Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Solfeggio and Ear Training Learning Assistant System Based on Computer Cat Technology

Abstract With the rapid development of Internet, many new technologies emerge in endlessly. Digital technology is one of them. Now digital technology is very popular in many fields. Now many music scholars are trying to apply this technology to music education. This kind of auxiliary teaching method is more and more popular, and is a kind of development trend at present. Solfeggio is one of the important courses in higher music teaching, and it is also a very basic course in music teaching. Digitizing the teaching of Solfeggio and ear training course is also a topic of research for many music majors in Colleges and universities. This paper discusses and analyzes the effect of CAT technology on Solfeggio and ear training teaching. This paper analyzes and compares the effect of CAT technology in Solfeggio and ear training learning aid system and the difference between the original traditional teaching through Two Conservatories of music. The results show that in the process of Solfeggio and ear training, more than 60% of the students are satisfied with the cat technology learning aid system, while they are satisfied with the traditional learning aid system The proportion of students should not exceed 50%.
Mr Paul Aharon, Mr Allan Chivas and John Chappell

Research School of Earth Sciences, Environmental Geochemistry Group - Allan Chivas, John Chappell, Paul Aharon, Dr. Tom Torgersen, Dr. E. Lilley, Mr. H. Tammemagi
Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

Abstract The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device’s behaviour at a given time interval.The practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.
Does BERT Look at Sentiment Lexicon?

The main approaches to sentiment analysis are rule-based methods and machine learning, in particular, deep neural network models with the Transformer architecture, including BERT. The performance of neural network models in the tasks of sentiment analysis is superior to the performance of rule-based methods. The reasons for this situation remain unclear due to the poor interpretability of deep neural network models. One of the main keys to understanding the fundamental differences between the two approaches is the analysis of how sentiment lexicon is taken into account in neural network models. To this end, we study the attention weights matrices of the Russian-language RuBERT model. We fine-tune RuBERT on sentiment text corpora and compare the distributions of attention weights for sentiment and neutral lexicons. It turns out that, on average, 3/4 of the heads of various model variants statistically pay more attention to the sentiment lexicon compared to the neutral one.
Native and foreign approaches to teaching coding at school

The article describes the experience of teaching coding in the curricula of schools in Ireland and the province of Ontario (Canada). The province of Ontario is a successful participant in the International Computer and Informational Literacy Study (ICILS); Ireland attaches particular importance to the possibility of developing 21st century skills in coding. The goals, objectives, structure of the content of the corresponding training courses and the planned learning outcomes are presented. The Canadian, Irish and Russian approaches to teaching coding at school are compared according to the following criteria: compulsory mastery, thematic blocks, software, subject results, skills of the XXI century, course duration. The advantages of the Russian informatics course and the possibility of using foreign experience in the construction of elective courses and/or extracurricular programs are described.
Study on teaching strategies for oral English grammar in sports schools based on information technology

The extensive application of Internet technology has promoted the profound transformation of society. The rapid development of artificial intelligence technology has made global affairs appear informatized, networked, and intelligent. The industry's demand for professionals with artificial intelligence background is increasing. Vocational education Development opportunities and challenges coexist. Information majors, as the most closely related to artificial intelligence technology, should take effective measures to deal with this change. The training of professional talents should be equipped with equipment from passive adaptation to active innovation as soon as possible, further optimizing the structure of professional knowledge and enhancing the content of professional talent training. As a result, the application of English informatization is also increasingly important. Experiments show that the method proposed in this study is feasible.
Cloud Learning Platform Technology of Online Preschool Education Based on Big Data

At present, information technology and Internet technology are developing rapidly, and more and more schools and teachers are adopting network information teaching methods to carry out teaching activities. According to incomplete statistics, people spend at least 15 years in school for education in their lifetime, which does not include the graduate level. To lay the foundation for this long period of school education is the pre-school education stage. The early childhood period is the most important stage of the intellectual development of life, and it is also the foundation of education. This paper uses Web data mining technology to provide personalized services to the online preschool education and learning platform, to study the interests and learning habits of preschool children, to choose more needed resources for students, and to provide better services. The survey report shows that more than 50% of students use the online learning platform for more than one hour a day, and even 5% of students use the cloud platform to study for more than 3 hours a day. The survey report also shows that 92% of students’ parents are satisfied with the online learning platform, and 63% of them are very satisfied. As the technology becomes more and more perfect, the quality of teaching will gradually improve in the future.
Digital Color Image Contrast Enhancement Method Based on Luminance Weight Adjustment

When collecting images, owing to the influence of shooting equipment, shooting environment, and other factors, often low-illumination images with insufficient exposure are obtained. For low-illumination images, it is necessary to improve the contrast. In this paper, a digital color image contrast enhancement method based on luminance weight adjustment is proposed. This method improves the contrast of the image and maintains the detail and nature of the image. In the proposed method, the illumination of the histogram equalization image and the adaptive gamma correction with weighted distribution image are adjusted by the luminance weight of w1 to obtain a detailed image of the bright areas. Thereafter, the suppressed multi-scale retinex (MSR) is used to process the input image and obtain a detailed image of the dark areas. Finally, the luminance weight w2 is used to adjust the illumination component of the detailed images of the bright and dark areas, respectively, to obtain the output image. The experimental results show that the proposed method can enhance the details of the input image and avoid excessive enhancement of contrast, which maintains the naturalness of the input image well. Furthermore, we used the discrete entropy and lightness order error function to perform a numerical evaluation to verify the effectiveness of the proposed method.
DrumCorr program for selecting volcanic earthquake multiplets based on cross-correlation analysis

Abstract The DrumCorr program based on cross-correlation detection has been developed to identify multiplets of the volcanic earthquakes. The program is implemented in Python 3 and reads ASCII and MiniSEED seismic data formats. The article presents the algorithm of the program, describing the cross-correlation detector and an example of subsequent processing of seismic data. The program was applied to volcanic earthquakes of the «drumbeats» seismic regime and allowed to identify earthquake multiplets characterized by various wave forms. The article presents the algorithm of the program, describing the cross-correlation detector, the features of the weak volcanic earthquakes selection by the STA/LTA method. And the primary analysis of the values of the correlation coefficients with the calculation of their standard errors depending on different signal-to-noise ratios.
Meaningful Translation and Transliteration for Marathi Language

Meaningful translation and transliteration is NP problem in case of languages like Marathi language as there are so many word disambiguation and multiple use and meaning of single word in different context is available. That is why identifying correct informational need and translating text into meaningful information is a tedious and error prone task. Google translate works on machine neuron network and WorldNet is an online reference system works on psycholinguistic theory of human memory. Both approaches are promising tools for language translation. Complete translation of Marathi text to English or English to Marathi also having problem of more complicated meaningless or tedious translation. Proposed algorithm is taking into consideration meaningful translation or transliteration as per user’s informational need. This novel approach consider machine neuron network for meaningful formation of translated sentence and morphological structure for correct translation of word based on ontological analysis of word.
Design and Implementation of Storage Benchmark Kit

The Storage Benchmark Kit (SBK) is introduced in this paper. The SBK is a software framework, designed and implemented to conduct the performance benchmarking of any storage system of any type of data/payload. It delivers the throughput and latency values for every specific interval for live performance analytics. The SBK supports a variety of storage systems such as local mounted and distributed file systems, local/single node (system) database and distributed database systems, any distributed storage system such as distributed messaging/streaming platforms, object storage systems, and distributed key-value storage systems. In this paper, the SBK is demonstrated with the performance benchmarking of XFS File system, Hadoop Distributed File System (HDFS), and Kafka streaming storage systems.
Development of microelectronics in the USA and Russia and Society

Progress of the semiconductor and integrated microelectronics contributed to the development of electronic computing systems and other types of equipment. The border of the 20th and 21st centuries became the birth time of nanoelectronics with an even greater degree of integration of elements. The USA and Russia’ governments and scientific communities paid big attention to the studies and developments in the field of nanotechnology. At the same time a part of society met information about works at the new technology with certain skepticism. Some authors expressed concerns that the development of nanotechnology unlikely will help the population of the developing countries, but will bring dividends to rich and prosperous. In Russia the main role in the implementation of programs drawn up in 2000-s belonged to the newly created corporations and innovative centers, and that arose questions among experts in the Academy of Sciences and Universities. Besides, the expected improvement of reciprocal access to advanced technologies of other countries, associated with accession Russia to the WTO (2014), did not occur because of some measures of world politics.
A Study on the Quantification of Market-Government Response for Import Interruption Risk of Base Metal in Korea

In Korea, base metals such as lead, zinc, copper, tin, nickel, and aluminum have a polarized supply and demand structure.Despite the presence of world-class producers of lead, zinc, and copper, and their production is insufficient.And there are no domestic producers of tin, nickel, and aluminum, Thus, most of the domestic demand is dependent on imports.Therefore, it is necessary to prepare for the risk of supply interruption, such as the disruption of the import of base metals or interruption of domestic production.In this study, we estimated the quantity required to respond to the risk of import disruption, the quantity to which the market can respond, and the quantity to which the government needs to respond for six base metals (copper, lead, zinc, aluminum, nickel, and tin).
How Do Agile Teams Manage Impediments?

Context: Impediments are blockers that prevent teams from working efficiently and achieving their goals. They are central to the continuous improvement of agile teams. However, common agile methods like Scrum only provide limited guidance on how to handle impediments. Hence, agile teams develop individual ways of impediment management and would benefit from understanding overall aspects that need to be considered as well as from insights into the approaches of other practitioners. Objective: This study seeks to solidify and enhance the understanding of how agile teams identify, document, and resolve impediments in practice. Method: Based on an earlier interview study, an online survey was conducted that collected the experiences of 26 participants from the agile community. Results: The results provide a quantitative overview of the different ways teams identify, document, and finally track/resolve impediments. Based on this, the underlying impediment management process has been enriched and additional insights were gathered. Conclusions: The process and survey results are expected to aid practitioners in defining their own approaches for handling impediments and provide an overview of the various options found in practice.
TimeLex: A Suite of Tools for Processing Temporal Information in Legal Texts

In this paper we present a suite of tools named TimeLex, that includes different systems able to process temporal information from legal texts. The first tool, called lawORdate, helps preprocessing legal references in texts in Spanish that can be misleading when trying to find dates in texts. The second one, Añotador, is a temporal tagger (this is, a tool that finds temporal expressions, such as dates or durations) that identifies temporal expressions in texts and provides a standard value for each of them. Finally, a third tool, called WhenTheFact, extracts relevant events from judgments, allowing a full processing of the temporal dimension of this kind of texts, and being a first step towards the complete temporal information processing in the legal domain.
The Impacts of Blockchain in Elections

There is a greater emphasis on improving voter participation in elections, and one way to improve voter participation is to implement electronic or online voting. However, many voters and voting administrators are still skeptical because of security concerns and a lack of trust in these systems. Traditional online voting represents the older version of internet-based technology as well as the use of paper ballots. Consequently, the use of the blockchain seems like a more suitable technical solution to ensure online voting is secure and to enable trust by the voters and administrators of voting. The latter would lead to increased voter participation in elections. This chapter per the author attempts to answer the following research questions: What is the impact of the blockchain on the political processes especially when constituents vote? What is the essence of online voting via the blockchain and what are its benefits? What are the vulnerabilities when applying the blockchain when used in online voting?
Enhancing the Time Performance of Encrypting and Decrypting Large Tabular Data

In the field of data analysis, encrypting and decrypting datasets must keep the information confidential. Currently, encrypting sizable tabular datasets is time-consuming. This study proposes a solution that helps encrypt extensive tabular data in lesser time than that required in conventional methods while preserving data analysis information. We use the feature by which a large dataset can be split into many files in hdf5 format and choose an encrypted algorithm to solve it. The study contributed to information technology knowledge management. We introduce a solution for small-scale companies to encrypt their extensive tabular data economically. The experimental results on three large datasets showed that our solution has a processing time between 1.2–5 times faster than the conventional processing time under some specific situations. The research results assist companies or individuals with a limited financial capacity to deploy data security and analysis at a low cost with time efficiency. The study opens several research opportunities in protecting large datasets and analyzing them in less time.
Bit Reduction based Compression Algorithm for DNA Sequences

Deoxyribonucleic acid called DNA is the smallest fundamental unit that bears the genetic instructions of a living organism. It is used in the up growth and functioning of all known living organisms. Current DNA sequencing equipment creates extensive heaps of genomic data. The Nucleotide databases like GenBank, size getting 2 to 3 times larger annually. The increase in genomic data outstrips the increase in storage capacity. Massive amount of genomic data needs an effectual depository, quick transposal and preferable performance. To reduce storage of abundant data and data storage expense, compression algorithms were used. Typical compression approaches lose status while compressing these sequences. However, novel compression algorithms have been introduced for better compression ratio. The performance is correlated in terms of compression ratio; ratio of the capacity of compressed file and compression/decompression time; time taken to compress/decompress the sequence. In the proposed work, the input DNA sequence is compressed by reconstructing the sequence into varied formats. Here the input DNA sequence is subjected to bit reduction. The binary output is converted to hexadecimal format followed by encoding. Thus, the compression ratio of the biological sequence is improved.
Privacy-Preserving Genotype Imputation in a Trusted Execution Environment.

Genotype imputation is an essential tool in genomics research, whereby missing genotypes are inferred using reference genomes to enhance downstream analyses. Recently, public imputation servers have allowed researchers to leverage large-scale genomic data resources for imputation. However, privacy concerns about uploading one's genetic data to a server limit the utility of these services. We introduce a secure hardware-based solution for privacy-preserving genotype imputation, which keeps the input genomes private by processing them within Intel SGX's trusted execution environment. Our solution features SMac, an efficient and secure imputation algorithm designed for Intel SGX, which employs a state-of-the-art imputation strategy also utilized by existing imputation servers. SMac achieves imputation accuracy equivalent to existing tools and provides protection against known side-channel attacks on SGX while maintaining scalability. We also show the necessity of our enhanced security by identifying vulnerabilities in existing imputation software. Our work represents a step toward privacy-preserving genomic analysis services.
Word-level Text Generation from Language Models

This research constructs and evaluates text generation models created from three different language models, n-gram, a Continuous Bag of Words (CBOW) and gated recurrent unit (GRU), using two training corpora, Berkeley Restaurant (Berkeley) and Alice's Adventures in Wonderland (Alice), and evaluated using two evaluation metrics; perplexity measure and count of grammar errors. The mean perplexities of all three models are comparable for each corpus, the N-gram model produces slightly lower values of perplexity. As for the number of grammatical errors in the Alice corpus, all three models show a slightly higher number of errors than the original corpus. In the Berkeley corpus, the n-gram model had the lowest number of errors, even lower than the original corpus, but the CBOW model had the highest number of errors and the GRU model had the highest number of errors.
Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming

In this paper, we explore model-based approach to training robust and interpretable binarized regression models for multiclass classification tasks using Mixed-Integer Programming (MIP). Our MIP model balances the optimization of prediction margin and model size by using a weighted objective that: minimizes the total margin of incorrectly classified training instances, maximizes the total margin of correctly classified training instances, and maximizes the overall model regularization. We conduct two sets of experiments to test the classification accuracy of our MIP model over standard and corrupted versions of multiple classification datasets, respectively. In the first set of experiments, we show that our MIP model outperforms an equivalent Pseudo-Boolean Optimization (PBO) model and achieves competitive results to Logistic Regression (LR) and Gradient Descent (GD) in terms of classification accuracy over the standard datasets. In the second set of experiments, we show that our MIP model outperforms the other models (i.e., GD and LR) in terms of classification accuracy over majority of the corrupted datasets. Finally, we visually demonstrate the interpretability of our MIP model in terms of its learned parameters over the MNIST dataset. Overall, we show the effectiveness of training robust and interpretable binarized regression models using MIP.
Does Identity Mapping Really Help in ResNet?

ResNet proposed residual learning using skip connections that perform identity mapping. However, as shown in the study [1], identity mappings for skip connections may not be the optimal or at least not the only way to get a good performance in ResNet. In this paper, we explore various types of skip connections, including shortcuts with or without identity mapping, to see their effects on the performance of ResNet and provide a comparison of the various methods. Our extensive experiments suggest that the use of identity shortcuts is the most appropriate than other methods in terms of model simplicity and performance.