Reviewerly/exHarmony huggingface dataset:
Microdisplacement Measurement Based on F-P Etalon: Processing Method and Experiments

Herein, a processing method is proposed for accurate microdisplacement measurements from a 2D Fabry–Perot (F-P) fringe pattern. The core of the processing algorithm uses the F-P interference imaging concentric ring pattern to accurately calculate the centre coordinates of the concentric ring. The influencing factors of measurement were analysed, and the basic idea of data processing was provided. In particular, the coordinate rotation by the 45-degree method (CR) was improved; consequently, the virtual pixel interval was reduced by half, and the calculation accuracy of the circle centre coordinate was improved. Experiments were conducted to analyse the influence of the subdivision and circle fitting methods. The results show that the proposed secondary coordinate rotation (SCR) by 45 degrees method can obtain higher accuracy of the centre coordinate than the CR method, and that the multichord averaging method (MCAM) is more suitable for calculation of the centre coordinate than the circular regression method (CRM). Displacement measurement experiments were performed. The results show that the standard experimental deviation of the centre of the circle is approximately 0.009 µm, and the extended uncertainty of the displacement measurement in the range of 5 mm is approximately 0.03 μm. The data processing method studied in this study can be widely used in the field of F-P interferometry.
INFORMATIONAL AND PEDAGOGICAL ASPECTS OF SOCIAL SOFTWARE IN LEARNING PROCESS OF HIGHER SCHOOL

The research is deal with the analysis of current stage of development and popularization of information and communication technologies and interactive means of learning communication in order to organize the common work in various spheres of society. The main attention is paid to the field of education services of higher education institutions in the context of rapid spreading of social software. It made it possible to join people together in specialized groups with common interests who even may be unacquainted in real life and have no previous communication experience. The essence of the newest social software system is revealed which consists of growth and maintaining social network interaction and building virtual communities with the high level of self-organizing. The main result of the research has become the explanation social software role and functions in the context of: interaction between individuals and groups in questions of web-publications and network communication as a type of computer-mediated communication; social feedback based on network ethics and virtual presence effect; forming and buildingout of social networks. An important element of social software, namely blogs, has considered. Today the blogosphere is one of the most striking web phenomena in terms of speed growing of blogs number, increasing link density within blogosphere and rapid growth of available content amount. Emergence and development of the latest social phenomena such as, for example, Facebook, Quorra, have given new impulse to further blogosphere progress, its modification and adaptation to current users needs exactly in education field. The newest tool is wiki which can use in learning process of higher school. Their advantage is fast access to online work results that allows to position wikisystem as a platform for collaboration at the level of students, study groups, universities etc. and use it as a tool of planning learning process in higher school. Key words: web, social software, blogs, wiki, networking, education services.
Application of Stochastic Dominance in Hedging Decision during COVID-19 Pneumonia Emergency Events

With the rise of virtual currencies, Bitcoin has gradually become one of the safe-haven tools in the financial market. During situations of worldwide outbreaks of an infectious disease, investors pay special attention to asset allocation. Therefore, this study discusses the outbreak of COVID-19 in China, which has affected financial markets and has led investors to avoid risks through investing in traditional financial products or Bitcoin. We found that during the time of the COVID-19 pneumonia, Bitcoin and gold futures were used for hedging transactions in the face of unstable Chinese market conditions and under the pursuit of investors' maximization of return on in-vestment. Furthermore, there was also no difference between hedging through Bitcoin or gold futures; however, investors had a preference to invest in gold futures for hedging under the as-sumption that an investor was absolutely risk averse.
The impact of some macroeconomic indicators on exchange rate fluctuations in Sudan in the period (1990- 2016): أثر بعض مؤشرات الاقتصاد الكلي على تقلبات أسعار الصرف في السودان في الفترة (1990- 2016)

The study aimed to identify the determinants and economic variables that affect the exchange rate in Sudan during the period (1990- 2016). The study used the descriptive analytical approach in data collection and analysis as well as the use of econometric methods in the construction of economic models and analysis of time series regression models to verify the existence of a long- term integrative relationship between independent variables and dependent variable. The results of the study showed a positive correlation between GDP, degree of economic openness, inflation and exchange rate during the study years, and an inverse relationship between money supply and exchange rate. The study recommended the need to move away from administrative decisions in determining the exchange rate, while achieving greater flexibility in the exchange rate, and increased interest in bank financing of projects that lead to increase productivity and improve GDP and thus improve the exchange rate by encouraging domestic exports.
A Survey on Efficient Parallelization of Blockchain-Based Smart Contracts

The main problem faced by smart contract platforms is the amount of time and computational power required to reach consensus. In a classical blockchain model, each operation is in fact performed by each node, both to update the status and to validate the results of the calculations performed by others. In this short survey we sketch some state-of-the-art approaches to obtain an efficient and scalable computation of smart contracts. Particular emphasis is given to sharding, a promising method that allows parallelization and therefore a more efficient management of the computational resources of the network.
Fluffy Character Based Information Trustworthiness Reviewing for Dependable Cloud Capacity Frameworks

The nature of the information, a security issue in a secure area, has received much consideration. Conference evaluation information enables the notary to humbly assess the validity of retransmitted information without downloading the data. A major research challenge related to existing knowledge analysis frameworks is a multi-faceted environment in critical management. We try to fix the confusing key in mind the board’s challenge in looking at the reliability of cloud information by presenting character-based reviews, first in that way, with all our understanding. Specifically, we present a critique of personality-based information that explores details, in which a client character can be seen as a multi-faceted feature. We formalize the framework model and the security model for this new platform. At the same time we present a strong development of personality based on exploring the assembly using biometric as a disruptive character. The new agreement provides an asset for the firmness of errors, in particular, it combines a secret key for one character that can be used to assess the validity of a response made by another character, if and only if that character is close enough. Finally, we have created a model for the use of assembly that reflects the general idea of a proposal.
Lightweight Architecture for COLM Authenticated Ciphers and Implementation on FPGA and ASIC

Authenticated encryption schemes provide both confidentiality and integrity services, simultaneously. The CAESAR competition is being held with the aim of designing this cipher. An important criterion for selecting the final portfolio, besides security, is the hardware performance of the candidate in the environments with limited resource. In this paper, for the first time for COLM authenticated ciphers from the final round of the CAESAR, an 8-bit lightweight architecture have been presented, which is compatible with API v2. Since COLM scheme uses AES cipher as a primitive, lightweight architecture of Atomic-AES has been selected and adopted according to the API rules. Furthermore, to reduce the area in the hardware implementation, several techniques are used, including implementing one AES core in the datapath, sharing of registers and implementation doubling on the GF (2128) with 8-bit architecture for constructing the higher-order multipliers. Proposed architecture of COLM is implemented on ASIC and FPGA platforms. This architecture is similar in both platforms, but different technology mapping optimization techniques are used for each platform. Comparing the results with 128-bit implementations shows that the area on FPGA and ASIC is reduced by 62% and 74%, respectively. Also, the customized API v2 for 8-bit data width reduced the API area by 8% and 6% on the FPGA and ASIC platforms, respectively.
Extrapolation for Positioning and Navigation Focused Content - Based Recommendation Mathematical Framework

The exponential development of the global website and the advent of electronic commerce have made the processing of information more than powerful analyzes in order to acquire the most useful results. It can be a challenging and time consuming method to search information on such large websites. Recommended programs can allow users to access knowledge by giving custom advice to them. It helps to make decisions without adequate personal knowledge. In this article we increase the scalability, sparsity and precision of the device suggested by solving the issue of sparseness and cold start. The correlations between items are usually determined by means of techniques such as the similarity between items and cosines. The 'Coldstart' problem cannot be solved with these techniques by using a statistical approximation principle that allows to improve improved rating estimating methods The dataset is the 'Film Lens' dataset. Mean absolute error and error ratio are the metrics used (ER). Some students at our college checked the method and it was found that the issue of 'Cold Start' and 'Sparsity' was successfully solved.
A Better Prediction for Higher Education Performance using the Decision Tree

Data mining is the application of specific algorithms forextracting patterns from data and KDD is the automated orconvenient extraction of patterns representing knowledgeimplicitly stored or captured in large databases, datawarehouses, the Web, other massive informationrepositories or data streams. Data mining can be used fordecision making in educational system. But educationalinstitution does not use any knowledge discovery processapproach on these data; this knowledge can be used toincrease the quality of education. The problem washappening in the educational management system, but tomake education system more flexible and discoverknowledge from it huge data, we will use data miningtechniques to solve problem.
Penggunaan Gaya Bahasa Dalam Teks Anekdot Pada Siswa Kelas X SMK Negeri 1 Kalianda Tahun Pelajaran 2018/2019

Bringing the topic of Language Style on Anecdotal Texts in Class X Students SMK Negeri 1 Kalianda 2018/2019, the problems raised based on field facts show that students do not yet have an in-depth understanding to use language styles in anecdotal learning contained in the curriculum. The purpose of thesis research is to find out how students understand about the style of language in anecdotal texts. Language style is a way for someone to describe a student's story. Data is obtained from giving assignments to students to write anecdotal texts and how they understand students writing anecdotal texts in a language style. This study used descriptive qualitative method. This data analysis uses theories from books written by Keraf, Ratna and Nurgiyantoro. The results of this study indicate that only a portion of all students are able to write language styles in anecdotal texts and students are only able to write one style of language in one text.
Reserving the clear ALPN Protocol ID

HTTP Alternative Services (Alt-Svc) are identified by a tuple ofApplication-Protocol Layer Negotiation (ALPN) protocol identifier, ahost and a port. The wire format for Alt-Svc is defined in ABNF andencodes this tuple or the keyword clear, which has a specialmeaning. This memo reserves the ALPN protocol identifier clear toreduce the chances of accidental aliasing with the clear keyword.
PERANCANGAN SISTEM INFORMASI PEMASARANDI UMKM FAS BOUTIQUE

FAS Boutique is one of the MSMEs that is engaged in the manufacture and sale and purchase of clothing. Currently, the sales or marketing system used is still using conventional systems, so it is necessary to develop it so that it uses an online information system. It is hoped that by using an online marketing information system, the marketing reach of Fas Boutique will be wider and the transaction process can be recorded properly. The system is designed using UML and developed with agile methods. as for some of the main features of the system are registration, marketing, sales and reports.
A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network

Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type.Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), denoted as RNN-LSTM-LSTM.This model includes a basic RNN, two Long Short-Term Memory (LSTM) networks and two Fully Connected layers.The experimental results show that, compared with the traditional Holt-Winters, Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) neural network prediction model, the mean square error (MSE) of the proposed RNN-LSTM-LSTM model are reduced by 11.82% and 20.34% on the order storage and data migration, which has greatly improved the efficiency of trend-type capacity index prediction.
A Novel Weighted Ensemble Method to Overcome the Impact of Under-fitting and Over-fitting on the Classification Accuracy of the Imbalanced Data Sets

In the data mining communal, imbalanced class dispersal data sets have established mounting consideration. The evolving field of data mining and information discovery seeks to establish precise and effective computational tools for the investigation of such data sets to excerpt innovative facts from statistics. Sampling methods re-balance the imbalanced data sets consequently improve the enactment of classifiers. For the classification of the imbalanced data sets, over-fitting and under-fitting are the two striking problems. In this study, a novel weighted ensemble method is anticipated to diminish the influence of over-fitting and under-fitting while classifying these kinds of data sets. Forty imbalanced data sets with varying imbalance ratios are engaged to conduct a comparative study. The enactment of the projected method is compared with four customary classifiers including decision tree(DT), k-nearest neighbor (KNN), support vector machines (SVM), and neural network (NN). This evaluation is completed with two over-sampling procedures, an adaptive synthetic sampling approach (ADASYN), and a synthetic minority over-sampling (SMOTE) technique. The projected scheme remained efficacious in diminishing the impact of over-fitting and under-fitting on the classification of these data sets.
CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts (“a scene with objects that have the same color”) and ad-hoc categories defined through goals (“objects that could fall on one’s head”). In contrast, standard classification benchmarks: 1) consider only a fixed set of category labels, 2) do not evaluate compositional concept learning and 3) do not explicitly capture a notion of reasoning under uncertainty. We introduce a new few-shot, meta-learning benchmark, Compositional Reasoning Under Uncertainty (CURI) to bridge this gap. CURI evaluates different aspects of productive and systematic generalization, including abstract understandings of disentangling, productive generalization, learning boolean operations, variable binding, etc. Importantly, it also defines a model-independent “compositionality gap” to evaluate difficulty of generalizing out-of-distribution along each of these axes. Extensive evaluations across a range of modeling choices spanning different modalities (image, schemas, and sounds), splits, privileged auxiliary concept information, and choices of negatives reveal substantial scope for modeling advances on the proposed task. All code and datasets will be available online.
Deliverable-Oriented Work Breakdown Structure: A Software Project Scope Verification Tool

Software project scope verification is an essential aspect in project scope management that must be completed correctly to prevent project rework and improve project scope control. A software project scope is defined as the work that needs to be carried out to create the required product with its necessary functions and features. Moreover, software scope verification is crucial in the process of delivering exactly what the customer requested and minimizing project scope changes. Well-defined software scope eases the process of scope verification and contributes to project success. A deliverable-oriented WBS often serves as a roadmap to a well-defined software scope of work. This chapter expands the use of deliverable-oriented WBS to the scope verification process on the basis of this. A deliverable-oriented WBS, according to this article, is a method for software scope verification.
Pengembangan augmented reality book berbasis situs peninggalan kebudayaan sebagai sumber belajar sejarah

Augmented Reality Book is the development of a book inspired by the development of Augmented Reality technology. A book that is developed combining books in the form of literacy works with the findings of archaeological relics created in 3D images to be displayed in books with the help of an android application. This research adheres to the development theory made by Plomp which consists of 1) Initial Phase, 2) Design Phase, 3) Product realization or manufacture stage, 4) Testing, assessment, and improvement phase, 5) Product testing phase. From the data on the results of filling in the research instrument in the form of a questionnaire, it was obtained that 80% of the student's response value or the percentage showed a positive value, as the assessment indicators were in good criteria, so a conclusion could be drawn that students were motivated by the use of learning resources Augmented Reality Book.
Future pedagogue’s sociо-professional maturity formation in scientific research activity

Scientific research activity is an essential component of the professional activities of a higher education pedagogue. Exploring the world around, future pedagogues not only organize their knowledge about pedagogy and the profession but also form a pedagogical style, professional and personal worldview. &#x0D; Students' scientific research activity is an important form of professional training, characterized by meaningful and organizational diversity, involves the formation of future pedagogues' knowledge and ability to use it in practice; the methods of scientific and pedagogical research, development of abilities to the analysis, synthesis, generalization of information obtained from literature sources and in the process of studying pedagogical process. Students' scientific research activities contribute to the development of professionalism of future lecturers of pedagogy, in particular skills and experience of carrying out scientific research activities. &#x0D; The methods we have used in the research process included, in particular, observation, expert evaluation, modelling and forecasting. &#x0D; The level of professionalism of the future lecturer of pedagogy is determined in particular by the development of one’s abilities and skills to organize scientific research activities. The scientific research activity itself is an important condition for the professional growth of the future pedagogue-professional.&#x0D; Scientific research activities of the future pedagogue involve one’s participation in the study of a particular pedagogical situation, observation of a particular pedagogical process, the study of students’ personality development.&#x0D; The generalized result of such activity is the work of the future pedagogue on the implementation of qualification work, which involves advanced study of theoretical aspects, systematization of previously acquired knowledge and supplementing them in the process of practical solution of a defined problem. Here the skills of research organization, organization of the experiment and independent activity are realized.
Benefiting Machine Learning Methods to Detect Fraud in the Validation of Bank Customers' Cards

The existence of money laundering and banking fraud is one of the major challenges of the banking system in any country. Crediting customers based on their track record and performance is a method to address the banking challenges. Classification methods can be used to validate customers, but these methods own high error. In this paper, machine learning methods are applied on banking data set to classify them and to reduce the error in customer validation. To this end, first the machine learning methods are trained and then tested using the banking data set. Experiments on the banking data set show that the accuracy of the proposed method for validating customers is less than 81.6%. So, the accuracy index of the random forest, decision tree, support vector machine, and multilayer artificial neural network are 80.50%, 80.05%, 80.93%, and 81.58%, respectively. The best performance is related to multilayer artificial neural network and, accordingly, the multilayer artificial neural network method can be used in detection of the validation of bank customers' cards.