Reviewerly/exHarmony huggingface dataset:
Determining the Best Strategy for the Network Administrator in Dynamic Environment Through Game Theory

Communication between individuals, organization, etc., takes place over a network through various protocols so it is necessary to protect the network from various kinds of security attacks from the attacker. Hence, risk assessment must be done by the security team of the organization to look for the security threats on the network and also protect it during the time of attack. Thus, during the attack, network administrator must apply the best strategies against the attacker. Hence, in this paper, a model is proposed wherein firstly, the risk assessment is done with risk rank algorithm and Monte Carlo simulation and based on this risk, a decision is taken in a view to mitigate this risk using conditional random fields (CRF). Further, during the attack, using game theoretic approaches, the network administrator determines the best strategy to protect the network. Then, to adapt to dynamically changing environment, advantage learning is used to learn the new risks, attacks and ways to tackle the same.
CCC: Weakly Supervised Semantic Segmentation using Class Contribution Classification

We propose a new semantic Segmentation technique that depends on the convolutional neural network of Xception using a modified categorical cross-entropy function to perform class contribution classification. We introduce a class contribution function for each class contribution in the image instead of categorical cross-entropy for maximum class classification. This approach is to be used for weakly supervised semantic segmentation (WSSS) using image-level annotation instead of the pixel-level annotations used in fully supervised semantic segmentation. We trained and tested our approach on both PASCAL VOC2007 and VOC2012 datasets. We show that our approach outperformed many other weakly supervised methods, specifically, it can attain a segmentation accuracy of 49.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> on PASCAL VOC2007 test set and an accuracy of 41.3% on PASCAL VOC2012 test set while the network is trained to learn the semantics of the objects in the image using the cheap image label annotation instead of the expensive and time-consuming segmentation masks.
Edge based Prevention System for Crowd Overcrowding

At present, it has become more and more important to prevent crowd overcrowding, especially under the current situation of the coronavirus. In order to process this situation, the governments of world take a lot of manpower and material resources to prevent the crowd overcrowding. Now, there is no effective method to prevent crowd overcrowding in real-time. As the emergence of edge computing, creating a prevention system between edge computing and cloud computing becomes a possibility. This paper introduces a crowd congestion prevention system based on edge, which combines object detection and cluster analysis, and presents the analysis results to the alarm system in the cloud.
Feature point extraction of high-resolution image based on multi-thread mode

To solve the poor real-time problem of SIFT algorithm, the cooperative operation mechanism of multi-core algorithm on multi-thread mode is designed based on OpenMP software runtime. First, a collaboration mechanism between multiple processor cores is designed based on the multi-core operating framework. Then, a high-speed interaction method is designed for the data between different platforms based on the TCP/IP protocol. Finally, the experiment shows that this scheme can greatly make the processing speed up for highresolution image and effectively solves the real-time problem of the algorithm.
WITHDRAWN: Multi-classifiers and deep cnns for implementing image block truncation coding in e-learning platform

Handling the image database with minimal storage complexity, minimal computational complexity and optimal quality is a significant task. To obtain these solutions, many image processing techniques are evolved. In the domain, image compression and decompression are more needed at any cost for effectively handling the complex image databases. Now days, E-Learning resources are widely used around the internet based knowledge sharing environments. In the E-Learning environment, multiple types of data resources are managed. Particularly, organizing the images is more crucial task where multiple qualities of images are appeared inside the E-Learning network databases. This problem expects solutions from effective image compression techniques. Block Truncation Coding (BTC) and Absolute Moment BTC (AMBTC) are the techniques provide useful and easy implementations of E-Learning based image compression platform. At the same time, they are limited to image dissimilarity rate. To maintain the quality of images in both compression and decompression phases, multilevel image analysis models and training phases are required. In this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) technique. DLDCT comprises the baseline benefits of BTC, multi-layered Support Vector Machine (SVM) units and Deep Layered Convolutional Neural Network (DLCNN) for producing classified range of image pixels and compressing the images under controlled circumstances. This proposed DLDCT makes the image compression and decompression with determined observations. This reduces real time errors occur during image reconstruction phases. This proposed system has been implemented and compared with existing works with respect to significant performance parameters.
A Security Situation Prediction Method Based on Improved Deep Belief Network

With the rapid development of smart grids and the continuous deepening of informatization, while realizing remote telemetry and remote control of massive data-based grid operation, electricity information network security problems have become more serious and prominent. A method for electricity information network security situation prediction method based on improved deep belief network is proposed in this paper. Firstly, the affinity propagation clustering algorithm is used to determine the depth of the deep belief network and the number of hidden layer nodes based on sample parameters. Secondly, continuously adjust the scaling factor and crossover probability in the differential evolution algorithm according to the population similarity. Finally, a chaotic search method is used to perform a second search for the best individuals and similarity centers of each generation of the population. Simulation experiments show that the proposed algorithm not only enhances the generalization ability of electricity information network security situation prediction, but also has higher prediction accuracy.
How to Prevent Cross-texting in Mobile Messengers Using Honorifics Features in Korean Text

`Cross-texting' refers to accidentally sending a message to an unintended person. As mobile phone messenger use becomes more active, such mistakes occur frequently. The functions provided by messenger are generally a post solution, and it is difficult for users to find mistakes in advance. In this paper, we proposed a model that analyzes the honorifics features of sentences to determine whether the sentences in the dialogue are cross-texting. By extracting the politeness and completeness features from the sentence, and modeling a specific user's dialogue, it determines whether the target sentence is suitable for the dialogue. This method can provide a hint to easily determine whether the sentence that the user writes is cross-texting, only with the previous chats. As a result of experimenting on a cross-texting dataset created using an actual messenger conversation corpus, cross-texting was detected with 94% accuracy.
Assessing the computational limits of GraphDBs’ engines - A comparison study between Neo4j and Apache Spark

Big Data paradigm has placed pressure on well-established relational databases during the last decade. Both academia and industry have proposed several alternative database schemes in order to model the captured data more efficiently. Among these approaches, graph databases seem the most promising candidate to supplement relational schemes. Within this study, a comparison is performed among Neo4j, one of the leading graph databases, and Apache Spark, a unified engine for distributed large-scale data processing environment, in terms of processing limits. The results reveal that Neo4j is limited to the physical RAM memory of the processing environment. Yet, until this limit is reached, the processing engine of Neo4j outperforms the Apache Spark engine.
Threshold audio secret sharing schemes encrypting audio secrets

Secret sharing is a method of encrypting a secret into multiple pieces called shares so that only qualified sets of shares can be employed to reconstruct the secret. Audio secret sharing (ASS) is an example of secret sharing whose decryption can be performed by human ears. The aim of this paper is to extend the existing result of ASS schemes encrypting audio secrets to the general threshold case. For this purpose, the decryption function is extended from the sum of shares to the weighted sum of shares. Moreover, the notion of noise tolerance is introduced and used to generalize the existing formulation of ASS schemes. Based on this generalized formulation, a construction of ASS schemes encrypting audio secrets is provided and its security and noise tolerance are examined.
Recommendation System using optimized Matrix Multiplication Algorithm

Volume, Variety, Velocity, Veracity & Value of data has drawn the attention of many analysts in the last few years. Performance optimization and comparison are the main challenges we face when we talk about the humongous volume of data. Data Analysts use data for activities like forecasting or deep learning and to process these data various tools are available which helps to achieve this task with minimum efforts. Recommendation System plays a crucial role while running any business such as a shopping website or travel agency where the system recommends the user according to their search history, likes, comments, or their past order/booking details. Recommendation System works on various strategies such as Content Filtering, Collaborative Filtering, Neighborhood Methods, or Matrix Factorization methods. For achieving maximum efficiency and accuracy based on the data a specific strategy can be the best case or the worst case for that scenario. Matrix Factorization is the key point of interest in this work. Matrix Factorization strategy includes multiplication of user matrix and item matrix in-order to get a rating matrix that can be recommended to the users. Matrix Multiplication can be achieved by using various algorithms such as Naive Algorithm, Strassen Algorithm, Coppersmith - Winograd (CW) Algorithm. In this work, a new algorithm is proposed to achieve less amount of time and space complexity used in-order for performing matrix multiplication which helps to get the results much faster. By using the Matrix Factorization strategy with various Matrix Multiplication Algorithm we are going to perform a comparative analysis of the same to conclude the proposed algorithm is more efficient.
Force Field Feature Extraction Using Fast Algorithm for Face Recognition Performance

Abstract Face recognition is method of recognizing individuals by facial expressions. It has become essential for security and surveillance applications, including banks, organisations, workplaces, and social areas, and is needed everywhere. In face recognition, there are a variety of difficulties faced, including face shape, age, sex, lighting, and other variable factors. Another problem is that the scale of the servers for these apps is relatively limited. Education and acknowledgment, thus, are increasingly complicated. In recent years, many unchanged features have been proposed in the literature, in this paper approach the use of the fast algorithm as local descriptors, and as we shall see, it is not only fixed-size features, but also offers the advantage of being highly efficient. The proposed approach allows distinguishing the destination after converting the image to the HSV system, after which the force field features will be extracted using the fast algorithm and then classification by using the distance for three methods (Manhattan, Euclidean, and Cosine) through which a comparison is made to choose the best resolution, as it was found that the resulting accuracy of the two dataset (ORL and UFI) is 99.9%.
Trends in the Formation of the Digital Educational Environment of Universities

The rapidly expanding digital economy requires a quick response of the education system to the changing demands of citizens, society, the state, and business. In this regard, there is a need to create a digital educational environment of the university, as one of the main areas that ensure its competitiveness in modern realities. The aim of the study is to identify the main directions of the formation of a competitive digital educational environment of the university. In this regard, a review of legislative documents was carried out, an analysis of scientific publications in the context of the digital transformation of education, an analysis of the results of our own research, which, together, was the methodological basis of this work. The creation of a digital educational environment is considered in the article from the perspective of a marketing approach, carried out with a focus on the market- its trends, opportunities and strategic development prospects; to the requests of various target groups, considering the joint creation of value in achieving the goals of self-development and self-education of students. In solving the tasks, the authors investigated the trends of modern education in the context of the digital economy; identified the problems that need to be addressed to remove obstacles to the effective use of the digital educational environment of the university; priority tasks are formulated that need to be addressed. The novelty of the study lies in determining the directions for the formation of a competitive digital educational environment based on a systematic integrated approach to solving priority problems in the process of its transformation. The results can be used both by higher educational institutions and other educational institutions.
On the Separating Redundancy of Ternary Golay Codes

Let G11 (resp., G12) be the ternary Golay code of length 11 (resp., 12). In this letter, we investigate the separating redundancies of G11 and G12. In particular, we determine the values of sl(G11) for l = 1, 3, 4 and sl(G12) for l = 1, 4, 5, where sl(G11) (resp., sl(G12)) is the l-th separating redundancy of G11 (resp., G12). We also provide lower and upper bounds on s2(G11), s2(G12), and s3(G12).
Advanced Method of Movement Compensation in Video Coding Technologies

Conceptual bases of construction of a method of compensation of movement of dynamic objects in a video stream directed on increase of efficiency of functioning of information and communication networks are considered. The method is based on the search for possible ways of convergence on six basis points.
Instrumental approach towards tradition in the conceptual and social contexts

&amp;nbsp; The subject of this research is the dynamics of conceptualization of the phenomenon of cultural reproduction, expressed in the concepts of tradition and cultural memory, as well as the related concepts of the invention of tradition, historical memory, and post-truth. The article analyzes the transformation of epistemological approach that took place in the late XX century towards reproduction of culture, reflected in the change of the fundamental conceptual metaphor &amp;ndash; from &amp;ldquo;delivery&amp;rdquo;(traditio) to &amp;ldquo;memory&amp;rdquo;, which means a shift in the dominant approach towards the structure of cultural continuum that appears to be attributed not to the objective reproducible content, rather than its construction by the subject. It is suggested to examine the questions of current interrelation between post-truth and public consciousness. The author creates an instrumental approach towards tradition, which is characterized by pragmatism expressed in the intention towards management of social relations, where tradition is a tool for managing the present through the formation of representations about the past, and constructivism, which implies that tradition is a construct of perception formed in the present, not reflecting the past itself. Being internalized in a broad social context, the instrumental approach is realized within the framework of the state of post-truth, which does not consider the past crucial for the formation of public opinion compared to other personal beliefs, as well as management methods applied to the latter. Problematization of the theme of post-truth demonstrates the internalization of instrumental approach into a broad social context, indicating the cross-effect pf epistemological and general cultural social context with regards to problem of interrelation between the social past and the present. &amp;nbsp;
Recognition of Side-scan Sonar Shipwreck Image Using Convolutional Neural Network

In order to enable the AUV to quickly and automatically identify the image of the side-scan sonar shipwreck, improve the speed of search and rescue of wrecked ships and the accuracy of the verification of shipwreck targets in submarine obstacles. Aiming at the limitation that AUV side-scan sonar image data cannot be transmitted in real time, a method for recognizing shipwreck image of side-scan sonar image based on convolutional neural network is proposed. Sample data is expanded through standardization and normalization processing, data augmentation, etc.; according to the characteristics of the side-scan sonar image sample data set, referring to the classic convolutional neural network VGG-16 model, a simplified convolutional neural network model is constructed; by adding Dropout, the joint adaptability between neuron nodes of the model is weakened, overfitting is reduced, and generalization ability is improved. The experimental results show that the accuracy of the model to the side-scan sonar shipwreck image recognition can reach 90.58%.
Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array

Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.
Lifting Scheme Based Image Retrieval in Medical CT and MRI Databases

In content based retrieval system, the reliability of image retrieval outcomes relies much upon the image features utilised for measurement of image similarity. In this work, we present retrieval of medical images based on lifting scheme wavelet transform. Low computational complexity in terms of integer-to-integer transform is the main advantage of lifting scheme over classical wavelet transforms which are implemented using floating point operation. In this work, the features are extracted from the medical images using lifting scheme. The feature vector, used to measure similarity, is determined by evaluating the standard deviation and energy from the lifting based wavelet filtered coefficients. The retrieved image is the pertinence between the query image and the image from database, the ranking of the similarity relevance is as per the closest similarity determined by Manhattan distance. The proposed work performance is assessed on three different publicly available medical image databases namely NEMA, OASIS and EXACT09. The effectiveness of the proposed method is compared with other well known existing methods in terms of average retrieval precision (ARP) and average retrieval rate (ARR).
Training of engineers in demand in the engineering industry at PSEDA

Abstract The article identified the criteria and levels of formation of the supra-professional competencies (team competency) which are essential for future engineers of technical universities, according to the stakeholders’ feedback on the priority social and economic development areas. The authors defined the formation levels of team competency among students of technical areas of training at the Research Tomsk Polytechnic University. They described and investigated theoretical and empirical research methods that allowed formulating specific pedagogical recommendations to solve the problem identified in the article.
Automatic Optimization of super Parameters Based on Model Pruning and Knowledge Distillation

In recent years, deep neural network has been widely used in computer vision, speech recognition and other fields. However, to obtain better performance, it needs to design a network with higher complexity, and the corresponding model calculation amount and storage space are also increasing. At the same time, the computing resources and energy consumption budget of mobile devices are very limited. Therefore, model compression is very important for deploying neural network models on mobile devices. Knowledge distillation technology based on transfer learning is an effective method to realize model compression. This study proposes: the model pruning technology is introduced into the student network design of knowledge distillation, and the super parameters (temperature T, scale factor λ, pruning rate Υ) are automatically optimized, and the optimal combination of parameters is selected as the final value according to the final performance. The results show that, compared with the commonly used pruning techniques, this method can effectively improve the accuracy of the network without increasing the network size, and the network performance can be further improved by adjusting the value of super parameters.
Efficiency of Parallelization Using GPU in Discrete Dynamic Models Construction Process

The objectives of this paper are: (i) to estimate parallelization efficiency of discrete dynamic models constructing process using GPU; (ii) to compare the execution time of parallel model depending on the order of model, the number of discrete values, the number of GPU threads and GPU blocks; and (iii) to compare the execution time on CPU and GPU. A parallel model for the prediction of sulfur dioxide emissions into the air of Żywiec city (Poland) based on historical observations is built and researched in this paper. This discrete dynamical model was made using an optimization approach that was based on Rastrygin’s althogoritm. Technology CUDA and GPU was used for parallelization of model construction process. We have obtained a parallelization efficiency of 78.1% while executing the constructed parallel model on GPU Nvidia GTS250. The obtained research results suggest that the constructing discrete dynamical models must include the efficient use of parallel computing resources nowadays.