Reviewerly/exHarmony huggingface dataset:
Schema and content aware classification for predicting the sources containing an answer over<i>corpus</i>and knowledge graphs

Today, several attempts to manage question answering (QA) have been made in three separate areas: (1) knowledge-based (KB), (2) text-based and (3) hybrid, which takes advantage of both prior areas in extracting the response. On the other hand, in question answering on a large number of sources, source prediction to ensure scalability is very important. In this paper, a method for source prediction is presented in hybrid QA, involving several KB sources and a text source. In a few hybrid methods for source selection, including only one KB source in addition to the textual source, prioritization or heuristics have been used that have not been evaluated so far. Most methods available in source selection services are based on general metadata or triple instances. These methods are not suitable due to the unstructured source in hybrid QA. In this research, we need data details to predict the source. In addition, unlike KB federated methods that are based on triple instances, we use the behind idea of mediated schema to ensure data integration and scalability. Results from evaluations that consider word, triple, and question level information, show that the proposed approach performs well against a few benchmarks. In addition, the comparison of the proposed method with the existing approaches in hybrid and KB source prediction and also QA tasks has shown a significant reduction in response time and increased accuracy.
Tagging large-radius $b$-jets from Higgs decays dropping unneeded information

Multivariate approaches used in physics analyses by the High Energy Physics community often combine high-level observables estimated by very complex algorithms.The process to select these variables is usually based on a "brute force" approach, where all available event features are tested for multiple combinations of the algorithm hyperparameters.In this work, we propose an original method based on the use of a CancelOut layer to select to give as input to a Fully Connected Neural Network.Promising results are obtained in the development of a DNN classifier to select proton-proton collisions where a boosted Higgs boson decay to two -quarks.
Methodological center for improving financial literacy was created

A decision was made to create a federal Methodological Center for improving financial literacy at the RANEPA, which is aimed at introducing methodological materials on financial literacy, and is also focused on improving the qualifications of teachers and tutors of schools and colleges.
An Evaluation of Big Data Use for Fraud Detection in Hotel Enterprises

The types of fraud encountered in enterprises are mainly classified into asset abuse, corruption, and financial statement fraud. The literature deals with the use of big data to detect these frauds from various sectors’ perspectives. The studies have stated that the data generated in the enterprise’s internal and external environments provides faster access than the database, reliable evidence is obtained by analyzing these data, and this evidence effectively detects fraud. In this study, big data in detecting fraud in hotel businesses is presented within the literature framework. The hotel industry has diverse data generated from management information systems, websites, social media, and blogs. Big data transforms these multiple source data into valuable, meaningful, and processable forms. When big data is interpreted with appropriate analysis techniques, it enables enterprises to manage fraud risks.
A Study on Teaching Strategies of English Writing in Senior High School From the Perspective of Thematic Progression

Writing is an important competence for English learners, which can reflect their English expression ability and logical ability. However, there are still many problems in English writing teaching, such as students’ lack of logic and teachers’ lack of writing teaching strategies. By analyzing the problems in English writing teaching in senior high school, this study puts forward some pre-writing and post-writing teaching strategies from the perspective of thematic progression, in order to provide more insights for the field of English writing.
Diseño instruccional ADDIE y tecnología emergente en educación superior para el aprendizaje del idioma inglés en época de pandemia

This work is an investigation with a mixed approach in which the results obtained in a blended training experience in an autonomous institution in Valle del Fuerte, in the State of Sinaloa, Mexico, are exposed. The study is supported by the use of technologies, a successful case of the innovative, quality, techno-pedagogical instructional model used in various prestigious universities called ADDIE, carried out in new students of the Bachelor of International Business Relations in the subject of English I. The instructional model was implemented to improve the traditional way of generating knowledge in the classroom, with the support of a virtual learning environment to satisfy the need for communication and teacher-student interaction and improve learning processes and results. The quasi-experimental research design was used, in two groups, one experimental and the other control, to know the possible implications on the academic performance of the students who participated in the experience. For the review of the sample, the F test was established to determine the asymmetry in the data or the type of distribution, the statistical test applied to carry out the analysis was determined by means of the Mann and Whitney U test to obtain the P -value. Regarding the dimension of integrating knowledge and the degree of statistical significance, 95% was considered, with a margin of error of 5%, yielding a result = 0.02267 clearly lower than the assumed error, stating that there is a significant difference after the application of instructional design. The conclusions are presented and based on these, recommendations are made based on the results of this research.
iSCREAM: a suite for Smart Contract REAdability assessMent

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Implementasi Teknik Metastasis Berbasis Keluarga dalam Upaya Mewujudkan Kampung Yanggandur Merauke sebagai Kampung Melek Aksara

The goal of this community service program is to make Yanggandur Village a Literary Literacy Village. The target of this program is 20 families who are still illiterate in Yanggandur Village. The method of implementing the program is a family-based metastasis strategy. Technically, this program is integrated with the Musamus University Community Service Program. 12 students act as the first tutor who will train the executive tutor who is one of the target family members. Furthermore, the executive tutor will teach other family members. At the end of the program, 36% of participants were able to write word for word in one or two sentences fluently and correctly, 56% of participants had been able to read word for word in one or two sentences correctly, and 60% of participants had been able to add and subtract shapes dozens. In conclusion, the implementation of family-based metastasis techniques can improve the ability to write, read, and count in the people of Yanggandur Village Merauke.
Peran Tenaga Administrasi dalam Meningkatkan Mutu Layanan Administrasi di MTs Hidayatul Muhisinin

Researchers are interested in researching this in order to find out: The Role of Administrative Personnel in Improving the Quality of Administrative Services at Mts. Hidayatul Muhsinin With the main problem: Activities and Role of Administrative Personnel in Improving Service Quality, Barriers in Improving Administrative Service Quality and Strategies in Improving Administrative Service Quality. The method used in this study is a descriptive method with a qualitative approach. The main data sources in this study were school principals, Deputy Head of Curriculum, Finance Head of Administration and teachers obtained through interviews, observation, and documentation. The results of this study indicate that 1). The performance of administrative staff with the strategy implemented is to facilitate the payment or fee system that has been implemented since 2008, and maintain and manage school infrastructure facilities. The integration of the program from administrative staff with daily activities at school is still ongoing, although there are several obstacles in implementation. 2). The obstacles faced by schools in managing administrative services are archive warehouses that have not been optimal, a comprehensive database system has not been implemented and the preparation of the School Revenue and Expenditure Budget Plan still has difficulties related to costs that are not raised, existing facilities still have complaints from service users but complaints These complaints are not always handled quickly because they are related to the number of administrative staff. 3). Efforts are being made to overcome some of these obstacles, namely providing a letter of notification to parents to collect files immediately, increasing the payment of school fees to new students to cover the financing system that is not increased, so that each class pays school fees with different fees, the next strategy implementation of a system for controlling the condition of school facilities. The most important aspect that determines the success of an educational institution is the efficient implementation of all school organizational activities. The role of the administrative staff themselves in the school is very meaningful in supporting the smooth and successful administration of the school. Therefore, the administration must be able to carry out its duties and functions properly so that competence and expertise are needed to support the field of administration.
FFDNet Based Channel Estimation for Multiuser Massive MIMO System with One-Bit ADCs

Low-resolution analog-to-digital converters (ADCs) in massive multiple-input multiple-output (m-MIMO) systems offer significant throughput increase for wireless communication. The low-resolution ADC part limits the performance of the transceiver, especially estimating the channels from highly quantized measurements. Mapping from quantized received signals to channel is very challenging. Since the channel response has the characteristic of sparsity, we have leveraged a fast and flexible denoising convolutional neural network (FFDNet) based channel estimation scheme for the m-MIMO system with one-bit ADCs treating the channel matrix as a 2D natural image in this letter. FFDNet can effectively learn from sufficiently large training datasets and is exploited to estimate the channel in the m-MIMO system equipped with one-bit ADCs. We have investigated the exhibited performance of the FFDNet based channel estimation through simulation results. A significantly reduced normalized mean square error has been achieved in our proposed scheme.
The study of L2 mispronunciation detection based on Mandarin landmarks

It has been proved that phonetics knowledge or data-driven method based acoustic landmarks are useful in detecting mispronunciation. The acoustic landmarks obtained by the two methods are not completely consistent. It shall be studied which method is better. The role that the acoustic landmarks play in the mispronunciation detection task needs to be further explored. This paper compared the consistency of different acoustic landmark detection methods. The paper also compared the effect of mispronunciation detection through TNDD-GOP architecture and hybrid CTC/Attention model. The paper verifies the role of acoustic landmark method in mispronunciation detection task with weighted method. The experimental results show that the higher the weight of acoustic landmark is added the better the detection performance is got. The results show that the mispronunciation detection performance of the updated acoustic landmark based on phonetics knowledge is better than that of the data-driven method. The DA and FRR are improved by 3.38% and 1.38%.
Investigating the feasibility of harvesting broadcast speech data to develop resources for South African languages

Sufficient target language data remains an important factor in the development of automatic speech recognition (ASR) systems. For instance, the substantial improvement in acoustic modelling that deep architectures have recently achieved for well-resourced languages requires vast amounts of speech data. Moreover, the acoustic models in state-of-the-art ASR systems that generalise well across different domains are usually trained on various corpora, not just one or two. Diverse corpora containing hundreds of hours of speech data are not available for resource limited languages. In this paper, we investigate the feasibility of creating additional speech resources for the official languages of South Africa by employing a semi-automatic data harvesting procedure. Factorised time-delay neural network models were used to generate phone-level transcriptions of speech data harvested from different domains.
Photonic accelerator based on optical chaos

We overview recent progress in reservoir computing and decision making using complex laser dynamics. We demonstrate reservoir computing using a photonic integrated circuit with optical feedback. We introduce a scheme for multiple semiconductor lasers placed in parallel for improving the performance of reservoir computing. We also perform decision making for solving the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers
8K-UHD medical expansion with development of an 8K-UHD encoding recorder

We have developed 8K-ultra high definition (UHD) endoscopic system and 8K-UHD microscope system to reduce the burden on surgeons and contribute to improving medical safety. On the other hand 8K-UHD video has large data size than the conventional one such as HD and 4K-UHD videos, and it is difficult to secure sufficient recording time. Moreover these systems are expected to be used only in operating rooms, and extra technical supports and special player devices are required for presentation of 8K-UHD surgical movies by surgeons at academic meetings. Therefore, by applying video compression encoding technology (H.265/HEVC), we have developed the 8K-UHD video recorder “RESTAR R8” that achieves both 8K-UHD image quality and ling-time recording. This new recorder enables medical professionals not only to record 8K-UHD video in the same way as conventional medical recorders in the operating room, but also to record, edit and play back outside the operating room with utilizing the network connection. We would like to continue working on technology and solution development so that we can utilize the recorder not only in the operating room and medical education fields, but also in a wide range of solution fields such as digital video, security and manufacturing inspection fields where 8K-UHD video is expected to be used in the future.
Useful techniques and applications of computational intelligence

The key component of computational intelligence is computational models and techniques that incorporate learning, adaptability, and/or heuristic optimization. Complex problems that conventional computer techniques cannot solve are handled with artificial intelligence. Neural networks, evolutionary computing, and fuzzy systems are the three main principles for creating artificial intelligence. These are some of the latest issues in machine learning and natural language processing, including artificial immune systems, support vector machines, rough sets, and chaotic systems. Computational intelligence techniques, including neural networks, fuzzy systems, and swarm intelligence, are reviewed in this chapter.
Management of construction cost of large-span steel structure project based on variable-two-dimensional cloud model

In order to improve the management and control ability for the large-span steel structure project, an evaluation model for the management of construction cost in the large-span steel structure project based on the variable-two-dimensional cloud model was proposed. First, the main factors affecting the management of construction cost were analyzed from the perspective of physical improvement, practical optimization and human coordination with the WSR methodology, to construct the evaluation index system for the management of construction cost in the large-span steel structure project. Then, the vector included angle cosine method and variable weight theory were adopted to assign weights for each evaluation index, and the economy and effectiveness of large-span steel structure project were comprehensively evaluated with the two-dimensional cloud model. Finally, taking a theater as the research object, the level of the management of construction cost was determined to be good through MATLAB software drawing comprehensive two-dimensional cloud map, and the proximity was calculated to further determine the evaluation results, which verified the effectiveness and applicability of the model, providing the theoretical basis for subsequent management of construction cost in the largespan steel structure project.
Prediction of daily direct solar energy based on XGBoost in Cameroon and key parameter impacts analysis

This study explores the ability of Extreme Gradient Boosting (XGBoost) to predict the direct normal irradiation (DNI) under clear sky conditions in Cameroon. The satellite data used are DNI clear sky, air Temperature, Relative Humidity, Wind Speed, Wind direction, irradiation at Top of Atmosphere (TOA) and Aerosol Optical Depth at 550 nm (AOD550) for each aerosol type (Black Carbon: BCAOD550; Organic Matter: OMAOD550; Sea Salt: SSAOD550; Sulphate: SUAOD550 and Dust: DUAOD550). To achieve this aims and build a worst case prediction scenario, K-means clustering algorithm with Elbow and Silhouette analysis are used to select training and validation data sets. The coefficient of determination R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , root mean square error RMSE and the interpretation of the model outputs in the light of the state of the art confirm the robustness of the used model. The interpretation of the XGBoost outputs using the Shapley’s value shows that the amount of energy in the study area is most impacted by DUAOD550, OMAOD550, temperature, SUAOD550, TOA, SSAOD550 and relative humidity respectively. Results suggest also that, if dust and organic matter aerosols are present in the same proportion, the attenuation produced by them can be 4 to 10 times higher than those induced by black carbon and sea salt aerosols.
Foreign Aid, Trade Openness and Economic Performance in Sub-Saharan Africa: A Panel Cointegration Investigation

The objective of this paper was to investigate the impact of foreign aid and trade openness on economic performance for a panel of 30 sub-Saharan African countries over the period 1970-2017 using panel cointegration techniques.The findings obtained in this paper suggest that foreign aid has contributed significantly to economic performance in SSA while trade openness has been detrimental to economic performance during the period under consideration.Also, the combined effect of aid and trade openness has enhanced economic performance.From a policy perspective, the study suggests that more should be done so that the benefits from aid inflows would result in sustained economic performance.Also, the external trade sector should be given the needed attention it deserves.
Simple design of digital frequency meter based on microcomputer

Frequency is a fundamental parameter in the field of Electronic technology. As a basic measuring instrument, the digital frequency meter is extensively applied for its features such as high precision, fast speed, digital display. This paper describes a very simple and data transmittable design of a Digital Frequency Meter using a PIC microcontroller with some simple functions. This digital frequency meter has a range of frequency measurement from 1Hz to 100kHz and a resolution of 1hz and accuracy less than 0.5%. Digital Frequency Meter measures the frequency and displays its data on 1602 LCD screen with a unit in thousands(kHz); the usage of microcontroller enables it to toggle measuring mode, pause the data display and transmit data to other microcontrollers for extra uses.
Soft Skills and Digital Competencies in Teacher Professional Development in Times of a COVID-19 Pandemic

In the current context, the priority objectives to recover education affected by the Covid-19 pandemic are evident and in that sense, the soft skills of teachers and their digital skills become important, because they influence teacher professional development. To determine the levels of soft skills, the validated instrument of the article by Rodríguez (2020) was used, the levels of digital competences were used by the instrument of Tourón et al. (2018) and to establish the levels of teacher professional development, the validated instrument of Porras (2020) was used, whose research purpose for this study was to establish the incidence of soft skills and digital competences in teacher professional development in times of pandemic by Covid-19. It was concluded that soft skills and digital competence significantly affect teacher professional development in times of the Covid-19 pandemic, since Nagelkerke = 0.669, establishing that soft skills and digital skills affect 66.9% in teacher professional development; and of the predictor variables, soft skills predict teacher professional development, due to Wald = 80,193; p = 0.000 &lt;0.05.
Testing a global null hypothesis using ensemble machine learning methods

Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is an important task in biomedical studies. We seek to improve the power of such testing methods by leveraging ensemble machine learning methods. Ensemble machine learning methods such as random forest, bagging, and adaptive boosting model the relationship between the outcome and the predictor nonparametrically, while stacking combines the strength of multiple learners. We demonstrate the power of the proposed testing methods through Monte Carlo studies and show the use of the methods by applying them to the immunologic biomarkers dataset from the RV144 HIV vaccine efficacy trial.
An Overview of Cloud Computing

Today is the technology of Cloud Computing Technology in IT Industries. Cloud computing that's primarily based totally on Internet has the maximum effective structure of computation. It reckons in of a compilation of included and networked hardware, software program and net infrastructure. It has numerous avails atop grid computing and different computing. In this paper, I even have given a short of assessment of cloud computing through reviewing greater than 30 articles on cloud computing. The final results of this evaluate signalizes the face of the IT industries earlier than and after the cloud computing.