. We followed a deep learning approach of using a modified version of the Image Segmentation Model U-Net. The U-Net model was modified by adding regularization. The results were promising with an F1 score of 0.96 on the validation and test sets with the model able to detect and localize forged sections.
Research on Reliability Optimization Model Construction of Embedded Electronic Information System

Aiming at the problem of inaccurate optimization in traditional reliability optimization methods, this research proposes a new reliability optimization method for embedded electronic information systems. Through analyzing the working principle of embedded electronic information system, the fault density function, failure rate, average life and availability of embedded electronic information system are optimized. This study establishes a model that restricts the reliability of embedded electronic information systems, so that the failure rate of embedded electronic information systems is reduced, the average life span is extended, and the reliability is significantly improved.
Incremental Ensemble of One Class Classifier for Data Streams with Concept Drift Adaption

Due to the digital era, and recent development in software and hardware technology uses enormous applications like e-commerce, mailing system, social media, fraud detection, weather and network application. These applications generate a huge amount of continuous, sequenced, temporarily ordered and infinite data called as a data stream. There is a need to manage such data streams with real-time responses and sufficient memory requirements. Data streams lead to a problem of changing data distribution of the target variable is called as the concept drift. The Learning model performance degrades if the concept drift is not addressed, so there is a need for a learning model that adapts the concept drift by retaining the good performance of the model. One-class classification is a promising research area in the field of data streams classification. In the One-class classification, only the positive samples are considered to address the class imbalance and drift detection problem by not considering their counterparts. In this paper, an Incremental One-class Ensemble classifier is used to adapt the concept drift problem in streaming data. Model is evaluated with the Spam and Electricity real-world datasets and the model is used to address Gradual and sudden drift with 82.30% and 81.50% accuracy.
Learning More Expressive Joint Distributions in Multimodal Variational Methods

Data often are formed of multiple modalities, which jointly describe the observed phenomena. Modeling the joint distribution of multimodal data requires larger expressive power to capture high-level concepts and provide better data representations. However, multimodal generative models based on variational inference are limited due to the lack of flexibility of the approximate posterior, which is obtained by searching within a known parametric family of distributions. We introduce a method that improves the representational capacity of multimodal variational methods using normalizing flows. It approximates the joint posterior with a simple parametric distribution and subsequently transforms into a more complex one. Through several experiments, we demonstrate that the model improves on state-of-the-art multimodal methods based on variational inference on various computer vision tasks such as colorization, edge and mask detection, and weakly supervised learning. We also show that learning more powerful approximate joint distributions improves the quality of the generated samples. The code of our model is publicly available at https://github.com/SashoNedelkoski/BPFDMVM .
Data Mining of Weibo for Public Sentiment Evolution regarding COVID-19

This article proposes a Weibo public opinion evolution model based on data mining for COVID-19. Python crawlers are used to collect Weibo public content, and the evolution process is classified into four phases according to the popularity of public opinion. Naive Bayes classifier is employed for sentiment analysis, and data visualization method is adopted to explore the frequent word. Regional heat characteristics at each phase, the temporal and spatial laws of public opinion are discussed accordingly. The analysis results show that through the data mining of Weibo public opinion, the evolution pattern and hot content of each phase can be identified. This study suggests the government should focus on the evolution of public opinion and take effective guidance measures timely.
Influence of Boundary Extraction Operators on Boundary Shape Classification Algorithms Based on Complex Networks

As an important visual attribute for characterizing image objects, shape is always be investigated by researchers in pattern recognition and image analysis fields. It provides the most relevant information about objects for identification and classification tasks. By using the classical first-order and second-order operator algorithms, this brief paper extracts the shape boundaries of contour objects in several image sets. Then, generating explanatory variables based on the newly proposed complex network analysis method of the associated dynamic evolution boundaries network was carried out. Differences in the construction of high-dimensional feature interpretation vectors of boundary shapes based on network analysis are investigated. Our research shows that: the shape contour fingerprint vectors constructed by contour network construction algorithms based on different boundary extraction algorithms, can effectively represent the internal geometric figures of contour graphics features. The extracted digital feature vectors can be successfully used for graph classification tasks on the Etu10Silhouette database and Kimia216 database. There are some differences in CPU costs and classification accuracy. For the Etu10Silhouette database, it is recommended to use Sobel operator to extract the contours of the image shape, then use the algorithm mentioned in this paper to extract the fingerprint vector, combined with the KNN classification algorithm, one can get the best classification accuracy with almost the least time overhead. For the Kimia216 database, it is recommended to use the Laplacian operator to extract the contours of the image shape, with the boundary network fingerprint vector extraction algorithm, and combined with the LDA classification algorithm, one can obtain the best classification accuracy with almost the least time overhead. The comparative study on the classification algorithm based on deep learning method based on CNN also showed that, based on the graphic boundary shape analysis and classification method described in this paper, the classification method is simple and easy to be implemented. Our method has potential application prospects in industry.
MPCR: Multi-path Credit Reservation Protocol

In today's era of heavy traffic, traffic congestion control is becoming more and more important in data center networks. To achieve the great congestion control in data centers, countless researchers are struggling to purse. ExpressPass is a distributed forward-looking congestion control mechanism based on credit reservation, which can effectively avoid network congestion and achieve network losslessness. However, it can't make full use of network resources because of its limited path diversity. This paper presents an approach called multi-path Credits Reservation (MPCR). MPCR develops a newly designed multi-path credit reservation mechanism, making full use of network links. Besides, MPCR introduces path ID, which can be used to perform load balancing and consistency scheduling. Compared with ExpressPass, the results show that MPCR has higher convergence, shorter transmission latency and greater throughput.
The policy of proletarisation of education based on the example of educational institutions of Moscow department of professional education

The article considers the process of proletarisation of secondary and lower vocational schools and their results. Certain methods of forming the social composition of students are highlighted and described n the example of admission campaigns to educational institutions of Moscow department of professional education. Concrete examples show the adoption of the principle of class selection for admission to technical schools, factory schools, and professional schools. The representatives of working and peasant youth took advantage of enrolling them to educational institutions. The features of the principle of sending students to educational institutions as a way to improve the social composition of students are revealed. Travel orders were issued by regional and local Communist Party and professional organisations. The presence of travel order increased the opportunity of admission, although that did not guarantee it. The article also shows the role of ideological upbringing of students in the context of the policy of proletarisation. Political and educational activities were carried out within the framework of teaching a course in social studies, extracurricular class activities, as well as the efforts of representatives of the Russian Communist Youth Union. In the course of the study the materials of reports on admission campaigns to vocational and technical educational institutions in Moscow and Moscow Province were analysed. In conclusion we note the progress in formation of the composition of students from the position of class selection.
Medical MRI Image Enhancement Based on Curvelet Transform and Fuzzy Algorithm

This paper proposes a medical MRI image enhancement method based on curvelet transform and fuzzy algorithm. First, the MRI image is subjected to curvelet positive transform to obtain the curvelet coefficients at various scales and directions, and then the Monte-Carlo test method is used to estimate each scale noise variance, and then apply hard threshold shrinkage processing to the curvelet coefficients. Finally, the Pal-King algorithm with modified membership function is used to perform fuzzy enhancement on the image after inverse curvelet transformation to obtain the final result image. We selected a brain MRI image to test the algorithm, the experimental results show that compared with the other two enhancement algorithms, the algorithm in this paper has higher PSNR and CONTRAST, which can effectively suppress noise, enhance the edges and details of the image, and has better visual effects.
Exploratory Analysis of Geochemical Data and Inference of Soil Minerals at Sites Across Canada

Soil plays a crucial role in ecosystem functioning, e.g., soil minerals provide important provisioning and regulating ecosystem services. The determination of soil mineral composition can help to link geochemical processes to underlying bedrock and surficial geology, however analysing quantitative soil mineralogy by X-ray diffraction can be expensive. This study used data from the North American Soil Geochemical Landscapes Project sampled at sites (n = 560) across Canada; exploratory analysis of major elements from the C-horizon, < 2 mm size fraction, was carried out to determine whether geochemical data can infer site-specific qualitative soil minerals. Results for the raw geochemical data indicated relative variability of major elements across Canadian provinces with noticeable differences for silica and calcium. Geochemical data are compositional, and as such their statistical assessment is subject to the problem of closure. In the current study, all raw geochemical data were centred log-ratio-transformed prior to statistical analysis to overcome closure. Graphical measures indicated skewed element data prior to centred log-ratio transformation, which produced a more symmetric distribution. Correlations between elements suggested tentative soil mineral composition, such as silica and aluminum from aluminosilicates minerals. Principal component analysis of transformed geochemical data revealed three distinct groups of calcium, magnesium; iron, titanium, manganese; and aluminum, potassium, silica, sodium, while phosphorus had smaller relative variability independent of these groups. The interpretation of these groups was based on soil minerals and identified as carbonates, silicates, and weathered secondary oxides. These minerals corresponded geospatially to the regional bedrock geology of the sites across Canada, such as the sedimentary rock types from Western Canada to more variable minerals from igneous, metamorphic, or sedimentary rocks in Eastern Canada.
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Let F2m be the finite field of 2m elements and s be any positive integer. The existing literature only gives an effective calculation method to represent all distinct Euclidean self-dual cyclic codes of length 2s over the finite chain ring F2m+uF2m (u2=0), such as in Cao et al., (2019). As a development of this topic, we provide an explicit expression for each of these self-dual cyclic codes, using binomial coefficients. The Gray image of any self-dual cyclic code over F2m+uF2m of length 2s is a self-dual 2-quasi-cyclic code over F2m of length 2s+1. In particular, we give a generator matrix for each of these self-dual 2-quasi-cyclic codes over F2m.
Practical Analysis of Discrete Variable Quantum Key Distribution

The quantum key distribution is a promising application of quantum information that provides quantum information security for daily communication. This article studies the discrete variable quantum key distribution (DVQKD), including the BB84 protocol work and the performance comparison for both ideal and practical Poisson source. The formulas used to analysis process are derived in detail. In addition, quantum information is also introduced with the principle of quantum superposition and entanglement explained in detail. This is followed by a short introduction of the quantum information and its application in real world. We also explained the principles of quantum cryptography and also the practical implementation of quantum key distribution. The detailed procedures of the famous BB84 protocol and its set-up are also explored. In the project, I used the Matlab to calculate the BB84 key rate and evaluate the performance.
An Adaptive System of Yogic Gesture Recognition for Human Computer Interaction

The purpose of the research is to validate the potential of Yogic Hand Gestures in a well-formed human-computer interface with a real-time image sequence taken on a video recording device to trace the potential subject region(PSR) spontaneously, essentially, the hand region with the help of skin detection algorithm, and detect, and perceive hand gestures for human-computer interaction. To detect skin, we use skin colour detection and softening to remove extra background information from the image, and then use background subtraction to detect the PSR. Moreover, to avoid the background information, we use the kernelised correlation filters (KCF) algorithm to track the detected PSR. The image size of the PSR is then resized to 50px * 50px and then fed into the deep convolutional neural network (CNN) to identify eight yogic hand gestures. The deep CNN architecture developed in this study that is a modified VGGNet. The above process of tracking and recognition is repeated with a ranking algorithm to produce a real-time impression, and the system's execution continues until the hand leaves the camera range. While recognising the gesture primarily, it adds the top-ranked image captures to add into the sample pool for future training, the training data set reaches a recognition rate of 99.00%, and the test data set has a recognition rate of 95.89%, which represents the feasibility of the practical application. The implemented proof of concept and the custom yogic gesture dataset, namely the YoGiR-1 dataset, are availed on request.
Various density light field image coding based on distortion minimization interpolation

In recent years, the light field (LF) as a new imaging modality has attracted wide interest. The large data volume of LF images poses great challenge to LF image coding, and the LF images captured by different devices show significant differences in angular domain. In this paper we propose a view prediction framework to handle LF image coding with various sampling density. All LF images are represented as view arrays. We first partition the views into reference view (RV) set and intermediate view (IV) set. The RVs are rearranged into a pseudo sequence and directly compressed by a video encoder. Other views are then predicted by the RVs. To exploit the four dimensional signal structure, we propose the linear approximation prior (LAP) to reveal the correlation among LF views and efficiently remove the LF data redundancy. Based on the LAP, a distortion minimization interpolation (DMI) method is used to predict IVs. To robustly handle the LF images with different sampling density, we propose an Iteratively Updating depth image based rendering (IU-DIBR) method to extend our DMI. Some auxiliary views are generated to cover the target region and then the DMI calculates reconstruction coefficients for the IVs. Different view partition patterns are also explored. Extensive experiments on different types LF images also valid the efficiency of the proposed method.
On an improvement of Graph Convolutional Network in semi-supervised learning

Nowadays, we can easily face with many real-world datasets having the form of graphs, such as social networks, knowledge-based graphs, protein-interaction networks, the World Wide Web, etc., while there was still lack of in-depth research on the generalization of neural network models to such structured datasets. Hopefully, in the last few years, a plenty of encouraging results devoted to generate neural networks to work on arbitrarily structured graphs. Among them, it can be seen an important scalable approach of Kipf and Welling in 2016, which employs an efficient variant of convolutional neural networks for semi-supervised learning on graph-structured data. The experimental results applied for citation networks and on a knowledge graph dataset show that their proposed approach can outperform all related methods by a significant margin. In this paper, we present an adaptive approach for semi-supervised learning on graph-structured data that is also based on an efficient variant of convolutional neural networks which improves the pilot work of Kipf and Welling. In a number of separated experiments on citation networks datasets (Citeseer, Cora, Pubmed), we demonstrate that our approach is able to outperform the baseline graph convolutional network (GCN).
Burrows Wheeler Transform and Wavelet Tree Based Retrieval of Genome Sequence in an Indexed Genome Database

Background: New generation sequencing machinery such as Illumina and Solexa can generate millions of reads from given genome sequence on a single run. There is a need for suitable data structure, efficient with respect to memory as well as time to align these enormous reads into reference genome. There are a number of existing techniques of indexing and reads alignment, such as MAQ, Bowtie, BWA, BWBBLE and Kart. Memory efficient versions of these techniques are 10- 20% slower than their respective normal versions. Objective: A new approach for efficient indexing and retrieval of large genomic data. Methods: In this paper, we propose an efficient method based on Burrows Wheeler Transform and Wavelet Tree (BWIT) for genome sequence indexing and reads alignment. Both types of alignments (exact and approximate) are possible by the proposed approach (BWIT). Results: The performance of BWIT is experimentally found to be better than existing ones with respect to both memory and speed. Experimental work shows that proposed approach performs best in case of protein sequence indexing. All the existing read alignment approaches depend upon the size of index used. In general, time required increases with reduction in index size used. Experiments have been done with Bowtie, BWA &amp; Kart by taking index size as 1.25N, 1.05N, .98N, where N is the size of the text (reference genome). In our approach BWIT index size is .6N which is lesser than index size used in all other approaches. It is observed that even using smallest index size alignment time in our approach is least. Conclusion: An innovative indexing technique is presented to address the problem of storage, transmission and retrieval of large DNA/Protein Sequence data.
Weight Distribution of a Class of Linear Codes

Linear codes with a few weights have many applications in secret sharing schemes, authentication codes, association schemes and strongly regular graphs, and they are also of importance in consumer electronics, communications and data storage systems. In this paper, based on the theory of defining sets, we present a class of five-weight linear codes over $\mathbb{F}_p$(p is an odd prime), which include an almost optimal code with respect to the Griesmer bound. Then, we use exponential sums to determine the weight distribution.