Reviewerly/exHarmony huggingface dataset:
IMPORTANCE OF SOFTWARE QUALITY MODELS IN SOFTWARE ENGINEERING

The purpose of this paper is to identify the importance quality in software engineering when the projects or products are developed. The degree to which a component, system or process meets specified requirements and/or user/customer needs and expectations is the quality. The totality of functionality and features of a software product that bear on its ability to satisfy stated or implied needs is software quality. Some even say that ‘quality’ cannot be defined and some say that it can be defined but only in a particular context. Some even state confidently that ‘quality is lack of bugs’. In this paper we discuss about the quality and the quality models.
A Comparative Analysis of Cloud Based Watson System and CNN for Gesture Recognition Systems

According to census 2011, the number of disabled people in India is 2.68 crores. Out of those, about 19 percent have a problem in hearing. With the advent of Convolutional Neural Networks (CNN) deployed on the host system and multi cloud platforms like IBM Watson, an important challenge faced by the developers is selection of suitable architecture for deployment. The ability of CNN to model non-linear relationships enables it to be used widely in biomedical domain and thus for the problem of disabled people. Recognition models deployed on Cloud offer on-demand secure storage, analysis and rapid scalability of services. This paper aims at providing a comparative study between the two architectures. For the first type of architecture the gesture of a mute person is recognized using image processing and CNN. Whereas second architecture uses cloud based visual recognizer to recognize the gestures. The prominent parameters such as recognition accuracy, angled detection and response time that play an important role when deploying the two architectures are measured and provide a perspective over the selection of architecture. The accuracy obtained for the CNN model is 98% and 97% for the cloud-based Watson model for the trained tested classes.
Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

In this paper, a new effective method for automatic detection of circular traffic signs is introduced. Automatic traffic sign recognition is a crucial application of Driver Assistance Systems for safe and comfortable driving conditions. The major step of traffic sign recognition is to detect and localize the traffic signs if they exist. An important portion of traffic signs (i.e. regulatory traffic signs) has circular shape. They include vital information about the traffic rules and regulations (especially the speed limits). Therefore, this study introduces an advanced circular detection method to detect and localize the circular traffic signs. In the previous literature, it is known that a circle creates a distinctive sinusoidal structure in Straight Line Hough Transform (SLHT). This study exploits this notion in circle detection by trying to catch a part of the envelopes of this sinusoidal structure. First, post edge detection is applied to SLHT, and this image is called H-Edge image. Then, edge linking is performed on H-Edge image to obtain multiple candidate curves. By sinusoid curve fitting and sinusoidal normalization, the curves belonging to the sinusoidal structure are identified, so the circle in the original image is detected. Furthermore, a new effective iterative linear image segmentation method which is based on local minima in SLHT is proposed. Combining these two methods and color filtering, a new effective method for circular traffic sign detection is obtained. For certain sample images, the new method effectively detects and localizes the existing circular traffic signs.
Methods of teaching the basics of programming in Python

The article discusses the main trends in the study of programming languages in the school course of informatics. The programming languages (Basic, Pascal, C, Python, algorithmic language) that are acceptable for use at the Unified State Exam in informatics and ICT and their main features are listed. The analysis of the use of the proposed programming languages in educational and methodological kits on informatics is carried out. The program of the elective course for teaching the basics of programming in Python is proposed. It contains an explanatory note, goals and objectives of the course, thematic planning, course content, expected learning outcomes. The methodical material on the theme "Programming of branching algorithms" in Python is considered.
THE ATTRACTIVENESS OF KINESICS IN A SEMIOPRAGMATIC PERSPECTIVE: A STUDY ON LANGUAGE LEARNING

Purpose of the study: Language is a productive communication tool for teachers to interact with learners. The use of non-verbal Language in speaking becomes strong support when the speech event takes place. This study aims to determine the elements of kinesics and politeness of the Language of the instructor in learning.&#x0D; Methodology: The method used in this study uses descriptive methods. The subjects involved in the research were one lecturer in Indonesian language courses andMahasaraswati university students who were taking Indonesian language courses. The study was conducted in 6 different classes for three meetings.&#x0D; Main Findings: The results showed that non-verbal Language is inherent and contributes to the application of the instructor’s politeness principle in learning. Utilization of kinesics in the politeness of Language perfects a speech that is based on the politeness of Language so that the care of speech harmony can be sustained and to the success in maintaining the face of the speech partner especially emotional closeness between learners and instructors.&#x0D; Application of this study: Learning includes embedded values ​​and imprinted into a role model for learners. The attitude that is no less important and inherent in the instructor’s self is the kinesic attraction that causes the learner’s pleasure and comfort when learning takes place.&#x0D; Novelty/Originality of this study: Research in Indonesian has not yet led to politeness in Language. The utilization of non-verbal Language in speaking becomes strong support when the speech event takes place, this is because Language is the main communication tool for interaction in learning.
Automation of business processes of an educational organization: Financial module

The national program “Digital Economy of the Russian Federation” and the modernization of education, the transition to new professional standards, the need for personnel with digital competencies, make it necessary the automation of the main and auxiliary business processes of the educational organization at different levels of management. The article presents the results of the research, the object of which is the management of educational, scientific and educational activities, the subject of the research is the process of creating a unified information and educational environment of the university, the purpose of the research is the automation of business processes that contribute to the adoption of competent and operational management decisions in accordance with the Federal Law “On Education in the Russian Federation”. The article deals with the issues of designing the educational environment of university on the basis of object-oriented and structural approaches. The experience of an educational organization in designing a unified information space based on modules (subsystems) that automate the main business processes related to the management of educational activities in the South Ural State Humanitarian Pedagogical University is described. The article describes the software solution used by the university to achieve its goals by integrating it into the main and auxiliary business processes. The experience of designing a unified information and educational environment based on the 1C:University PROF system at the South Ural State Humanitarian Pedagogical University can be used by other educational organizations to build an integrative management system based on ready-made software systems.
An Empirical Study on Neuroevolutional Algorithm Based on Machine Learning for Crop Yield Prediction

Classification algorithms create an overwhelmingly large number of rules, sometimes exceeding the number of data instances in the data set. Large number of rules hinders decision making. Therefore, there is a need for decreasing the rules before presenting it to the user. The process of removing unwanted rules is known in the data mining literature as rule pruning. In this pruning process, care should be taken to preserve the accuracy of the classifier. Mining compact classifiers with less number of rules that are also accurate and novel is a challenge. Thus, rule mining is a multi-objective optimization problem. Finding the best combination of subjective and objective metrics to present the user with compact, accurate and novel rules is the problem taken for study in the present study.
Jaunu burtu veidošana ar diakritiskajām zīmēm latviešu valodas kā svešvalodas apguvēju tekstos

A Latvian learner corpus “LaVA” is being built in the Institute of Mathematics and Computer Science, University of Latvia. The corpus includes texts written by beginner learners in the first two semesters of learning Latvian as a foreign language. The texts are written by hand and digitized afterwards in order to reduce the issues that could be caused by the necessity to learn not only writing itself but also using a foreign keyboard. One of the features that cannot be digitized is the new letters created by adding diacritical marks which are not used that way in the standard Latvian alphabet. Since one of the essential steps in learning to write in a language is learning the letters and diacritical marks of that language, this study aims to find instances of such newly made letters and to discuss the basic quantitative measures in order to define hypotheses and areas of interest for further research of such usage. Altogether 322 texts were searched, and 175 examples were found. The amount of examples found in 2nd semester texts was less than half the amount of examples found in the 1st semester texts, but the percentage of texts containing examples was higher than expected – more than 33 % in the 1st semester and almost 20 % in the 2nd semester. It leads to a conclusion that this is quite a common occurrence but also prone to reduction in the second semester. The corpus does not provide any data on later semesters so it cannot be predicted when such instances should become a rare, individual feature rather than a common one. The average amount of examples in a text is not high, though. Counting only the texts where at least one example was found, the average amount of examples per text is 2.136 in the 1st semester and 1.690 in the 2nd semester. Considering that the absolute lowest possible value here is 1, it should not be considered as a high value. Therefore, using diacritical marks to make new letters, while a common feature of the Latvian interlanguage, could be characterized as casual rather than systemic. However, that does not exclude the possibility of certain patterns in usage. The currently collected data already shows that there are some words – such as garšo, viņš, ļoti, četri – where examples were found in more than one author’s text. Examples of using unsuitable diacritical marks are also sometimes found next to letters for which said diacritical marks would be suitable. This should be explored more thoroughly using qualitative methods. The size of the corpus keeps growing; the expected size upon completion is 1000 texts. When it is reached, it would be useful to repeat the study and check whether the larger amount of data still confirms the same assumptions. The larger sample size would also allow for more detailed quantitative analysis discussing each letter, diacritical mark, placement of the diacritical mark, and metadata collected for the corpus, such as gender, native language and other spoken languages by the authors of the texts.
Aerial foggy image rejection using single neuron

Aerial images taken during terrain mapping can be affected with a foggy weather.In this paper we proposed new fog detection method and image rejection using single neuron.Fog detection is based on statistical data collected by converting color image to grayscale image and then applying gamma correction factor.With this statistical data, training dataset is created for single neuron.Neuron is simple two input neuron with sigmoid activation function.We establish connection with single neuron and statistical data so it can recognize is there presence of fog in aerial image.
AWS EC2 Spot Instances For Mission Critical Services

For over a decade now, Amazon Web Services (AWS) has offered its spare capacity at a discounted price in the form of EC2 spot instances. This discount comes at the price of variable pricing and sudden instance termination. In this paper, we present a machine-learning solution to one of the challenges when using AWS Spot Instances, namely the termination of the instance on short notice. Our system, Spot Instance Management System (SimS), can effectively manage spot instances and keep up the availability at the desired level using 100-tree Random Forest Regression model. By using a risk assessment mechanism and proactive actions, SimS assures a three-nines SLA using AWS spot instances with lower running costs on workloads for a major European financial institution.
MOOC opinion mining based on attention alignment

Purpose The purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments. Design/methodology/approach The approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism. Findings The experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets. Originality/value To take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.
Mobile Privacy: Scalable Ensemble Matching for User Identification Attacks

Mobile privacy is broadly concerning in the mobile big data era, as user mobility behaviors are privacy-sensitive and unique. User identification attacks consist of one of the most critical privacy concerns on mobile big data. In this paper, we study mobile privacy in terms of user identifiability from the perspective of privacy adversaries. User identification in two datasets from the same data source or two different data sources is generally formulated as a linear assignment problem (LAP), in which the cost matrix of users is generated by a single distance measure. However, user identification via one single distance measure may lead to a large portion of false matches, especially when only a few users coexist across these two datasets. In addition, the cubic computational complexity of LAP limits the scale of user identification analysis. In this paper, we propose a multi-feature ensemble matching framework to improve the user identification precision based on a majority voting rule, by integrating multiple distance measures. The computational complexity of the proposed ensemble matching algorithm is an order of magnitude less than that of the single-distance based approach, which results from solving an LAP on a highly sparse matrix rather than a dense matrix. Experiments demonstrate the superior performance of our proposed scalable ensemble matching framework with respect to matching precision as well as the vulnerability of mobile network subscribers' privacy.
Bat-Grey Wolf Optimizer and kernel mapping for automatic incremental clustering

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails, internet and web pages. Therefore, it becomes a complex task for arranging and browsing the required document. This paper proposes an approach for incremental clustering using the Bat-Grey Wolf Optimizer (BAGWO). The input documents are initially subjected to the pre-processing module to obtain useful keywords, and then the feature extraction is performed based on wordnet features. After feature extraction, feature selection is carried out using entropy function. Subsequently, the clustering is done using the proposed BAGWO algorithm. The BAGWO algorithm is designed by integrating the Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) for generating the different clusters of text documents. Hence, the clustering is determined using the BAGWO algorithm, yielding the group of clusters. On the other side, upon the arrival of a new document, the same steps of pre-processing and feature extraction are performed. Based on the features of the test document, the mapping is done between the features of the test document, and the clusters obtained by the proposed BAGWO approach. The mapping is performed using the kernel-based deep point distance and once the mapping terminated, the representatives are updated based on the fuzzy-based representative update. The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy, Jaccard coefficient, and rand coefficient with maximal values 0.948, 0.968, and 0.969, respectively.
Shuffle Scheduling for MapReduce Jobs Based on Periodic Network Status

MapReduce jobs need to shuffle a large amount of data over the network between mapper and reducer nodes. The shuffle time accounts for a big part of the total running time of the MapReduce jobs. Therefore, optimizing the makespan of shuffle phase can greatly improve the performance of MapReduce jobs. A large fraction of production jobs in data centers are recurring with predictable characteristics, and the recurring jobs split the network into periodic busy and idle time slots, which allows us to better schedule the shuffle data in order to reduce the makespan of shuffle phase with the future predictable network status available. In this paper, we formulate the shuffle scheduling problem with the aim to minimize the makespan of MapReduce shuffle phase by leveraging the predictable periodic network status. We then propose a simple yet effective network-aware shuffle scheduling algorithm (NAS) to reduce the number of idle time slots required to transfer the shuffle data so as to reduce the shuffle makespan. We also prove that the proposed algorithm NAS is a 3/2-approximation algorithm to the shuffle scheduling problem when all the future idle time slots have the same duration. We finally conduct experiments through simulations. Experimental results demonstrate the proposed algorithm can effectively reduce the makespan of MapReduce shuffle phase and increase network utilization.
Classical University Education in Volgograd Region: Origin and Development

Introduction. The article covers history waypoints of the corporate culture, tradition, emblems of Volgograd State University, its corporate ideology and the role of the University in the regional community. The author enlightens the activity and responsibility of the rector department and faculties in terms of the development of the University as region’s leader of science, education and culture. Methods and materials. The principles of historicism and objectivity were used as a methodological base of the research. The study is based on scientific papers on the role of university education in modern society and corporate cultural space in the university community. Analysis. The core activity and focused informational work of the University has allowed to form its positive image. Special attention is paid to the appearance of higher education institutions in the region and the establishment of Volgograd State University. The author studies the contribution of the University to the social-economic, public and cultural sphere of region’s life. Results. The main conclusion of the article is that nowadays classic university education leads to forming the social elite, increases the level of education and culture in the regional community. The program of transforming Volgograd State University into the University Centre of innovative, technological and social development of the region allows to reach success for not only the University, but for the whole region.
Binary image description using frequent itemsets

Abstract In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset. Firstly, the rows of the image are considered as transactions and the columns of the image are considered as items. Secondly, we considered rows as items and columns as transactions. Besides, we also apply our technique to color image; firstly we segment the image and each segmented region is considered as a binary image. The proposed method is tested on the MPEG7 database and compared with the moment’s method to show its efficiency.
Development of a method for compressing images on the basis of JPEG algorithm

The problem of image optimization, namely the reduction of the physical size of the image by minimizing image quality as little as possible, is considered. The object of research are methods for processing and compressing images. When analyzing the methods, one of the biggest problems was discovered, which consists in the fact that when solving the problem of image processing and compression, the studied methods allow to achieve the slightest loss in quality, but as a result, the compression ratio is significantly reduced. To overcome this problem, it was decided to develop a modification of the JPEG compression algorithm. The proposed modification consists in additional quantization of the spectrum after a discrete cosine transform, and then the resulting spectrum is fed to a Huffman encoder, which makes compression even more efficient. A method is obtained for solving the image optimization problem, which allows one to obtain an image with a smaller size and a large compression ratio while maintaining optimal quality. This is due to the fact that the proposed method has a number of features, as the original color image can have 24 bits per point, in particular, the ability to set the compression ratio. Thanks to this, it is possible to obtain a signal-to-noise ratio of 54.2 dB at a quality factor of zero. Compared with the well-known LZW algorithm, which is much better, as a result of which it allows to get a processed image with a much smaller physical size. The assessment of image quality, depending on the parameters of the task. It is shown that for problems of small and medium dimensions, the developed method provides minimal quality loss. The results of solving the problem for a specific example demonstrate the advantage of the developed method over existing ones. The results can be successfully applied to solve the problem of optimizing image size while maintaining maximum quality