Reviewerly/exHarmony huggingface dataset:
Some properties of the output sequences of combined generator over finite fields

Математические методы криптографии 151 Conclusion and Future WorkIn this work, we have presented a new algorithmic-algebraic scheme based in the Lai Massey structure for constructing permutations of dimension n = 2k, k 2. For the common case k = 8, we have obtained new cryptographically strong 8-bit permutations having better resistance to algebraic attacks in comparison with the inversion function in GF(2 8 ) which so far has the best-known values for nonlinearity and differential uniformity.Compared to the best nonlinearity (108, for k = 4) offered by the construction presented in [6] and later generalized in [7], the nonlinearity for the permutations obtained by our scheme slightly decrease up to 104, but to the best of our knowledge the schemes presented in [6, 7] can not produce involutions and orthomorphisms with strong cryptographic properties, so we can conclude that the new structure presented in this work is more powerful and attractive due to the diversity of permutations that can be constructed.Interestingly, the involutions and orthomorphisms founded in this work have comparable classical cryptographic properties like those constructed by using spectral-linear and spectraldifference methods [8].The main advantage of our 8-bit permutations is that they can be constructed using smaller 4-bit components which could be useful for the implementation of the S-Box in hardware or using a bit-sliced approach.We only presented a new scheme that can help to find permutations, involutions and orthomophisms with rather good cryptographic properties.There are several questions (theoretical results, hardware and bit-sliced implementations, efficient methods of masking) about the construction suggested in this work which are left as future work.
An Adaptive Face Tracker with Application in Yawning Detection

In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.
We Know What They've Been Put Through: Revisiting High-scalability Blockchain Transactions.

Scalability has been a bottleneck for major blockchains such as Bitcoin and Ethereum. Despite the significantly improved scalability claimed by several high--profile blockchain projects, there has been little effort to understand how their transactional throughput is being used. In this paper, we examine recent network traffic of three major high-scalability blockchains--EOSIO, Tezos and XRP Ledger (XRPL)--over a period of seven months. Our analysis reveals that only a small fraction of the transactions are used for value transfer purposes. In particular, 96% of the transactions on EOSIO were triggered by the airdrop of a currently valueless token; on Tezos, 76% of throughput was used for maintaining consensus; and over 94% of transactions on XRPL carried no economic value. We also identify a persisting airdrop on EOSIO as a DoS attack and detect a two-month-long spam attack on XRPL. The paper explores the different designs of the three blockchains and sheds light on how they could shape user behavior.
3D Printing Watermarking Algorithm Based on 2D Slice Mean Distance

3D printing has recently been applied to many areas of life including education, healthcare, aerospace, automotive, industrial design and other domains in industry.Due to the fact that the benefit of 3D printing is significant, the copyright issue of 3D printing has been become essentially and importantly.To solve the copyright issue of 3D printing, we would like to propose a 3D printing watermarking algorithm based on the mean distance of 2D slices in this paper.In the proposed method, 3D printing model is sliced into a set of 2D slices, and each 2D slice is then embedded a bit of watermark data by changing the length of mean distance of that slice.Finally, the coordinates of points in each 2D slice will be altered according to the changed mean distance value of 2D slice which has been embedded watermark bit.Experimental results showed that the mean distance error between the watermarked 2D slices and the original 2D slices is very small.The bit error rate (BER) between the extracted watermark and the original watermark is also very low.So, we verify that the invisibility of the proposed method is effective, and the proposed method is also robust to geometric attacks as rotation, scaling, and translation.Comparing to the conventional works, the proposed method also provides a solution with better accuracy.
Formation of components of smart platform for the management of power systems and networks

Features of implementation of the Smart Grid concept in modern energy systems and networks with consideration of modern energy drivers, in particular, Internet energy, Internet of things, dispersed generation, development of customer services and aggregators of demand and supply for electricity, are considered. The basic components of the development of the electric power industry have been evaluated taking into account the requirements of energy transition, digitalization of the economy, in particular, business applications and information and communication systems. The elements of the modern technology platform Smart Grid are given. An assessment of the peculiarities of the formation of an intelligent platform for the management of energy systems and electric networks for individual energy companies is given.Features of construction of the Distributed Energy Management System (DEMS) are considered in detail. Features of DEMS are analyzed on the example of DEMS by Siemens. The planning and management functions of DEMS are considered. It is shown that DEMS controls provide control and monitoring of the power of all power generators, storage equipment, and flexible demand, as well as the ability to control to maintain a consistent electricity exchange profile.The basic functions of the Distribution Management System (DMS) are presented. The control systems developed by Oracle, ETAP and Schneider Electric have been analyzed as components of the customer-oriented power distribution management platform. Here is a description of the individual ETAP ADMS modules and the basic SE ADMS features developed by ETAP and Schneider Electric, respectively.
Automatic Query Refining Based on Eye-Tracking Feedback

This paper presents a new method named AQueReBET, which automatically refines a query set by an information seeker searching on the web. A revelation of the intention of an information seeker who is running a search can bring a significant improvement to the search process, and to browsing as well. It is practically impossible to acquire such intention by the explicit indication (feedback) due to the fact that web browsing takes place in real time. Therefore the intention must be determined in some other way. We hypothesize that it can be approximated by means of the implicit feedback preferably in the form of data from an eye tracker and mouse. We propose a method which automatically refines a seeker’s search query and thus we can offer documents with higher relevance, decrease the number of query reformulations and increase the seeker’s satisfaction. The query refinement is based on an analysis of gaze data from an eye tracker and also on groupization. In the proposed method, we calculate word-level importance based on term frequency, term uniqueness (tf-idf) and total fixation duration within the subdocument (word's snippet in search results).
Metaphor Analysis on Students’ English Poems

This study aims to know the students’ ability in using a metaphor for their English poems writing. Through the poem, the poet uses the metaphor to sharpen the ideas as the media to express the feeling, thought and life experience. In this case, love poems were written by the fifth-semester students using metaphor. This study uses a qualitative method to gain a description of the metaphor analysis in students’ English love poems. In research findings, the writer found that the students use several kinds of metaphor in semantic perspective and the mystical love metaphor represented in their poems.
The Development of Student Worksheets Based on Simulation Methods

This study aims to determine, the process of development, eligibility and student responses to student worksheets based on simulation methods. This type of research is Research and Development (R&amp;D), using the Borg &amp; Gall development model. The subjects of the study were students of class XI of SMA Negeri 1 Sambelia. The results showed that the results obtained were validated material, language, and design 3.44 with the appropriate category, and the students' response was 4.16 with the appropriate category. The results showed that the development of student worksheets based on simulation methods was feasible to use and influenced student learning outcomes in economic subjects. The contribution of research as a tool in the learning process for teachers and students in schools.
A Dempster-Shafer Evidence Theory Based Multimodal Human Gesture Recognition Method

Hand gestures are widely used in human-computer interaction, and dynamic gesture recognition is still a challenging task. In this paper, a Dempster-Shafer evidence theory based multimodal human gesture recognition method is proposed. Firstly, the audio-based and skeleton-based command recognition models are established. Then, an alignment method for multimodal recognition results of continuous gestures is proposed to combine the recognition results of the audio-based model and the skeleton-based model for the same action into the same group. Furthermore, for the results in each group, the Dempster-Shafer evidence theory is used for fusion. Finally, the performance of our method is evaluated using the ChaLearn Multi-modal Gesture Recognition dataset. The results show that this method can effectively improve the recognition accuracy of dynamic gestures by fusing information from audio and skeleton.
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index

Detecting microcalcification clusters in mammograms is important to the diagnosis of breast diseases. Previous studies which mainly focused on supervised methods require abundant annotated training data but these data are usually hard to acquire. In this work, we leverage unsupervised convolutional autoencoders and structural similarity (SSIM) based post-processing to detect and localize microcalcification clusters in full-field digital mammograms (FFDMs). Our models were trained by patches extracted from 3,632 normal cases, in total with 16,702 mammograms. Evaluations were conducted in three aspects, including patch-based anomaly detection, pixel-wise microcalcification localization, and microcalcification cluster detection. Specifically, the receiver operating characteristic (ROC) analysis was used for patch-based anomaly detection. Then, a pixel-wise ROC analysis and a cluster-based free-response ROC (FROC) analysis were performed to assess our detection algorithms of individual microcalcifications and microcalcification clusters, respectively. We achieved a pixel-wise AUC of 0.97 as well as a cluster-based sensitivity of 0.62 at 1 false positive per image and 0.75 at 2.5 false positives per image. Both qualitative and quantitative results demonstrated the effectiveness of our method.
SMILE: Smart Monitoring IoT Learning Ecosystem

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results. Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.
People Search on Social Media Platform Using Face Recognition

Face Recognition has received a lot of attention lately due to its varied applications. It has been widely used in defense projects and crime prevention. Such applications would require face recognition to be computed in real time when incorporating into mobile phones. This would encounter major problems like limited storage and high processing power. This could be taken care of at the cost of utilizing network bandwidth to perform the computation on a server which would make the process much faster. This paper concentrates on using two techniques to perform face recognition and return the results to the mobile devices which would then search the person on social networking sites, thus giving more insight about the person’s background.
Background Modelling using a Q-Tree Based Foreground Segmentation

Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only inthe intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects. The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.
Method of Coupling Metrics for Object-Oriented Software System Based on CSBG Approach

Context . Coupling between classes is an important metric for software complexity in software systems. Objective . In order to overcome the shortcomings of the existing coupling methods and fully investigate the weighted coupling of classes in different cases in large-scale software systems, this study analyzed the relationship between classes at package level, class level, and method level. Method . The software system is considered as a set of special bipartite graphs in complex networks, and an effective method for coupling measurement is proposed as well. Furthermore, this method is theoretically proved to satisfy the mathematical properties of coupling measurement, leading to overcome the disadvantages of the majority of existing methods. In addition, it was revealed that the proposed method was efficient according to the analyses of existing methods for coupling measurement. Eventually, an algorithm was designed and a program was developed to calculate coupling between classes in three open-source software systems. Results . The results indicated the scale-free characteristic of complex networks in the statistical data. Additionally, the calculated power-law value was used as a metric for coupling measurement, so as to calculate coupling of the three open-source software. It indicated that coupling degrees of the open-source software systems contained a certain impact on evaluation of software complexity. Conclusions . It indicated that coupling degrees of the open-source software systems contained a certain impact on evaluation of software complexity. Moreover, statistical characteristics of some complex networks provided a reliable reference for further in-depth study of coupling. The empirical evidence showed that within a certain range, reducing the coupling was helpful to attenuate the complexity of the software, while excessively blindly pursuit of low coupling increases the complexity of software systems.
Application of Interactive Cloud Class Teaching Platform in “Embedded System” Course

The ministry of education has released the education information 2010-2020 ten-year development plan, striving to promote the construction and application level of informationized teaching in colleges and universities. This paper mainly discusses the application of interactive cloud class teaching platform in “embedded system” course.
Grayscale Portrait Colorization using CNNs and Pretrained VGG-Face Descriptor

Colorization problem is a process of adding colors to a grayscale image. Traditionally it requires human-labeled color scribbles on the grayscale image and the image is colored by propagating the scribbled colors throughout the image using optimization techniques. It is not very long ago that colorization without the intervention of human was almost impossible. Recently colorization using Neural Networks has achieved big success due to the availability of huge datasets. This paper attempts to color only portrait images using Convolutional Neural Networks along with a pretrained VGGFace descriptor as a global feature extractor. It has been shown that using separate networks, one to extract 3x3 patch sized features or local features and another to extract global features, result in higher quality of colorization outputs. In portrait colorization, we used VGGFace descriptor as the global feature extractor that encodes a grayscale portrait into a vector representation which later can be used to train the system to have overall coloring effects on the grayscale portrait using fusion technique. To measure the colorfulness of the output image we introduce the use of colorfulness metric that has the potential to capture the perceptual colorfulness essence from the output images. To evaluate the model performance we used Mean Squared Errors (MSE) and Mean Absolute Errors (MAE).
THE ESSENCE AND INTERRELATION OF THE BASIC CONCEPTS OF THE THEORY OF CHANGE MANAGEMENT IN EDUCATION IN THE PHILOSOPHICAL AND SOCIAL ASPECT.

Abstract. The formation of competitive ability of our state on the world stage led to the development of change management theory, in particular, in the educational field. The modernization of education is impossible without understanding the philosophical and social aspects of differentiation of the conceptual apparatus in the theory of change management. The urgency of the problem is enshrined in such normative documents as the Law of Ukraine on Education and the Concept of the New Ukrainian School.&#x0D; The philosophy of change pervades both classical and modern philosophical sciences, in which the leading idea is the general variability of events. In the social context, scientists will highlight the problem of managing human life in the environment, which is directly the task of education.&#x0D; Today, the educational institution is managed in line with changes. It has been investigated that the philosophical aspects of change management relate to particular concepts. It is substantiated that the concepts of «change» and «development», «process» and «evolution», «change» and «innovation» are in a dialectical connection to the extent of development of society. However, these concepts cannot substitute each other&#x0D; Project developments specialize in the practical management of changes in educational institutions that apply and deepen the change of a single apparatus in philosophical and social work, which allows to shift a deeper understanding of the integral essential management processes.&#x0D; The prospect of further research is the study of the social effects of applying change management mechanisms in general secondary education.
Exploring the optimal design of computer control system for heating boilers in power plants

Objective: For stable and efficient control of the heating boilers in power plants, an improved Smith-fuzzy PID algorithm is used to optimize the computer control system for heating boilers. Methods: Under the computer control system, the pressure, exhaust gas temperature, water temperature, safety, and energy consumption of heating boilers are explored, thereby determining the optimization effect of the computer control system. Results: The improved Smith-fuzzy PID algorithm has the optimal control effect on water temperature and pressure of the heating boilers, with the highest balance and stability. In comparison, the fluctuations in temperature control curves under Smith-PID and PID algorithms are large. Compared with the exhaust gas temperature of the other two algorithm systems, the exhaust gas temperature of the improved Smith-fuzzy PID algorithm-based computer system is reduced by 40 ?C, which decreases the consumption of coal resources. Conclusion: The improved Smith-fuzzy PID algorithm-based heating boiler computer control system has the most prominent effects on water temperature, pressure, and exhaust gas temperature. The designed system is accurate and reliable, satisfying the actual design requirements of computer control systems for heating boilers.
A data-driven design framework for early stage PSS design exploration

Ubiquitous and pervasive computing holds great potential in the domain of Product-Service Systems to introduce a model-driven paradigm for decision support. Data-driven design is often discussed as ...