Reviewerly/exHarmony huggingface dataset:
RUSSIAN ASSOCIATION OF STATISTICIANS: FILLING THE GAPS IN THE EDUCATION CHAIN

Typically, training in Russia for professionals includes school, university, and postgraduate education. People make their choice regarding university or job after school, and they choose jobs after university. These are very sensitive matters. Help in making the right choice is a real asset. The Russian Association of Statisticians (RASt) is an independent, non-profit organisation that does not provide statistical education as a university and does not collect and process data as a statistical institution. But RASt helps students, universities, and producers of statistical data find each other. The paper describes the activities of RASt which organises the school competition in statistics called “Trend” to support students in choosing a profession and the kick-off competition “Career” for university students to help them get to know their employers. The organisers of the competition for school children usually face a number of problems related to the young age of participants and to limited funding. If we are talking about such a country as huge as Russia, the problems increase. To solve these problems, organisers use a combination of competition of presentations about original statistical researches provided by school teams in regions and an online quiz on statistical topics at the final stage. Technologically, the entire process is supported by ROSSTAT with its IT network. The organisers hope that the competition will make the profession of statistician more popular in Russia and attract more students to statistical programmes in universities.&#x0D; First published February 2020 at Statistics Education Research Journal Archives
Zero-skipping in CapsNet. Is it worth it?

Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i.e. multiplication by zero in CapsNet, can possibly save energy. We base our analysis on the number of multiplications by zero detected while training CapsNet on MNIST and Fashion- MNIST datasets.
A Consumer Recommendation System based on Big Data

With the continuous improvement of production technology and the improvement of people's living standards, mass-produced products can hardly meet people's growing material and cultural needs, and users' individual needs are becoming stronger.The development of personalized customization from Consumer to Business (C2B) is one of the important ways for manufacturing enterprises to transform and upgrade.However, at this stage, companies cannot personalize customization, and users have not introduced smart recommendations to assist in customization when they participate in the customization process.The existing research on a personalized intelligent recommendation for manufacturing enterprises is only for the optimization and adjustment of the algorithm itself, and does not effectively combine the characteristics of personalized customization step by step, and attributes and attribute customization content are independent and interrelated.To better guide users' product customization and decision-making, users can accurately describe their needs and improve customization efficiency.In the personalized customization of products, the idea of intelligent recommendation is introduced, and improvements are made based on the original item-based collaborative filtering recommendation algorithm.A step-by-step intelligent recommendation algorithm suitable for C2B personalized customization is proposed, and a car customization case is introduced.This paper introduces the internal mechanism and recommendation steps of the recommendation algorithm in detail and introduces an example to simulate the recommendation process of the algorithm.Experimental results and recommendation results show that the recommendation algorithm for C2B personalized customization in this paper is feasible and effective.
Energy Diminution Methods in Green Cloud Computing

Distributed computing center around the information figuring proficiency whereas green distributed computing is reasoning which depends on distributed computing design and concentrates on the vitality productivity of gadget and processing.Green Cloud Computing is an approach used to enhance the usage of figuring assets those are being utilized as part of the distributed computing system, for example, stockpiling, servers, its application, and benefits and decrease vitality utilization of these assets which enhances control proficiency.This is finished by different innovations like virtualization and virtual machines movement.This paper surveys the different systems purposed by the distinctive creators to make distributed computing more vitality proficient.The primary target of this paper is to think about and break down the idea of vitality proficient server farm engineering, asset allotment and streamlining.
Investigating the Appropriateness of Reading Passages of Grade Seven English Textbook: Tsehay Chora Primary School in Focus, Addis Ababa, Ethiopia

The purpose of this study was to assess the appropriateness of reading passages of Grade Seven English textbook suitability in developing students’ reading comprehension Descriptive research design with both qualitative and quantitative research methods were employed. In the study, 42 Grade Seven students randomly and four English language teachers with comprehensive sampling technique were included. The instruments used to collect data were document analysis, students’ questionnaire, and teachers’ interview. The data gathered through document analyses and students’ questionnaires were analyzed with frequency, percentages and mean; whereas, teachers’ interview data were analyzed through narration. The finding of study showed that, the reading passages of the current Grade Seven English textbook lack interestingness, authenticity and sufficient vocabulary. These deficiencies could hamper the appropriateness of reading passages so that the reading texts could not be adequate to develop the students’ reading skills. Hence, the reading passages should be revised to be appropriate for students’ success in reading.
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images

Deep learning has achieved great success in image analysis and decision making in radiology. However, a large amount of annotated imaging data is needed to construct well-performing deep learning models. A particular challenge in the context of breast cancer is the number of available cases that contain cancer, given the very low prevalence of the disease in the screening population. The question arises whether normal cases, which in the context of breast cancer screening are available in abundance, can be used to train a deep learning model that identifies locations that are abnormal. In this study, we propose to achieve this goal through the generative adversarial network (GAN)-based image completion. Our hypothesis is that if a generative network has a difficulty to correctly complete a part of an image at a certain location, then such a location is likely to represent an abnormality. We test this hypothesis using a dataset of 4348 patients with digital breast tomosynthesis (DBT) imaging from our institution. We trained our model on normal only images, to be able to fill in parts of images that were artificially removed. Then, using an independent test set, at different locations in the images, we measured how difficult it was for the network to reconstruct an artificially removed patch of the image. The difficulty was measured by mean squared error (MSE) between the original removed patch and the reconstructed patch. On average, the MSE was 2.11 times higher (with standard deviation equal to 1.01) at the locations containing expert-annotated cancerous lesions than that at the locations outside those abnormal locations. Our generative approach demonstrates a great potential for using this model to aid breast cancer detection.
Designing of Information System for Stock Management using the FAST (FRAMEWORK FOR THE APPLICATIONS) Method

The use of information technology system in managing data of various company activities, especially in the warehouse, is an important part for every company. From the observation that have been made, the information system for recording goods flow in PT Global Infotech Solution is still done manually. During this time, employees carry out the process of recording data of goods at the warehouse using paper, so that the risk of data loss is easily caused by tearing, wet or tucked with other data. The system development method that used is the FAST (Framework for Application System Thinking) Method. This study aims to design an information system for recording goods flow which is expected to make it easier for employees to record goods in the warehouse. Using a computerized system can shorten the time and protect all data items from the risk of theft. The result of this study is designing an information system for recording goods in the form of information systems using the Unified Modeling Language and User Interface. This item recording information system is expected to be able to change the process from a manual recording system to a computerized recording system, so that it can speed up every recording process and minimize the possibility of mistakes in entering the data of goods in the warehouse.
Bidirectional Feature Transfer for Cross-Domain Sentiment Analysis

With the evolution of user-based web content, people naturally and freely share their opinion in numerous domains. However, this would result in a massive cost to label training data for many domains and prevent us from taking advantage of the shared information across domains. As a result, cross-domain sentiment analysis is a challenging NLP task due to feature and polarity divergence. The main aim of this work is to automatically create a bidirectional thesaurus which could be used to transfer feature vectors of the source and target domains. This paper aims at designing an algorithm of feature transfer to select and transfer the informative and representative features between the source and target domains. Furthermore, several experiments were conducted in order to evaluate the proposed model, and the results were compared to similar known baseline methods.
LIBRARY IN THE INFORMATION SOCIETY:ONTOLOGICAL APPROACH

The formation of modern society as an information one is accompanied as a transformation of all its structures so as a rethinking of their role in the existence of society. Such a rethinking requires the introduction of new constructive approaches to the activities of institutions, is a system-forming factor in the informatization of public relations. It is the digitalization of various spheres of human existence, the increase in the amount of information that is produced by mankind, the need for its qualitative assessments, the need for its availability, which makes the library a special institution that can provide guidelines in the information space and form a special culture of the knowledge society.&#x0D; The aim of this research is to consider a modern approach to the modernization of library science, based on an ontological approach to the organization of knowledge bases, the reason for using which is the ability to provide prompt and high-quality satisfaction of requests and needs of library users based on local and remote information resources.&#x0D; The proposed research reveals the role of the library in the information society as a means of verifying information. The features of the initial stage of the transition of domestic libraries to the modernization path of development are characterized. External and internal factors that necessitate innovative changes in the industry are given. The innovations occurring in the implementation of the basic functions of libraries are considered: information, educational, cultural and educational, memorial, social and communicative.&#x0D; The features of the presentation of digital content in the framework of traditional library activities are considered, where the library is defined as a repository of structured various data with the possibility of their integration with other data sources and provides for the possibility of specifying its content by describing the subject area. An ontology of the content of the semantic library acts as a means of formalization. The usage of ontologies is connected with the impossibility of adequate automatic processing of natural texts by existing means, determines the search for new approaches and the introduction of innovative developments.
Developing A Compelling Vision for Winning the Cybersecurity Arms Race

In cybersecurity there is a continuous arms race between the attackers and the defenders. In this panel, we investigate three key questions regarding this arms race. First question is whether this arms race is winnable. Second, if the answer to the first question is in the affirmative, what steps we need to take to win this race. Third, if the answer to the first question is negative, what is the justification for this and what steps can we take to improve the state of affairs and increase the bar for the attackers significantly.
A morpheme sequence and convolutional neural network based Kazakh text classification

Word embedding techniques can map language units into a sequential vector space based on context. And it is a natural way to extract and predict out-of-vocabulary (OOV) from context information, word-vector based morphological analysis has provided a convenient way for low resource languages processing tasks. In this paper, we discuss Kazakh text classification experiment based on the m2asr morphological analyzer for small agglutinative languages. Morpheme segmentation and stem extraction from noisy data based on stem-vector similarity representation are experimented on Kazakh language. After preparing both word and morpheme-based training text corpora, we apply convolutional neural networks (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that morpheme-based approach outperforms word-based approach.
Synthetic media: The real trouble with deepfakes

Keeping on top of manipulated videos and images is no easy feat. Scientists trace the roots of their proliferation, dig up new techniques to counter digital forgeries and warn of the growing dangers ahead.
Knowledge Mining from Large Volume of Dataset using Fuzzy Association Rule

As we know that fuzzy association rules are used to convert crisp set elements in to fuzzy set elements like “height=long”. On other hand Association rules on crisp set are bounded with in a limit to transfer crisp set elements in to the binary values like “height = [5.5feet or above]” and it losses some information at boundaries because of its restricted nature. Today the variations of fuzzy association rule mining is most popular. As the crisp version of Apriori, fuzzy Apriori algorithms are quit inefficient for large volume of data sets. Hence it is required to bring an efficient and powerful FA rule mining for better performance over large volume of data sets. I f we compare the fuzzy Apriori with the proposed algorithm the proposed algorithm is almost 16% faster than the earlier one if both the algorithm compared together in case of very large data sets. The proposed algorithm also has excellent processing techniques to convert the non-fuzzy dataset into fuzzy dataset.
Training of Physical Education Teachers in the Context of Digital Education Implementation

In the context of digital education implementation, addressing the problem pertaining to the training of highly qualified physical education (PE) teachers in the Republic of Sakha (Yakutia) is essential for the innovative development of the region, along with that of its scientific and educational environments. To this end, specialists should improve their professional skills in order to develop and effectively use advanced educational technologies that take into account the specific conditions of work, study, life as well as the national and cultural traditions of the region. The current article highlights that the implementation of digital education requires complex transformations in the systems of both pedagogical and physical education, which are influenced by progressive digital and information technologies. However, its practical implementation requires a change in the goals, along with the organizational forms and technologies of educational activities, based on information and communication technology (ICT) and network technologies, in addition to the development of productive strategies to integrate innovations into the traditional educational process. Thus, the aim of this study is to substantiate the current trends regarding the development of digital education and training of PE teachers in the Republic of Sakha (Yakutia), by means of the analyses of foreign and domestic researchers’ experiences. Further, its theoretical aspect delves into the analysis of the research conducted on digital education and, primarily, the ways in which it can be implemented in the educational process of the Institute of Sport and Physical Education (ISPE), Ammosov North-Eastern Federal University (NEFU). On the other hand, the practical section of this study presents a comparison between the knowledge of part-time and full-time students studying at the ISPE, NEFU, concerning the framework of digital educational materials’ implementation for the training of future PE teachers. The results showed the considerable improvement of the students’ knowledge on the importance of ICT and digital resources, in addition to an increased level of digital competence regarding professional activity.
Petrology of Eocene Volcanic Rocks in Dastgerdo to Molla Ahmad Area (East of Isfahan); Middle Part of the Urumieh- Dokhtar Magmatic Arc (UDMA)

Petrology of Eocene Volcanic Rocks in Dastgerdo to Molla Ahmad Area (East of Isfahan); Middle Part of the Urumieh- Dokhtar Magmatic Arc (UDMA)
Cooperate Delegation Computation for Rational Party Using Zero-Determinant Strategy

Delegating calculation is an important approach to solve the correctness and reliability of task subcontracting and delegating calculation results in cloud computing environment. However, the dynamic, complexity and openness of cloud computing bring unprecedented risks to the security and reliability of computing tasks. To solve the problem the new approach is presented. Firstly, the game theory is introduced into the delegating calculation, and the single-client and multi-server rational delegating calculation game model is given. Secondly, the Zero-determinant Strategy (ZD strategy) scheme based on delegating calculation is constructed through implementing the single-client and single-server rational delegating calculation protocol and ZD strategy, and the conditions of the existence of Nash equilibrium of ZD strategy is analyzed. Then, through the iterative implementation of the ZD strategy, the participants will cooperate with each other actively. Finally, the performance analysis results show that the entrusting party can regulate the betrayers in the computing party through ZD strategy to ensure the interests of honest people in cloud computing.
A Novel automatic voice recognition system based on text-independent in a noisy environment

Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.
Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

Fine-grained spectrum management is promising in addressing the contradiction between the access requirements for massive radio systems and the shortage of spectrum resources. It is difficult to identify the radio system and securely transmit its spectrum identity information due to the complexity, openness, and variability of the electromagnetic environment, which has become a major challenge for the fine-grained spectrum management. In this paper, we consider a practical and proactive spectrum monitoring scenario, where the spectrum monitoring node monitors the communication signal sent by a source node to a destination node for the fine-grained spectrum management. A malicious node eavesdrops on the transmission for accessing and deceiving them. We propose a secure transmission method of spectrum watermark for the fine-grained spectrum management. The spectrum watermark for identifying the radio system is hidden in the normal communication signal via adjusting the embedding parameters, and has no effect on the required transmission performance. We investigate the transmission performance of the spectrum watermark in two typical use cases. In first case, the transmission rate of the communication signal is given and fixed. In another case, the transmission rate of the communication signal can be adaptively adjusted according to the channel quality. The analysis and simulation results show that the proposed method achieves the optimal and secure transmission of the spectrum watermark by adjusting the embedding parameters of the spectrum watermark while ensuring the required and optimal transmission performance of the communication signal.
PREDICTING CRIMES AGAINST WOMEN’S AND CRIMINAL PERFORMANCE IN TAMILNADU STATE USING CLUSTERING AND CLASSIFICATION ALGORITHM

Crime investigation besides anticipation is an efficient method aimed at classifying then evaluating forms besides tendencies popular criminality. The main impartial of the research is to organize clustered crime based on manifestation regularity for the duration of different years. With the accumulative initiation of online structures, criminal records predictors container assistance the Commandment administration majors to quickness up the progression of cracking criminalities. Consuming the perception of data mining we can quotation previously unknown, useful evidence from a formless data. Statistics for together Criminalities and Offenders were composed after permitted data set law enforcement agency branches from the Internet, to generate and assessment the deliberate structure, besides formerly these statistics remained pre-processed to become unsoiled and particular records consuming altered pre-processing methods. The pre-processed records stayed recycled to creation available altered criminality and immoral groups, associations besides relationships among different characteristics were discovered, and lastly the construction between different qualities counting crime type and criminal age, job, history and others has initiate. Rapid Minor tool has applied to analyze the given set of data.