Reviewerly/exHarmony huggingface dataset:
A System to Privacy Preserving and Guarantee Worker Rewards in Blockchain-Based Crowdsourcing

The crowdsourcing platform is a very popular platform where individuals collaborate to solve complex problems. Requesters share tasks that they are unable to solve, and workers perform these tasks based on their expertise. In today's digital world, privacy is a major concern for users of this platform. Most workers prefer to keep their personal information, such as age, location, and interests, private. Additionally, ensuring that workers receive rewards after completing their tasks is another reason for participating in crowdsourcing. However, traditional crowdsourcing requires a third party to send the rewards to workers, which can be unreliable and destructive. Therefore, most workers prefer platforms that guarantee protection of their privacy and full payment of their rewards. In this article, we use blockchain technology and smart contracts based on Ethereum to eliminate the need for an unreliable third party and make workers fully assured that they will receive their full rewards. In addition, we improve worker privacy protection using encryption algorithms and show that worker privacy can be largely protected by a feature-based encryption algorithm with a ciphertext policy.
Energy Efficiency and Establishing Service Level Agreement using Fuzzification of Virtual Machine Selection Policies for Migrating in Cloud Computing

Cloud computing has grown exponentially as an essential technology for the development of Internet services these days. The spread of this technology has led to the creation of large-scale data centers and complex practical applications with a wide range of communication needs. There has been an increased demand for cloud services in recent decades which led to higher energy consumption and Service Level Agreement Violation (SLAV). Through utilizing the Service Level Agreement Violation (SLAV) parameter, it is possible to calculate the SLA of a data center. Via reducing the number of migrations of Virtual Machine (VM) aggregation, we can establish the SLA and reduce the SLAV accordingly. In this study, we have proposed a suitable method per consolidation for choosing an appropriate VM to migrate from an overload host to a destination host using fuzzy logic. For doing so, a variety of VM selection policies for migration, such as Minimum Migration Time (MMT), Maximum Correlation (MC), migration control, and Maximum Power Reduction (MPR) have been fuzzified; hence different parameters in selecting a VM for migration have been considered. Using CloudSim simulator was indicated that the proposed approach had made improvements in the case of the number of migrations, total SLAV, SLA time per active host, and energy consumption. Using the migration policies fuzzification, we reduced SLAV and energy consumption.
Re-rank Search Engine Results via Machine Learning

Research on search engines has demonstrated their significant impact on information search and user behavior and highlighted the need for continued investigation of their effectiveness, efficiency, and ethical implications. Moreover, search engine optimization (SEO) has emerged as a crucial aspect of online content creation and marketing, as it involves optimizing websites and online materials to improve their visibility and ranking on search engine results pages, thereby increasing their reach and impact. SEO metrics provide valuable insight into a website’s search engine performance. However, the relative importance of these metrics remains unclear as search engine algorithms and keyword competitiveness can vary widely. Therefore, ongoing research is needed to better understand the relationship between SEO metrics and search engine rankings. Given the challenge of determining the relative importance of SEO metrics using traditional statistical methods, interested in applying machine learning techniques. By training models with custom datasets of three search engine results (Google, Bing, and DuckDuckGo) and their associated SEO metrics, we hope to gain a better understanding of the complex relationships between these variables. The first two pages of each search engine return the most relevant results. In this article, we propose two types of methods to do this. First, classify the page index of web search results. Second, learning ranking methods to rank them. LambdaRank has the best NDCG compared to other methods for Google and DuckDuckGo which are 0.86 and 0.93 respectively. RankNet is the best learning-to-rank method for Bing with 0.85 as the NDCG value. Logistic regression has the highest accuracy compared to other methods in page index classification for Bing and Google, which are 61.29% and 64.71%, respectively. K-nearest neighbor performs best for DuckDuckGo with an accuracy of 66.67%.
Interpreting the definition of time series anomalies using fuzzy ontologies

The paper describes an approach to detecting time series anomalies, taking into account the specifics of the subject area, represented as a fuzzy ontology. The approach involves the use of LSTM (long short-term memory) networks for the mathematical search for anomalies, fuzzy ontology allows you to filter the detection results and draw an inference for decision making.
Method for automatic cartoon colorization

Colorization task consists of acquiring a full-color RGB image from grayscale image or a sketch. Article is concerned with the task of colorizing grayscale cartoon images and image sequences using neural networks. Efficiency of an existing prototype algorithm is reviewed with different modifications, as well as different combinations of loss functions. A new neural network loss function is proposed. It is based on a hypothesis that specifics of cartoons, such as clear object boundaries and color consistency within those boundaries can be used to improve colorization quality. Proposed loss function uses segmentation of cartoon images in the bilateral space, and minimizes difference between closest found segments and inside each segment, thus bringing closer predicted colors within the segment and between neighboring segments. Quantitative and qualitative experiments are conducted on efficiency as well as generalization ability of modified prototype algorithm with proposed loss function. Quantitative experiments consisted of measuring PSNR, LPIPS, MSE in Lab color space and CC, while qualitative focused on comparing temporal consistency, quality of colorization and quality of generalization.
Entropy Modeling in Video Compression Based on Machine Learning

The paper investigates an entropy model of spatio-temporal characteristics in video compression using machine learning algorithms. A significant drawback of the vast majority of existing works on neural video codecs is the emphasis on the process of optimizing the creation of a latent representation through the study of various network structures, using well-studied models for image compression as an entropy model The model under study makes it possible to effectively evaluate both the spatial and temporal characteristics of compressed video data, which makes it possible to achieve a greater reduction in video redundancy. In addition, the universality of the entropy model also allows you to set the quantization step over the spatial channel. This content-adapted quantization mechanism, similar to rate control in standard codecs, not only helps achieve smooth compression ratio adjustment, but also improves final performance by dynamically distributing quantization intervals. The results of computational experiments on real video sequences confirm the efficiency of the studied video compression method.
Deep Contextual Video Compression Based on Machine Learning

The structure of existing neural network video compression methods in most cases includes predictive encoding, which uses a subtraction operation between the predicted and current frames to remove redundancy. To increase efficiency, an approach based on deep contextual video compression is used. In addition to the difference frame, this approach relies heavily on specialized algorithms for extracting additional information characterizing the difference of closely spaced frames. The use of context in this case makes it possible to achieve a better quality of reconstruction of video sequences, in particular for complex textures with a large number of high frequencies. This implies that the proposed method can potentially lead to significant savings in storage and transmission costs while maintaining high-quality video output. This article presents the results of computational experiments to evaluate the effectiveness of the investigated method of deep contextual video compression on real video sequences. Experimental findings demonstrate the advantages of the considered technique in PSNRR/bpp coordinates when compared to the performance of three common video codecs: H.264, H.265, and VP9.
Style transfer effectiveness for forensic sketch and photo matching

In this paper, the problem of comparing portrait photographic images and forensic sketches is considered. The paper analyzes the feasibility and potential advantage of applying style transfer methods to solve this problem. A method for comparing photographic and synthetic images is proposed. It consists of the feature extraction from a pair of images, the subsequent element-by-element difference between feature vectors, and further classification. We also consider a modification of the proposed method, which uses the style transfer from a sketch to a photographic image. Experimental research of the two mentioned methods is carried out on a test set of image and sketch pairs. Its results show the advantage of the modified method over the initial one.
Study of astigmatic transformation of Legendre polynomial beams

In this paper, the properties of the astigmatic transformation of two polynomial beams are studied on the basis of numerical modeling using the Fresnel transformation.
Orthogonalization and Parameterization of Convolutional Kernels in Machine Learning for Image and Video Compression

We study orthogonalization and parametrization of convolutional filters within the framework of the image and video compression method based on machine learning. We use the convolutional filters to interpolate less sparse video frame meshes based on sparser video frame meshes. We consider superresolution neural networks and decision trees as machine learning algorithms at the interpolation stage. Decision trees adaptively select an interpolating function from a predefined set of convolutional filters with parameterized orthogonal weights. The use of adaptive functions can significantly improve the accuracy of interpolation. Optimization of machine learning algorithms makes it possible to use the adaptability of interpolators in the most efficient way. We use orthogonalization and parametrization of convolution filter weights to increase the efficiency of the machine learning interpolation algorithm, which in turn leads to an increase in the efficiency of the image and video compression method in general. Computational experiments demonstrate the advantage of the proposed algorithm in real videos.
Method for detection of adversarial attacks on face detection networks

The paper is concerned with the problem of detecting adversarial attacks against face detection networks. The paper reviews existing adversarial attacks, as well as defense techniques. A novel defense method is proposed, which provides improved detection quality compared to the base technique. Comparative experiments are conducted on a dataset composed of original and fake images obtained using FGSM and MI_FGSM techniques with various parameters.
Automatic analysis of face images for college degree verification

The article discusses application of machine learning to physiognomy. Two different neural-network models are examined as feature extractors from face images. In total three classifiers are trained and compared with each other, pursuing the goal of answering a question if it is possible to automatically verify a college degree based only on a human face. Our findings show that to a certain extent it is possible.
Fast SVM-based One-Class Classification in Large Training Sets

SVM is one of the popular methods to solve One-Class classification problem. However, it is time and space-consuming. This fact makes it hard or even impossible to apply SVM for large training sets. In this paper we propose fast method (One-Class Kernel-based Mean Decision Rule method, OC-KMDR) to find an approximate decision of One-Class SVM problem. The main advantages of the proposed approach are: 1) the obtained decision is near exact (and in a number of cases the method can outperform the original SVM in quality); 2) the obtained decision has absolutely the same structure as the original SVM; 3) the absence of theoretical restriction for the training set size; 4) it can be realized in an iterative manner but without inter-iteration data dependencies, and, as a result, provides the possibility for effective parallel computing. Experimental study of the proposed OC-KMDR-method in series of simulated large data sets shows that it outperforms existing methods for solving One-Class SVM problem in a computing time or(and) in a decision quality.
Sudden death of entanglement in a thermal three-qubit Tavis-Cummings model

In this article, we consider the entanglement between three identical qubits resonantly interacting with a thermal field of an Q-infinite resonator. We derived the exact solution of the evolution equation for density matrix of the considered system. On it basis we calculate the negativity as a criteria of entanglement between pairs of qubits for biseparable and entangled W-type states of qubits. The results showed that for considered initial qubits states the effect of the entanglement sudden death occurs for all values of the average number of thermal photons.
Improving the Efficiency of Noninvasive Electrocardiography Screening System

The paper considers the issues of improving the efficiency of noninvasive electrocardiodiagnostic screening system (NESS) based on the registration of multiple leads of the electrocardiographic signal (ECS). The proposed methods and means of ECS processing refer to the 4th generation electrocardiography, which allows obtaining new information about cardiac processes more reliably and revealing clinically significant cardiac pathologies under screening conditions.An approach to building NESS has been proposed that consists in "convergence" of solutions of direct and inverse electrocardiology problems with subsequent visualization of the results of the solutions for a visual presentation of the heart electrical activity (HEA) to the physician. Increased efficiency of NESS is due to the original "technological pipeline" of ECS processing: registration of multiple ECS leads, pre-processing of ECS, multivariate analysis of ECS, modeling and visualization of ECS.Physicians are provided with a tool for screening and diagnostics of HEA, providing increase of efficiency of HEA assessment on the basis of collection, processing and transfer of cardiographic information; modeling of HEA; visualization of HEA analysis results.
Source Camera Identification Using Neural Networks

Source camera identification is a forensic problem used for image authentication. The identification goal is to determine the camera model by digital image. At present, the most prosperous approach to source camera identification applies neural networks to classify camera models. In my research, I provide verification and modification of the source camera identification method based on the EfficientNetB5 neural network proposed by Hadwiger and Riess. The original method is very simple in implementation and it is reported to be very efficient in camera model classification. However, I demonstrate that the original method’s performance was overestimated. Therefore, I proposed a modification of the original method using the BagNet9 network. The experimental results with Forcheim Image Dataset show that modified method gives significantly better camera identification accuracy than the original method. Thus, BagNet9 is more effective in terms of camera identification than EfficientNetB5.
The use of hyperspectral images as cover data in information hiding

The paper tells the features of using hyperspectral images (HSI) as a container for embedding hidden information. It is proposed to use the accuracy of the classification of HSI (as the most common problem solved with the help of HSI) to control the permissible level of introduced distortions instead of the indicator of visual imperceptibility. We need to increase the volume or robustness of embedded information while maintaining acceptable classification quality. For this it is proposed to change the embedding parameters for different HSI channels in proportion to their significance in classification. As an example, three fundamentally different methods of embedding information are considered: the method based on quantization index modulation(QIM), the method based on spread spectrum and the method based on interpolation. Experiments have shown the absolute advantage of the proposed approach over the standard one (parameter values constant for each channel) for all three methods. Experiments have confirmed that the proposed approach makes it possible to fill a significant part of the volume of the HSI (about 3/4 for the method based on QIM) almost without deterioration in the quality of classification. And for the interpolation-based method, it is possible not only not to worsen, but to improve the quality of classification along with the embedding of hidden information.
Designing an algorithm for annotating Russian-language text data of social media using transfer learning

This article considers ways to build an algorithm for annotating Russian-language texts from social media. Annotation will be defined as the estimation of the emotional coloring of the text. The article addresses both classical basic methods of statistical learning and modern methods of deep learning based on transfer learning and transformers. The main problem in solving the problem of determining the sentiment of Russian-language texts is the lack of a large corpus of labeled data, which severely limits the training of the model. In conclusion, a model that combines the transformer model and gradient boosting will be developed. The relevance of this work is to create a model with low memory consumption and thematic independence of posts, trained on a small amount of data, which can be used to analyze the textual content of posts in social media.
Optimization of Multi-microgrid Operation Based on Blockchain Technology and Multi-agent Reinforcement Learning

Blockchain technology has attracted attention in the field of electricity market due to its decentralization and fair transaction characteristics. However, the existing research lacks the quantitative analysis of the benefits brought by blockchain, and the solution efficiency is not high. In view of the above problems, the paper first proposes a local energy market (LEM) architecture based on blockchain technology, which guarantees the openness and transparency of transactions, and guarantees the privacy of participants. On this basis, a distribution market operation optimization model with multi-microgrid based on blockchain technology is proposed. Using the multi-agent reinforcement learning algorithm to solve the proposed model can effectively improve the solution efficiency. By transforming the objective function from the maximization of the overall benefit to favor of the interests of one party, it is proved that blockchain can effectively improve the overall income by guaranteeing the transaction fairness.
A Data Storage Method of Intelligent Substation Based on Physical Model

In view of the sharp increase in the amount of data in intelligent substations at present, the gap between the data transmission mode and the data storage mode is large, resulting in the problem of low data storage efficiency. This paper presents a data storage method of intelligent substation based on physical model. Firstly, this paper analyzes the data transmission mode of IEC 61850, introduces the object-oriented modeling technology of IEC 61850, the relationship between various configuration files, and the tree structure of the transmitted data. Then, this paper introduces the concept, structure and characteristics of the physical model. Build a physical model of the device based on the CID (Configured IED Description) file of the device, and form a device self-describing JSON file. After then, according to the characteristics of each service in the physical model, this paper uses different types of databases to store the data under this service. Finally, this paper develops and applies the method, and completes the back-to-back data storage model of a substation equipment. The experimental results show that the functional requirements and performance indicators of the developed substation data storage method can achieve the desired goals and effectively improve the efficiency of data storage.
PODA: Positive Active Power Outlier Detection Based on Auto Machine Learning

As China continues to strengthen the implementation of the "double carbon" development strategy, the power industry is in urgent need of upgrading its industrial structure. The identification and processing of inevitable outliers in power data is a prerequisite for all optimization and adjustment work. However, traditional identification methods are often ineffective, and machine learning methods are often associated with high learning and labor costs. To address this problem, this paper proposes a positive active power outlier detection method based on automated machine learning techniques, which can realize automated feature extraction and outlier identification of power data. Firstly, the power outlier detection and automated machine learning are introduced. Then the related working process is designed and experiments and analysis are conducted. The experimental results show that the method can significantly improve the efficiency of power data analysis and ultimately improve the quality of power business services.
Photovoltaic Power Forecasting Model Based on the Fusion of Bagging Algorithm and Generalized Regression Neural Networks

Power prediction of photovoltaic (PV) power generation is very important for photovoltaic power generation to be connected to power grid. Based on this, this paper proposes a prediction model based on Bagging algorithm and GRNN to improve the prediction power of PV power generation. Generalized Regression Neural Network (GRNN) is a variant of Radial Basis Function Neural Network (RBFNN). Compared with RBFNN, GRNN has better nonlinear function approximation ability, and the network has higher robustness and fault tolerance. Bagging algorithm is a typical integrated learning algorithm, which can optimize the performance of a single prediction model. Through the simulation of an example, the simulation data show that the prediction model based on Bagging algorithm and GRNNs combination can obtain higher prediction accuracy.