Reviewerly/exHarmony huggingface dataset:
Analysis of Text Collections for the Purposes of Keyword Extraction Task

The article discusses the evaluation of automatic keyword extraction algorithms (AKEA) and points out AKEA’s dependence on the properties of the test collection for effectiveness. As a result, it is difficult to compare different algorithms who’s tests were based on various test datasets. It is also difficult to predict the effectiveness of different systems for solving real-world problems of natural language processing (NLP). We take in to consideration a number of characteristics, such as the text length distribution in words and the method of keyword assignment. Our analysis of publicly available analytical exposition text which is typical for the keywords extraction domain revealed that their length distributions are very regular and described by the lognormal form. Moreover, most of the article lengths range between 400 and 2500 words. Additionally, the paper presents a brief review of eleven corpora that have been used to evaluate AKEA’s.
Application of SVGA video real-time transmission technology in music education information communication

The current music education information has certain information loss in the real-time transmission process, which leads to poor educational effect. The research content of this thesis is based on FPGA video image acquisition and processing system. At the same time, this research mainly uses FPGA as the platform to realize and simulate video acquisition, transformation, storage, display and transmission. This research solves the problem of long-distance real-time transmission of high-definition video stream, and in order to improve the subjective quality of the recovered video at the receiving end, the algorithm processing is added to reduce the blockiness phenomenon in the video displayed at the receiving end. Finally, the validation test was designed to validate the research perspective. The experimental results show that the system works stably and realizes the functions of video image acquisition, conversion, display and transmission, and achieves the design goal. However, due to time constraints and other conditions, the entire design needs further improvement, so that it can provide a basis for the development of subsequent music education information communication technology.
A block encryption algorithm based on exponentiation transform

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Preserving empirical data utility in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e433" altimg="si12.svg"><mml:mi>k</mml:mi></mml:math>-anonymous microaggregation via linear discriminant analysis

Today's countless benefits of exploiting data come with a hefty price in terms of privacy.k-Anonymous microaggregation is a powerful technique devoted to revealing useful demographic information of microgroups of people, whilst protecting the privacy of individuals therein.Evidently, the inherent distortion of data results in the degradation of its utility.This work proposes and analyzes an anonymization method that draws upon the technique of linear discriminant analysis (LDA), with the aim of preserving the empirical utility of data.Further, this utility is measured as the accuracy of a machine learning model trained on the microaggregated data.By transforming the original data records to a different data space, LDA enables k-anonymous microaggregation to build microcells more tailored to an intrinsic classification threshold.To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction by a factor α that penalizes distortion across the classification threshold.The upshot is that thinner cells are built along the threshold, which ends up preserving data utility in terms of the accuracy of machine learned models for a number of standardized data sets.
The Design of Compact SM4 Encryption and Decryption Circuits That Are Resistant to Bypass Attack

In order to achieve the purpose of defending against side channel attacks, a compact SM4 circuit was designed based on the mask and random delay technique, and the linear transformation module was designed with random insertion of the pseudo operation method. By analyzing the glitch data generated by the S-box of SM4 with different inputs, the security against glitch attacks was confirmed. Then, the DPA (Differential Power Analysis) was performed on the designed circuit. The key could not be successfully obtained even in the case of 100,000 power curves, so that the safety of SM4 against DPA is verified. Finally, using Synopsys DC (Design Compiler, Mountain View, CA94043DC, USA) to synthesize the designed circuit, the results show that the area of the designed circuit in the SMIC 0.18 process is 82,734 μm2, which is 48% smaller than results reported in other papers.
An Ontology Framework for Generating Requirements Specification

Requirements engineering is the initial process of software development that critically determines the overall software process quality. However, this process is error-prone. This is generally related to the factors of communication, knowledge, and documentation. With the lack of business knowledge, it is complicated for (technical) engineers to define customer needs. Also, modeling and documenting requirements need much time and effort to ensure that the requirements are valid, and nothing is missed. The current approach of the requirements modeling process mostly focuses on the Unified Modeling Notation (UML) use case, that not provide enough information for a stakeholder to define system requirements. The generated SRS is still lack of detail development guideline that increase risk of development error. The purpose of this research is to guide for the elicitation process to avoid missing and mismatch requirements, and to make the modeling and documentation process more effective and efficient. We propose an ontology framework for generating requirements specification. This framework, namely the Rule-Based Ontology Framework (ROF) consists of two main processes: First, requirements elicitation. This step provides a guideline for the stakeholder to define system requirements based on the problem of the current system and business process enhancement. Based on this final requirement list, the requirements ontology is generated. Second, the auto-generation of the requirements specification document. The document consists of semi-formal modeling and natural language. In this research, we use Business Process Modeling Notation (BPMN) is a modeling language. For natural language documents, we use IEEE for the SRS template.
Effective ways of digital media usage in school education

Our society is increasingly dependent on digital media. Socio-economic changes in Russia and the information explosion that has affected the whole world require a revision of traditional systems, content, methods and technologies of education in the context of various media usage. The purpose of this research is to theoretically substantiate, develop and experimentally prove tools for effective usage of digital media in the educational process of secondary schools in Russia. According to the purpose and hypothesis, the objectives of this research are: to define the essence, functions, didactic and educational possibilities of digital media; to identify pedagogical aspects of their usage in education; to determine structural components of teachers' readiness to using digital media in their work; to develop tools for using digital media in the educational process of secondary schools; to prove experimentally the effectiveness of the developed tools. The research object is the educational process of secondary schools under conditions of digitalization of the Russian education. The subject of this research covers tools of effective usage of digital media in the educational process of secondary schools. The methodological basis of this research was the philosophical concept of dialectical cognition as the basis of scientific pedagogy; the theory of system-complex and personal-activity approaches to the study of pedagogical phenomena, the integration concept of science, education, technology and production. Results: based on the analysis of theoretical sources on the use of digital media in education and the own diagnostic study, the author determined and experimentally tested possible ways of applying digital media as pedagogical tools taking into account their functional load: as means for transforming traditional tasks in a special form; as a teaching assistance instrument; as a communication means; as a reality modeling tool.
HafaNet: An Efficient Coarse-to-Fine Facial Landmark Detection Network

Facial landmark detection can be applied in various facial analysis tasks. It is a challenging problem due to the various poses and high real-time requirements. To balance accuracy and computational efficiency, we propose an efficient coarse-to-fine network by combining heatmap regression and lightweight coordinate regression to detect facial landmarks. The heatmap regression network branch employs an efficient spatial pyramid and attention mechanism to regress to a better quality heatmap. The lightweight coordinate regression network branch introduces the local region perceptron to make the small network focus on the region of interest. Our method develops the advantages of heatmap regression and coordinate regression, which can improve landmark detection. Moreover, we propose a novel two-stage FTS loss function that can further effectively solve the outlier problem in facial landmark detection. The comprehensive experiments demonstrate the effectiveness of our approach.
Sampling Non-relevant Documents of Training Sets for Learning-to-Rank Algorithms

Learning-to-rank (LtR) is a framework that spans from training set generation from unlabelled data to learning a model to runtime efficiency.While a good number of learning-to-rank algorithms have been developed over the past decade, "out-of-the-box" topics such as investigation into the properties of training sets have not been given much attention to date.In this article we investigate the imbalanced nature of LtR training sets, which generally contain very few relevant documents as compared to the non-relevant ones.Since the relevant documents are rare in a document collection, the need to include in the training set as many relevant documents as possible is well-understood.However, the lower bound of the number of non-relevant documents needed to learn a good ranking function is still largely uninvestigated.This article aims at addressing this question with a special focus on random forest based LtR algorithms.We employ both random and deterministic undersampling techniques to reduce the number of non-relevant documents.Minimization of training set size reduces the computational complexity (i.e., learning time) which is an important factor, especially for large scale LtR.Extensive experiments on Letor benchmark datasets reveal that in many cases the performance of a LtR algorithm trained on a much smaller training set (in terms of presence of non-relevant documents) remains largely similar to that of a model learnt from the original training set.This investigation thus suggests that for large scale LtR tasks, we can leverage undersampling techniques to reduce training time with oftentimes negligible effect on predictive accuracy.We further examine the potential benefit of random forest based LtR algorithms in the context of undersampling which demonstrates that the inherent structure of a random forest is conducive to using undersampling, and thereby allows for even more scalability.
Analysis of the Effect of Compensation on Twitter Based on Job Satisfaction on Sustainable Development of Employees Using Data Mining Methods

It is very important that the managers responsible for the performance of their employees know the reasons that affect their job satisfaction in order to carry the organizations to the future and continuity. From this point, the involvement of employees in the development of the organization, the achievement or dissatisfaction of loyalties depends on the people in the Organization. It has shown on various events that one of the ways to deal with get the best execution out of agents is to make them pleasant and perky. Workers everything being equivalent and levels of pay continue getting dynamically hopeless at work - an extended stretch example that should concern supervisors. How, by then do directors make satisfied agents? Since pay has been a fundamental thought of pushing delegates in an Organization. The present investigation broke down the issue of pay and compensation based satisfaction. Data was accumulated and separated similar to drawing in experiences, and the analysis of the data was done through the SPSS package program with a 95% confidence interval. Chi-square test was used to investigate with frequencies and explanatory information. The other purpose of this study is to examine the tweets of people who use social media, based on their own declarations, who have a high job satisfaction due to their salary, and those who work with less salary, and as a result of the analysis on tweets sent by these people, the employees who love their job are distinctive. to make clear differences. In this study, data mining, machine learning and data science methods are applied to data captured on Twitter. A total of 142,656 tweets were made and these tweets were worked on. Among the applied algorithms, the highest success rate belongs to the Grandient Boosted Tree and it can accurately classify on a nearly balanced and bipolar dataset with a success over 99%. As a result of the applied machine learning algorithms, it was observed that there was a significant difference between the sentence structures, the words used and the posts made by those who were satisfied and dissatisfied with the salary they received. Thanks to machine learning, people who have high job satisfaction and who do not have high job satisfaction can be learned by analyzing their twitter accounts. Other machine learning algorithms can also be attempted when sufficient hardware capacity is available. Techniques have used to test the importance of association among pay and compensation based satisfaction. The result demonstrated that there is no significant association among compensation and delegate occupation satisfaction among the respondents. In any case portions of pay, for instance, livelihood improvement and business dependability were significant contributing factors to delegate occupation satisfaction. The assessment recommended that better conveyor headway openings should be given to the agents to assemble work satisfaction.
Detecting cache-related bugs in Spark applications

Apache Spark has been widely used to build big data applications. Spark utilizes the abstraction of Resilient Distributed Dataset (RDD) to store and retrieve large-scale data. To reduce duplicate computation of an RDD, Spark can cache the RDD in memory and then reuse it later, thus improving performance. Spark relies on application developers to enforce caching decisions by using persist() and unpersist() APIs, e.g., which RDD is persisted and when the RDD is persisted / unpersisted. Incorrect RDD caching decisions can cause duplicate computations, or waste precious memory resource, thus introducing serious performance degradation in Spark applications. In this paper, we propose CacheCheck, to automatically detect cache-related bugs in Spark applications. We summarize six cache-related bug patterns in Spark applications, and then dynamically detect cache-related bugs by analyzing the execution traces of Spark applications. We evaluate CacheCheck on six real-world Spark applications. The experimental result shows that CacheCheck detects 72 previously unknown cache-related bugs, and 28 of them have been fixed by developers.
A new sky imager based global irradiance forecasting model with analyses of cirrus situations

High resolution irradiance forecasts based on sky imagers are valuable for applications that require short term decisions based on ramps of solar irradiance.Here, we present our sky imager based forecasting algorithm, using images of a low cost surveillance camera.Model development and evaluation is done separately for the different steps in sky imager forecasting, starting with cloud detection, followed by estimation and extrapolation of cloud movement, and finally deriving irradiance forecasts from the predicted cloud images.We distinguish between clear and cloudy conditions and especially evaluate the effect of cirrus situations on the different forecasting steps.To create binary cloud masks, we adapted a pixel value based cloud algorithm using a set of manually classified pixels.In an independent validation dataset 90.3 % of the pixels are classified correctly.For the circumsolar region, where cloud decision is known to be especially challenging and crucial for the forecasting of the direct component of the solar radiation, we introduce a correction procedure using real-time irradiance measurements and object recognition methods.Applying this method we can significantly improve the cloud detection in the circumsolar region and increase the forecast skill of the cloud decision forecast.The development of the irradiance algorithm is a special focus of this paper.Real-time irradiance measurements and cloud decision information are used as input to our irradiance model.The algorithm is developed using cloud decision information derived from measurements instead of sky imager cloud decision forecasts to exclude the influence of errors in cloud decision and cloud motion methods for model development.Afterwards, the irradiance algorithm is applied to sky imager based cloud decision forecasts.Even though we start with binary cloud information, the distribution of the clear sky index from our forecasts is in very good agreement with the distribution of the measurements.In a validation dataset of 46 days, we receive a positive forecast skill for all forecast horizons larger than 1 min.We also apply our forecast chain to a dataset of two month from an independent measurement station resulting in a comparable forecasting performance.
stRDFS: Spatiotemporal Knowledge Graph Modeling

In Semantic Web, modeling knowledge graph based on RDF becomes more and more popular.There is quite a lot of spatiotemporal information in Semantic Web, and recent works focus on not only general data but also spatiotemporal data.Existing efforts are mainly to add spatiotemporal labels to RDF, which expand RDF triple into quad or quintuple.However, extra labels often cause additional overhead for the system and lead to inefficient information organization management.In order to overcome this limitation, we propose an stRDFS model by labeling properties with spatiotemporal features and the corresponding determination methods of topological relations among different spatiotemporal entities.stRDFS considers spatiotemporal attribute as a part of the RDF model, which can record spatiotemporal information without changing the current RDF standard.Our approach improves the ability of recording and linking spatiotemporal data.More importantly, depending on formatting of spatiotemporal attributes in stRDFS, it will improve the semantic inferring ability, and the users are not required to be familiar with the underlying representations of spatiotemporal data.
A multi-flow information flow tracking approach for proving quantitative hardware security properties

Information Flow Tracking (IFT) is an established formal method for proving security properties related to confidentiality, integrity, and isolation. It has seen promise in identifying security vulnerabilities resulting from design flaws, timing channels, and hardware Trojans for secure hardware design. However, existing IFT methods tend to take a qualitative approach and only enforce binary security properties, requiring strict non-interference for the properties to hold while real systems usually allow a small amount of information flows to enable desirable interactions. Consequently, existing methods are inadequate for reasoning about quantitative security properties or measuring the security of a design in order to assess the severity of a security vulnerability. In this work, we propose two multi-flow solutions - multiple verifications for replicating existing IFT model and multi-flow IFT method. The proposed multi-flow IFT method provides more insight into simultaneous information flow behaviors and allows for proof of quantitative information flow security properties, such as diffusion, randomization, and boundaries on the amount of simultaneous information flows. Experimental results show that our method can be used to prove a new type of information flow security property with verification performance benefits.
METHODOLOGICAL BASIS OF DESIGN OF THE CLOUD-BASED LEARNING AND RESEARCH ENVIRONMENT OF A HIGHER EDUCATION INSTITUTION

The feasibility of using and implementing of the cloud data processing services into the educational process of a higher education institution in order to support educational and scientific activities is substantiated. The results of the analysis of modern psychological and pedagogical researches testify to a steady movement towards the search of new ways of creating and using educational software based on the concept of cloud computing, which significantly changes the means and approaches to the organization of pedagogical activity. The essence of the problem of formation and development of the cloud oriented learning and research environment of higher education institution is analyzed; the basic conceptual and terminological apparatus of the research is specified. It has been determined that in the cloud-oriented environment of a higher education institution, the computer processing activities of participants of educational processes are supported by cloud computing technology, which implies flexible use of a virtual hybrid or only public or corporate ICT infrastructure. The stages of designing a cloud-oriented environment are distinguished, among which there are the stages of pilot design and widespread implementation. The first stage involves the creation and experimental testing of a prototype of the environment. The second stage of design consists in the widespread implementation of the results of the pilot test, at this stage the basic regularities, characteristics and properties revealed in the first stage must be considered and summarized. Both stages have their components, in particular - target, structural-functional, resource, resulting. It is substantiated that in the process of designing it is advisable to rely on the developed system of models of formation and development of the cloud-oriented learning and research environment: the models of educational and scientific components; the general model of its formation and development, the model of grouping of components; the hybrid service model for organizing access to software. The content of a hybrid cloud-based service model encompasses electronic educational and research resources. The cloud-based component grouping model contains different types of services, among which it is advisable to distinguish the following main groups: generalpurpose services; communication services; specialized educational and research services. The results of the pilot design of a cloud-oriented educational and research environment of a higher education institution, carried out on the basis of joint research laboratories of the Institute of Information Technologies and Learning Tools of the National Academy of Educational Sciences of Ukraine and a number of higher education institutions are generalized.