Reviewerly/exHarmony huggingface dataset:
Promoting the Employment of Students as a Priority Activity of Higher Educational Institutions in Modern Conditions

The financial and economic crisis, including the COVID-19 pandemic, determined the negative changes in the modern labor market. Under these conditions, the understanding of the role of higher educational institutions in solving the problems of employment of graduates is of both scientific and practical importance. The key method of collecting information was a questionnaire survey of students (N=974 respondents). The curators of the study groups distributed the link to the questionnaire posted on the Google platform. The purpose of the study is to analyze the specifics of the work of the Employment Assistance Center functioning in the structure of a higher educational institution. It was found that the majority of students (87.8%) consider it necessary to create Employment Assistance Centers in the structure of universities. The following services would be in demand among students: referral for internships, information about vacancies and internships of partner companies, career events, assistance in finding employment during their studies. It is concluded that modern youth is focused on an early start of a career, combining study and work. The following dysfunctions of the activity of the Employment Assistance Center were identified: an insufficient level of information support, a narrowed list of services provided to the students. The conclusion is made that the employment of graduates requires improvement of both pedagogical methods for the development of professional competencies of graduates, and the formation of organizational mechanisms that ensure the provision of services for internships of students, their part-time employment, carrying out activities for interaction with employers. The authors substantiate the concept of “seamless employment” of the students: the integration of the educational process and part-time employment within the study period, focusing the key areas of the university’s activities on the career needs of young people.
Data Privacy and Data Protection Issues in Cloud Computing

This chapter evaluates the key data protection requirements and compliance obligations that governments must account for when entering into contracts with cloud service providers. The chapter concentrates on data protection issues that pose particular barriers for governments attempting to adopt cloud-computing services.
A platform to support the visual analysis of the SALMANTICOR study outcomes: conveying cardiological data to lay users

Cardiovascular diseases are the largest risk factor for mortality in developed countries, and hospitals are facing an increasing workload in relation to cardiovascular diseases. In this regard, policymaking related to these diseases and based on epidemiological knowledge of the population can be a powerful means to prevent and treat them. The SALMANTICOR study was conceived in this context. This study had the purpose of collecting data concerning the prevalence and incidence of structural heart disease in the province of Salamanca (Spain). However, the amount of data and variables collected (more than 300), can make the understanding of the results cumbersome for non-experience users. This work overviews the design and architecture of a data visualization platform to support the exploration of the SALMANTICOR study results, with a special focus on conveying this information to lay users.
K-Means Clustering Based Adaptive Context Assignment Method for Image AC Term Encoding

Context modeling is to classify the input data into several classes and apply different probability models for each class. It plays a critical role in data compression. Conventionally, when constructing the context, one often uses several criterions to separate each axis in the feature domain into several parts independently. However, it does not consider the correlation among different features. In this work, we propose an advanced context modeling method based on over-segmentation and merging in the feature domain using the k-means clustering method. It considers the relation among different features and the number of training data corresponding to each context is more balanced. The experiments in image AC term encoding show that, with the proposed context assignment method, the coding efficiency can be much improved.
Playing and Fighting as an Electric Violinist

The violin is an instrument used in various musical genres. Besides preserving, elaborating, and valuating, the classical form of the instrument as well as the classical violin repertory, an electronic version of the instrument has entered the music business many decades ago. It allows the musician to produce sounds ranging from classical violin sounds to electric guitar or even electric bass sounds. Nora Kudrjawizki (‘Angelstrings’, “One Violin Orchestra”) is an electric violinist living in Berlin and using the instrument for as many different genres and occasions as possible: playing Nirvana songs or fighting with the violin bow as an improvised sword to “Pirates of the Caribbean” music as part of her performance. Her work will be presented as a case study and will be set into a bigger framework with further electric violinist statements generated from the literature. I focus on the differences in the instrumentalist–instrument relation when playing electric or acoustic. My aim is to prove that the electric violin is mostly used to play public and impress others and that there are also musically interesting aspects and individual experiences that should be valued.
Research on TextCNN-based Evaluation of Rationality of Narrative Text Structure

Automatic Essay Scoring refers to the use of computers to score composition by some technologies. This process does not require human intervention. Rational text structure analysis is an important part of automatic essay scoring. However, the study of text structure is still in its infancy, ignoring its importance to the evaluation. Existing research lacks a corpus for the evaluation of text structure. The recognition of text components mostly uses artificial experience for feature selection, and evaluation model is established based on them. To figure out these problems, this paper refers to the curriculum standards, works with experts to build text structure standard and labeling method, and formulate corresponding labeling specifications. Finally build a corpus of a certain scale. TextCNN are used to build a model for the text structure. Model treats each article as a whole for training, and realizes the use of deep learning algorithms to make the model automatically evaluate. The results in test set show that in the constructed narrative composition corpus for grades 5-9, the accuracy of the model can reach 72.4%.
Semi-Autonomous Human Detection, Tracking, and Following Robot in a Smart Building

Surveillance and monitoring in a smart building can usually become a difficult task to carry out. Multiple factors, including weather conditions, venal nature of humans, and risky conditions – such as the presence of land mines – make effective monitoring a challenge. To address these challenges, mobile robots can offer an effective solution. The proposed work aims to address these challenges by building a semi-autonomous system that can detect human presence effectively and also track the human until the identity is verified. The proposed work uses the well-known method “histogram of oriented gradients” (HOG) for human detection. For tracking and following, the algorithm built has been based on the multiple instance learning (MIL) and Lucas-Kanade (LK) optical flow tracking method. A password protection module has been included in the system in order to reset the robot once the trespasser has been caught by the authorities or a friendly person was detected as a possible trespasser. The system has been implemented and tested on the Raspberry Pi board running the OpenCV library.
A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers

Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.
Mixed Precision Level Based Sparse Grid Quadrature Filter for Nonlinear System

In order to reduce the computational burden of the sparse grid quadrature filter (SGQF) in nonlinear filtering system, the mixed precision level based sparse grid quadrature filter (MPLSGQF) is proposed. The formulations of the Gaussian approximation filter and the SGQF for the nonlinear discrete-time system are presented. The MPLSGQF uses the different precision level SGQF algorithms for the time prediction process and measurement update process in the filtering cycle. For the more frequent time prediction process, the level-2 sparse grid quadrature (SGQ) rule is utilized for sampling, and the level-3 SGQ rule is used in measurement update process. The algorithm reduces the computational burden by reducing the number of sampling points in time prediction process. A 5-dimensional target tracking problem is used as a simulation application to compare the filtering performance and running time of the MPLSGQF. The simulation results show that compared with the unscented Kalman filter (UKF), the level-2 SGQF and the level-3 SGQF, the MPLSGQF can reduce the computational burden while maintaining the filtering accuracy. It proves that the MPLSGQF is a better choice to balance accuracy and computational amount.
Experience in developing a web application for relational databases modeling with a forward engineering function for training students of technical specialties

The article actualizes the need to develop software for modeling relational databases for use in the process of teaching students of technical specialties in disciplines related to databases. The problem is considered from the point of view of assessing modern software used in the process of teaching students database design skills. Based on the shortcomings identified during the software review, a number of requirements for the actual software were determined. Formed key requirements are mobility, accessibility, versatility and openness of the development platform. The article describes the process of solving key problems that arose during the implementation of a project to develop a web application for modeling relational databases in accordance with the generated requirements. The practical implementation of the following functions is sequentially considered: creation of a logical relational data model, creation of a physical data model, direct engineering into relational database software. The main technological solutions used in the development of a web application to ensure the qualities specified in the condition are described. The result of the work is the successful testing of the development results in the process of creating a real web application, both within the framework of laboratory and practical work in the disciplines “Design and administration of databases” and “Data management”, and at the stage of writing graduate works for technical directions of training.
Geological environment evaluation of river valley slope projects based on remote sensing technology

The method to evaluate the geological environment of river valley slope projects has a small precision in determining the weight of geological environment evaluation index, which leads to a large error between the results of geological environment evaluation and the actual value of river valley slope projects. Therefore, the reverse research of the geological environment evaluation of river valley slope projects based on remote sensing technology is proposed. Remote sensing technology is used to build the projects’ geological environment evaluation framework of river valley slope, on this basis, the Delphi method is used to preliminarily determine the geological environment evaluation index, the cluster analysis is used to screen the evaluation index, the evaluation system of projects’ geological environment is constructed, the AHP method is used to calculate the evaluation index weight, and the corresponding remote sensing image number is obtained according to the projects’ geological environment evaluation system. According to the data, the corresponding software is used to preprocess it, and the remote sensing data of projects’ geological environment is obtained. The grey evaluation method is constructed based on AHP to realize the evaluation of geological environment of river valley slope projects. The experimental results show that compared with the existing methods, the evaluation error of the proposed method is smaller, which fully proves that the application performance of the proposed method is better.
Inferencing Relational Database Tuning Actions with OnDBTuning Ontology

OnDBTuning is a relational database (automatic) tuning ontology. Ontologies are software artifacts that represent specific domain knowledge and can infer new knowledge. However, most cases involve only a formal and static description of concepts. Moreover, as database tuning involves many rules-ofthumb and black-box algorithms, it becomes challenging to describe these inference procedures. This research work first presents the OnDBTuning ontology solution focusing on the inference of tuning actions. Next, we provide an actual implementation using SPARQL Inferencing Notation (SPIN). Finally, we discuss a practical evaluation for index recommendation.
Research and Analysis of Urban Road Traffic Anomie Behavior Based on Fault Tree Model

Many traffic participants are not aware of traffic misconduct, which leads to frequent traffic accidents, which in turn worsens the traffic environment and increase economic losses. This paper designs a questionnaire survey on the anomie behavior of three types of traffic participants and analyzes influencing factors, such as age and gender, using SPSS21.0 analysis software. A fault tree model is established to determine the key factors affecting the anomie behavior of traffic participants and analyzes the degree to which the key factors produce anomie behavior. FreeFta software is used to analyze and calculate the minimum cut set and structural importance, as well as perform quantitative analysis and calculation of the probability and critical importance. Finally, according to the analysis results, the key factors are summarized, and targeted measures are given to the three types of traffic participants, which is of great significance to alleviate traffic accidents.
Investigating Students’ Language Learning Strategies During Online Learning: How They Deal with Speaking Ability

The pandemic situation shifted the learning process from offline to online. It challenges the students in English learning, especially in learning their speaking ability. However, the students manipulated the strategies by utilizing online platforms or mobile phone applications accordingly to their learning experience. This study investigates students' language learning strategies and how the strategy is applied in learning speaking ability. The case study was carried out in a senior high school. The respondents 64 were chosen purposively to be involved in this study. The instruments of data collection were conducted by questionnaire and interview under Strategy Inventory for Language Learning (SILL). The results showed that 63 (95%) of students used metacognitive strategies in the form of centering learning process, arranging, and planning learning strategies. Moreover, 61 or 91% of students applied practicing, receiving, and sending messages of group cognitive strategies to motivate them to practice their speaking ability during online learning. Furthermore, less than 80% applied social, memory, affective, and compensation. It recommended that the teacher be a facilitator by providing a good stimulus for the students to motivate themselves to be self-regulated.
Agile Target Tracking Based on Greedy Information Gain

The information acquisition capability of remote sensing nodes is limited by the scarcity of sensing, processing, communications, and power resources. In this work, a resource allocation method is proposed with an application to target tracking. The method predicts the locations of moving sensing nodes that provide improved information acquisition and tracking performance. Information acquisition is quantified by the expected information gain for each sensing node action. A greedy strategy is applied to select actions that provide the highest information gain while reducing computational complexity. The improvement in tracking performance by the proposed method is demonstrated through a simulation-based experiment. The simulation scenario includes tracking a point target with measurements from a stationary network of sensing nodes and coordinated measurement acquisition from moving nodes using the proposed greedy information gain method.
Data Mining and Knowledge Discovery in Computer Aided Medical Diagnosis System

In view of the characteristics of epidemiological research, in the computer-aided medical diagnosis system, the use of medical database data mining and knowledge discovery technology is of great significance and its development inevitability. Based on data mining and knowledge discovery, this paper studies the characteristics and functions of computer-aided medical diagnosis systems. This article analyzes and synthesizes the general laws and particularities of the research and development of such systems, and points out some of the problems that need to be solved and the direction of future research and development.
Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale

Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating.
Institutional framework for regulating innovative activity of higher education organizations

Subject. I consider parameters of the condition of higher education organizations that are formed under the influence of institutional impact of the State, in the aspect of giving the former the status of a driver of innovative development. Objectives. The aim of the study is to review the institutional framework for regulating the innovation activity of higher education organizations. Methods. The study employs the method of dialectical cognition, as well as historical, structural-functional, systems research methods. Results. The paper presents an overview of institutional framework for regulating the innovation activity of universities, resulting from the growing importance of higher education organizations in the development of innovative economic relations at the present stage. It analyzes government initiatives aimed at achieving the goals based on national interests. The paper provides a list of councils and funds supporting and stimulating the development of innovative activity of educational organizations in Russia. Conclusions. The innovative development of the national economy as an essential condition for its competitiveness, which is implemented against the background of the growing role of educational organizations in this process, determines the urgency of permanent improvement of the institutional framework for regulating the innovative activity of educational organizations.
A Method for Identifying Ground Truth Labels in Regression Problems using Annotator Precision

We propose a novel method for deriving ground truth labels for regression problems that considers the precision of annotators separately for each label. This method ensures that higher performing annotators contribute more to the final landmark position which is in contrast to conventional methods that assume all annotators are equally accurate in completing the set task. In addition to describing the novel method, a set of preliminary experimental results is also provided, comparing the performance of the precision method to that of the global mean.