Reviewerly/exHarmony huggingface dataset:
The Introduction of Popular Music in Music Education Teaching in Vocational Colleges

With the development of modern education cause, vocational colleges and universities in the process of completing professional knowledge teaching of music, begin to introduce some kinds of music which are popular in the modern society development into related music education teaching. The development of our country has a long history, and the accumulated cultural wealth has influenced people’s behavior and thoughts. Different types of music culture have their own strong characteristics, popular music represents the current trend of music culture development, gradually improve its status in vocational colleges.
A Projection-Based Augmented Reality System for Medical Applications

The aim of this paper was to present the development of an Augmented Reality (AR) system which uses a 2D video projector to project a 3D model of blood vessels, built by combining Computed Tomography (CT) slices of a human brain, onto a model of a human head. The difficulty in building this system is that the human head contains, not flat surfaces, but non-regular curved surfaces. Using a 2D projector to project a 3D model onto non-regular curved 3D surfaces would result in serious distortions of the projection if the image was not uncorrected first. This paper proposed a method of correcting the projection, not only based on the curvatures of the surfaces, but also on the viewing position of the observer. Experimental results of this system showed that an average positional deviation error of 2.065 mm could be achieved under various test conditions.
A Bibliometric Analysis of the Spanish Scientific Production on Teaching Oral Communication Between 2010 and 2020

This research paper aims to analyse the visibility, scientific production and educational impact of teaching spoken language between 2010 and 2020 by using bibliometric techniques. The Scopus and Web of Science databases have been used in order to carry out the study. We have selected articles published in journals included in the thematic category of Teaching Oral Communication in the decade 2010-2020. Out of 2677 articles, only 457 offer relevant data for our analysis, and of these, no more than 155 documents are written by Spanish authors. The lack of articles on this topic seems obvious, which consequently leads to the conclusion that there is a high need to conduct rigorous research on the subject and design educational models that allow teachers to implement efficient didactic methods and strategies when teaching oral communication in the current Spanish educational system.
Методические основы формирования матрицы социальных счетов для республики беларусь в разрезе видов экономической деятельности

The article examines some theoretical and practical aspects of the construction of a social accounting matrix (SAM) and presents a methodology of building a SAM for the Belarusian economy with the disaggregated production account by 31 types of economic activity.The methodology was used to build the SAM of the Republic of Belarus for 2016-2020 based on the data of the SNA and the inputoutput tables.The stability of the structure of the SAMs was analyzed for the specified period.
Prevention and Detection Mechanisms for Re-Entrancy Attack and King of Ether Throne Attack for Ethereum Smart Contracts

The second revolution in blockchain technology is smart contracts. Smart contracts are used in most of the blockchain applications like cryptocurrency, Health care, banking sectors, supply chain and IOT with different platforms like Fabric, Ethereum, Corda etc. In Ethereum blockchain, due to lack of inefficiency of the knowledge of technical developers and insecure programming languages for smart contracts, the attackers have exploited the smart contracts and the end users have lost millions of dollars like re-entrancy, king of ether throne attack, DoS, forcefully send ethers, multisig wallet, unexpected ether and poly network attack etc. In the year 2016, the attackers have exploited approximately $289 million US dollars with the help of re-entrancy vulnerability. The attackers have also attacked the smart contracts and broke the execution of that particular contracts through king of ether throne attack. In this paper, we propose a novel prevention and detection mechanisms for re-entrancy and king of ether throne attacks using time mechanisms and also implementing the same with proof of concepts for these vulnerabilities.
A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.
Developing and Validating an Instrument to Measure Students’ Perceptions of the use of ICTs and Educational Technologies in Times of the COVID-19 Pandemic

The main goals of the study were, on the one hand, to develop and validate an instrument to measure students' perceptions of the use of ICTs and educational technologies in times of the COVID-19 pandemic, and on the other hand, to know about the participants' attitudes towards the educational use of ICTs and other educational software. Some specific objectives of the research were (i) to identify the disposition of the students towards the use of ICTs, (ii) to characterize the Interaction with others through the use of ICTs, (iii) to identify the interaction of the students with LMS and ICTs, and to (iv) validate the instrument to measure students' perceptions about the use of ICTs and educational technologies, among others. An in-depth theoretical review allowed the authors of this paper to construct an instrument to measure students' perceptions about the use of ICTs and educational technologies in times of the COVID-19 pandemic. Concerning the statistical analysis, descriptive techniques were used to find out the perceptions of a group of Chilean university students about the online learning process through the use of ICTs, and then, the characteristics of the instrument itself were examined in-depth with exploratory factor analysis. Finally, in order to make the factor analysis more complex and to find proposals for improving the instruments for future research, the percentages of the variables grouped into factors were analyzed, as well as the reliability of each one of them. This research was financially supported by the German Academic Exchange Service (DAAD) as part of the project “Praxispartnerschaften zwischen Hochschulen und Unternehmen in Deutschland und in Entwicklungsländern ab 2017” (Project Nr. 57334905).
Building an integrated requirements engineering process based on Intelligent Systems and Semantic Reasoning on the basis of a systematic analysis of existing proposals

Requirements Engineering is one of the fundamental activities in the software development process and is oriented toward what should be produced. One of the development team&amp;rsquo;s most common problems is a lack of communication regarding an understanding of the discourse domain and how to integrate and process excessive information originating from different sources. This may lead to errors of omission and the consequent production of incomplete and inconsistent artifacts, which will have a direct effect on the quality of the software. The use of machine learning techniques helps the development team produce successful software on the basis of the acquisition of knowledge and human experience with which to understand the domain of the application. This paper, therefore, presents a proposal for a new methodological process oriented toward the construction of a vocabulary concerning the application domain. The authors propose to do this by employing Natural Language Processing (NLP), ontologies and heuristics that will lead to the production of a Lexicon that is common to analysts and customers, both of whom will understand the universe of discourse, thus mitigating problems of completeness. This objective has been achieved by carrying out a Systematic Literature Review of the artificial intelligence techniques employed in the requirements engineering process, which led to the discovery that 41.37% use NLP, while 55.71% apply ontologies such as semantic reasoners which help solve the problem of language ambiguity, the structures in specifications or the identification of key concepts with which to establish traceability links. However, the review also showed that the problems regarding the comprehension and completeness of requirements problems have yet to be resolved.
Crowd counting Using DRL-based segmentation and RL-based density estimation

People counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF_QNRF, UCF_CC_50, ShangaiTech_(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.
Trustworthy Blockchain Oracles for Smart Contracts

With the issuance of Bitcoin, blockchain technology has become more and more popular, and more and more well-known cryptocurrencies have been born. However the blockchain technology does not stop there, it can also be regarded as a very secure database. And Ethereum is a big pusher to promote the development of blockchain. It has developed the smart contract, which allows users to conduct secure transactions without a third party and avoid mutual mistrust between the two parties. With the development of blockchain, it is now possible to connect to the outside world through the API provided by the web service, which is called an Oracle. When a smart contract calls function, external data can be returned to the smart contract through the API. Such as obtaining the temperature of a certain country or obtaining the exchange rate of the current cryptocurrency. Now, oracle is mainly divided into two ways, through decentralization and centralization return data to the contract. However, the former method used by the oracle has little credibility for data collection, the latter is relatively slow in obtaining data return. This manuscript will update and optimize these parts, and hope to achieve a balance between the two. Finally, hope that more DAPP (decentralized applications) of the blockchain will be generated and the technology will be implemented. Chemical applications are more reliable.
Development of compact entangled photon-pair sources for satellites

Satellites are expected to play a significant role in the future of global quantum networks. Small satellites such as those following CubeSat standards may enable space-based quantum networks. CubeSat type satellites require less resources to develop and deploy and can be used to rapidly qualify technologies in orbit. The constraints of small satellites drive the development of compact quantum light sources, which will be useful even if the final satellite volume is large due to transmitter aperture requirements. In this article, we discuss the state-of-the-art in compact entangled photon-pair sources and suggest challenges that must be met by new source designs.
Analisis Perancangan Sistem Informasi Akuntansi Pencatatan Dan Pengelolaan Keuangan Pada Yayasan Panti Asuhan Harapan Karomah

Yayasan Panti Asuahan Hrapan Karomah is a social institution or non-profit institution that has the responsibility to provide social welfare services to the children of the residents of the orphanage. Therefore, the manager of the orphanage must be able to manage the orphanage properly, one of which is in terms of financial management. Currently, the Harapan Karamah orphanage is in the process of recording both revenues and expenses using a manual system. A manual system causes many errors of risks. Therefore, to minimize the risks that exist and also facilitate the financial management of the orphanage, a computerized information system is needed. In addition, it can also make it easier for the manager of the orphanage and also donors to monitor and evaluate the orphanage's finances which can be accessed quickly and at any time. This study aims to create a website-based financial administration information system. The software development method used is the Waterfall method which includes requirements analysis, system design, implementation, testing, and system maintenance. The development of this system uses the PHP programming language, MySQL database, and the CodeIgniter framework. The result of this research is an information system that can be used to process data and financial data reports. Based on the test results, the system being developed can run according to its function.
Some Remarks on Malicious and Negligent Data Breach Distribution Estimates

Digitization offers great opportunities as well as new challenges. Indeed, these opportunities entail increased cyber risks, both from deliberate cyberattacks and from incidents caused by inadvertent human error. Cyber risk must be mastered, and to this aim, its quantification is an urgent challenge. There is a lot of interest in this topic from the insurance community in order to price adequate coverage to their customers. A key first step is to investigate the frequency and severity of cyber incidents. On the grounds that data breaches seem to be the main cause of cyber incidents, the aim of this paper is to give further insights about the frequency and severity statistical distributions of malicious and negligent data breaches. For this purpose, we refer to a publicly available dataset: the Chronology of Data Breaches provided by the Privacy Rights Clearinghouse.
Retraction: A carboxymethyl cellulose modified magnetic bentonite composite for efficient enrichment of radionuclides

[This retracts the article DOI: 10.1039/C6RA10990J.].
Automatic Update Summarization by a Multiobjective Number-One-Selection Genetic Approach

Currently, the explosive growth of the information available on the Internet makes automatic text summarization systems increasingly important. A particularly relevant challenge is the update summarization task. Update summarization differs from traditional summarization in its dynamic nature. While traditional summarization is static, that is, the document collections about a specific topic remain unchanged, update summarization addresses dynamic document collections based on a specific topic. Therefore, update summarization consists of summarizing the new document collection under the assumption that the user has already read a previous summarization and only the new information is interesting. The multiobjective number-one-selection genetic algorithm (MONOGA) has been designed and implemented to address this problem. The proposed algorithm produces a summary that is relevant to the user's given query, and it also contains updates information. Experiments were conducted on Text Analysis Conference (TAC) datasets, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics were considered to assess the model performance. The results obtained by the proposed approach outperform those from the existing approaches in the scientific literature, obtaining average percentage improvements between 12.74% and 55.03% in the ROUGE scores.
Sub-word based unsupervised bilingual dictionary induction for Chinese-Uyghur

In this paper, we focus on the task of bilingual dictionary induction for the Chinese-Uyghur language pair. Usually, correlating long-distance linguistic information requires cross-linguistic information as supervision, which often requires parallel corpora to link in seed lexicons. And the parallel corpora are expensive. The low-resource Uyghur language text data are only available in a small amount, and the derivative morphological structure is vibrant and complex. In bilingual processing aligning most similar units and entity stems is the first step. So separating sentences into morpheme sequences is essential in the cross-lingual processing tasks. Uyghur words in text sentences consist of stems joined with several suffixes/prefixes. Rich and complex multiple affix forms exist in the text, forming many derivative words. This situation can easily lead to an increase in the repetition rate of intentional features in the text, which affects the efficiency of bilingual dictionary extraction. In this work, we actively explore the resource construction and granularity optimization of minority low-resource languages and learn cross-language word embeddings without the supervision of parallel data. A Chinese-Uyghur bilingual dictionary extraction method is proposed based on the neural network cross-language word embedding vector technology and the multilingual morphological analyzer. Experiments show that the way based on morpheme sequence significantly improved compared to the baseline model of the word sequence.
Adaptive Linear Unit for Accurate Binary Neural Networks

Binary Neural Networks (BNNs) have show huge advantages over full-precision floating-point neural networks in both computing efficiency and storage cost. However, there is still a considerable accuracy gap between BNNs and their floating-point counterparts. In order to close the gap, we propose Adaptive Linear Units (AdaLU) which can stabilize the training process of BNN and improve the accuracy of BNNs effectively. AdaLU introduces learnable offsets, slopes and saturation value to the traditional linear rectification function. The learnable offsets can enhance useful feature information, while learnable slopes are effective for stabilizing the training process, and the learnable saturation value introduces smoother loss landscapes, indicating these networks are more generalizable. Extensive experiments on the CIFAR-10 and ImageNet datasets have shown that the proposed AdaLU delivered superior performance improvements over current advanced training methods for BNNs, especially on ImageNet dataset, when Bi-Real Net was used as the benchmark model, the proposed scheme improved the accuracy by 4.6% over the baseline network.
A comprehensive blockchain technology survey: architecture, applications and challenges

Blockchain technology has evolved to solve the complexity and privacy issues associated with online digital content distribution in the last decade. Several studies have centred on different implementations of blockchain technology, and there is no comprehensive survey of the technology from both a technical and an application viewpoint. To close this gap, we conducted a detailed blockchain technology survey. This paper gives an in-depth look at blockchain technology. It discusses the blockchain fundamentals, taxonomy, characteristics, architecture of blockchain, and accessible consensus mechanisms used in the various blockchain systems. This paper also gives blockchain applications in multiple domains, including finance, medical, banking, etc. In addition, this paper presents an overview of the various problems that currently exist in blockchain technology and future research directions.
Blockchain Based Digital Forensics: A Fundamental Perspective

Blockchain technologies have come a long way as far as smart healthcare is concerned, and has been slowly entering other realms of modern technologies. Of late, smart forensics, or more appropriately, digital forensics, has come under the influence of blockchain technology. Being endowed with decentralization properties, it has become an excellent match as far as integrity and attributes towards the collection of evidence of digital forensics is concerned. As such, a paradigm shift in IoT has taken place with the emergence of the IoT forensic chain. Accordingly, this chapter discusses a fundamental overview of blockchain-based digital forensics. This will be followed by a discussion of special features such as digital forensics incident response, a chain of custody in the context of the IoT forensic chain. We further aim to include practical implementations, with future recommendations.
Conceptions of consent, family and jurisdiction in forensic genetic genealogical searches

Law enforcement officials in the United States announced in April of 2018 that they had identified a serial rapist and murderer known as the Golden State Killer (GSK) by linking crime scene DNA samples to the suspect using a recreational genetics database, thereby heralding a new era in forensic genetic investigation (FGG). The emergence of FGG affords a valuable platform from which to assess an incipient technolegal world – one in which cutting-edge technologies clash with well-established legal, social and political concepts. Forensic genealogy harnesses several distinct scientific advances, including the availability and affordability of inexpensive, large-scale testing of the genome; the commercialisation of recreational genetics by private companies and the development of open-platform sites to share genomic data. Hitched to one another by law enforcement, these technosciences pose a challenge to fundamental legal concepts that have long constrained law enforcement activity. Namely, they unsettle notions of consent, the spatial and relational limits on knowledge generation and conventional legal structures governing law enforcement access to information. Focusing primarily on the law and practice of forensic genetic genealogy in the United States, this chapter addresses each of these sites of conflict.