Reviewerly/exHarmony huggingface dataset:
Paperant: Key Elements Generation with New Ideas

Long text generation, leveraging one sentence to generate a meaningful paper, is an effective method to reduce repetitive works. Conventional text generation models utilize rule-based and plan-based methods to produce paper, such as SCIgen, which is hard to suit the complex sematic scene. Recently, several neural network-based models, such as Point Network and PaperRobot, were proposed to tackle the problem, and achieve state-of-the-art performance. However, most of them only try to generate part of the paper, and ignore the sematic information of each entity in input sentence. In this paper, we present a novel method named Paperant, which leverage not only multi-sentence features to describe latent features of each entity, but also hybrid structure to generate different parts of the paper. In experiment, Paperant was superior to other methods on each indicator.
MODELING OF A WATER-SALT BALANCE OF A RIVER POOL WITH DEVELOPED IRRIGATED AGRICULTURE

The construction of a mathematical model of the water-salt balance for river basins with the predominant development of irrigated agriculture is considered. The structures of the water and salt balance of the irrigated territory and the balance of the main river bed within it are given. It has been established that if the inflow and outflow of water and salts dissolved in it are caused by its horizontal flows, then the evaporation and accumulation of water (salts) in the aeration zone and groundwater are caused by its vertical flows.This leads to the need to consider instead of the general equation of the water (salt) balance of the irrigated territory of its modifications for the distinguished three vertical zones, that is, “the surface of the territory – aeration zone – groundwater zone”. During the operation of the algorithm, the following are successively determined: elements of the water and salt balance of the irrigated territory and the riverbed; groundwater level as a result of the intake of salts with in-person flow of local tributaries; filtration losses from the channels and the lateral residue of the inflow; moisture content in the aeration zone after irrigation water and precipitation enters it; groundwater level after water from the aeration zone; moisture content in the aeration zone after the influx of groundwater into it; the outflow of groundwater into the riverbed and the final mineralization of water in the aeration zone.
From Quantum Mechanics to Quantum Technologies

PhotonicsViewsVolume 17, Issue 6 p. 38-38 PreviewFree Access From Quantum Mechanics to Quantum Technologies How fundamental physics turned into one of the hottest technologies today Dr. Stephan Ritter, Corresponding Author Dr. Stephan Ritter stephan.ritter@toptica.com www.toptica.com +49 89 85837-0 TOPTICA Photonics AG, Lochhamer Schlag 19, 82166 Gräfelfing / Munich, GermanySearch for more papers by this author Dr. Stephan Ritter, Corresponding Author Dr. Stephan Ritter stephan.ritter@toptica.com www.toptica.com +49 89 85837-0 TOPTICA Photonics AG, Lochhamer Schlag 19, 82166 Gräfelfing / Munich, GermanySearch for more papers by this author First published: 06 November 2020 https://doi.org/10.1002/phvs.202070610AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume17, Issue6December 2020/January 2021Pages 38-38 RelatedInformation
DIFFERENTIAL SEMANTIC SKETCHES FOR RUSSIAN INTERNET-CORPORA

The current paper suggests a new representation type of word collocations—the semantic sketches. It was first tested on one of the subcorpora of the General Internet-Corpus of Russian. The semantic sketches continue the idea of word sketches based on grammatical relations between words and expand it by adding the semantic information—word meanings and semantic relations between words. Moreover, the sketches can be additionally provided with metatextual characteristics. Certainly, building such sketches demands the semantic markup of the corpora. Therefore, we have used partial semantic analysis of the Compreno parser for our purposes. The paper demonstrates the examples of the sketches, provides the quality evaluation of the markup they are based on, and shows the advantages and disadvantages of the given approach.
Strategy Based Making Instruction Management Information Framework (EMIS)

The Education Management Information System (EMIS) is planned for share genuine data like allies of strategy creators. This examination intends to depict and investigate the execution of EMIS in strategy making in the Mapenda zone of ??the Ministry of Religious Affairs region Gunungkidul. The exploration results show that EMIS is compared to a base learning data for strategy making can't yet be utilized viably ideal. EMIS data is utilized distinctly as right on time as essential data cross-check through face to face gatherings or by means of phone correspondence so it requires some investment. The obstruction was that the EMIS information assortment measure was attempted distinctly at specific occasions so the data was not refreshed consequently, EMIS application issues, EMIS the executives skill, and the cooperation of madrasah chiefs in information assortment.
The Impact of Obstacles to Implementing Electronic Management Programs on The Development of Workers in Tourism Companies

The research aims to study the impact of administrative, organizational, financial, and human obstacles on the use of electronic management programs in tourism companies to develop workers' performance and analyze the performance level of workers in tourism companies as a result of their use of electronic management programs. The study found that the electronic program contributes to the development of workers' performance in the tourism companies that apply it in terms of quality of work, skill at work, and cooperation in work. The results also showed a lack of awareness among human resource managers and senior management in general of the importance of implementing and developing electronic programs. The study recommends that managers and senior management in tourism companies adopt and develop electronic programs that positively impact developing their employees' performance. The research suggests the necessity for tourism companies that use electronic programs to continuously develop these programs to suit the rapid technological changes to raise their employees' performance level and achieve their goals in general. It also recommends that companies that do not use electronic programs should oblige the upper management and human resource managers to change and provide all means To implement and develop electronic management programs that ensure the continuation of work with a high performance of its human resources within the overall strategy of the company.
Pengaruh Media Terhadap Pengambilan Keputusan Dalam Menjalankan Program Keluarga Berencana Dengan Algoritma Decision Tree

Indonesia has become one of the countries with a diverse population so that it has the potential to experience social change, one of which is the influence of the media. Media is an information content that is almost a part of human life. One of the impacts of the media is in the health sector, one of which is in determining the Family Planning program. Family planning is one of the Indonesian government programs designed to reduce the speed of population growth. Since the implementation of the Family Planning Program in Indonesia many tools have been used to prevent pregnancy, namely contraception. Selection of a good contraction is certainly one important thing to plan. In determining good kotrasespi certainly there are influences from various things one of which is the media. Measurement of the influence of the media in determining the Family Planning program can be known by applying data mining. Research conducted with data mining uses a standard methodology called the Cross-Industry Center Process for Data Mining (CRISP-DM). The use of decissin tree in this study was done by comparing the same method by looking at the results of three models namely Split Validation, Cross Validation and Decision Tree Split. The results of Split Validation produce an accuracy of 90.50%, Cross Validation produces an accuracy of 91.58% and Decision Tree Split produces an accuracy of 89.83%. The best results are obtained by using cross validation where with the results of research on 1473 records the accuracy value is 91.58% and the AUC value is 0.690, where the results are obtained from the calculation of the True Positive (TP) 1328, False Negative (FN) ) 36, False Positive (FP) is 88 and True Negative (TN) 21. Exposure to the media is said to be good or influential if they do not have children and are Muslim and educate their husbands in junior high school with a low standard of living but the wife has a college education. Keywords: Family Planning, Media Exposure, Data Mining, Decision Tree.
Проєктування віртуальної практичної підготовки (virtual practice) майбутніх педагогів в умовах змішаного навчання (blended learning)

International scientific and practical conference «Current trends and factors of the development of pedagogical and psychological sciences in Ukraine and EU countries» : conference proceedings, September 25–26, 2020. Lublin : Izdevnieciba «Baltija Publishing», 2020. 516 pages.
Study on Quality Demand for Online Shop Website

Purpose: This study focuses on the online shop website H and by Kano two-dimensional quality model, it obtains "the items to improve service quality" as the criteria for the website to improve the quality.Design/Methodology/Approach: According to SERVQUAL this study classifies the measures of service quality into Responsiveness, Tangible, Reliability, Empathy and Guarantee.According to Kano model analysis, this study obtains items which highly enhance customer satisfaction and highly reduce customer dissatisfaction.Findings: The study obtains five items which highly enhance customer satisfaction and highly reduce customer dissatisfaction: specific classification of products on the websites, immediate accomplishment of service, precise amount of bill after shopping, customer service personnel maintain certain degree of service quality, customer service personnel provide various products with consistent quality.It is suggested that the online shop website H should improve these five items in order to increase customer satisfaction and profits.Practical implications: This study focuses on the online shop website H and by Kano twodimensional quality model, it obtains "the items to improve service quality" as the criteria for the website to improve the quality.Originality/value.This study obtains five "items to improve service quality" which increase customer satisfaction and reduce customer dissatisfaction.The website can maintain good service quality of these items to result in maximum profit.
An End to End Learning Approach for Distance Estimation Trained with Artificially Generated Stereo Images

This paper proposes a solution for distance estimation using stereo images.The solution is a convolutional neural network that takes two images as an input, and outputs the distance estimate, without the need for prior camera calibration or disparity map calculation.The dataset used for training consists of images generated from an artificially constructed 3D scene.The training algorithm used was stochastic gradient descent.Evaluation of the solution was conducted on a separate dataset.Mean absolute error after the evaluation was 1.59 m, while the median value of the absolute error was 1.2 m.These results show that the proposed solution is a valid proof of concept for the usage of convolutional neural networks for the distance estimation of objects in stereo images in a single step.
Practice and Exploration of Online Education and Teaching during the Epidemic Situation——Taking Qingdao Teaching as an Example

The COVID-19 in now has disrupted the normal teaching schedule, but it did not stop the pace of education . Under the national call of “school suspension without suspension”. Qingdao primary school as an example, started a new learning mode of “home-schooling”, to minimum the impact of epidemic prevention and control on the teaching order. Qingdao City set the city’s famous teachers, build diacritical marks teams, create quality courses. Each school undertakes the heavy responsibility, does not show the weakness, the teacher advances the battle, displays the teacher style, discusses the new mode of online education and teaching together.
A Trust-Based Collaborative Filtering Approach to Design Recommender Systems

Collaborative Filtering (CF) is one of the most frequently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction.Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance.Moreover, CF is fragile and it is not suitable to find similar users.The existing literatures on CF show that integrating users' social information with a recommender system can handle the above-mentioned issues effectively.Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items.In this paper, we propose a trust-based recommender system, TrustRER, which integrates users' trusts into an existing user-based CF algorithm for rating prediction.It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores.For trust score, we have defined three novel trust statements based on user rating values, emotion values, and review helpfulness votes.To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected.The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method.TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.
Academic Mobility Of Foreign Language Students In The Credit System Of Higher Education

The article deals with the role of academic mobility in the credit system of higher education,with the use of level differentiation and a multi-level approach to teaching foreign languages. The article describes the international relations of the Mukimi KSPI and the peculiarities of its division into subgroups according to the level of language proficiency, which is the reason for the development of academic mobility.
Investigating the Effect of Mobile Apps on Student’s Motivation and Achievement in Motor Vehicle Mechanic Work

Purpose: The study was carried out to investigate the effect of mobile apps on students’ achievement in petrol engine maintenance work in technical colleges in Enugu State, Nigeria. Approach/Methodology/Design: The study adopted quasi-experimental design and was carried out in Enugu State, Nigeria. The population for the study was 170 NTC II students. The sample size for the study was 40 motor vehicle mechanics work NTC II students who have any mobile technologies (Smart phones) participated in the experimental group and were selected using purposive sampling technique. The instrument for data collection was motor vehicle mechanic work achievement test (MWMWAT). The data collected were analyzed using mean, to answer the research questions while ANCOVA was used to test the hypothesis. Three research questions were answered, while one null hypothesis formulated were tested at 0.05 level of significance. Findings: The study revealed that students taught petrol vehicle maintenance works with mobile apps had a higher mean achievement score than those students taught using conventional teaching aids in the achievement test. There was a significant difference between the mean achievement scores of students taught with mobile apps and those taught with conventional teaching aids in motor vehicle mechanics work trade. Practical Implications: The study will contribute positively to the understanding of using mobile apps to increase students’ motivation and academic achievement. Originality/value: This study innovates in examining the use of mobile apps by motor vehicle mechanic work teachers in improving the motivation and academic achievement of students.
(De-)Constructing Attacker Categorisations: A Typology Iteration for the Case of Digital Banking

In this extended and updated paper, the experimental construction of a new attacker typology grounded in real-life data is proposed, using grounded theory analysis and over 300 publicly available documents containing details of digital banking related cybercrime and involved attackers. Seven attacker profiles forming the typology specific to the case of digital banking are presented. An initial light-touch evaluation approach based on peer review feedback and basic heuristics is suggested. A short excursus on circumplex models is added to address this visualisation tool used across past categorisation efforts.
Improving Solar Potential Map: Ditching Net-Metering, Embracing Demand Curve and Local Weather

The research expands the bottom-up approach to determine investment cost for self-sufficient Solar Plus systems. Existing solar maps usually utilize net-metering scheme and treat electricity grid as giant battery. However, with increased solar production, this might put an additional strain to the grid. In addition, to obtain national wide results, multiple weather patterns obtained from different meteorological stations were analyzed which also resulted in different demand curves. The scope of the research was therefore largely increased by calculating all available meteorological data in form of a typical year for Slovenia, which consist of 23 data points evenly spread all over Slovenia. However, those points vary quite a bit – some stations are positioned in the cities and towns, while others are located in small villages or places of interests (i.e., mountain resorts or airports). The points also vary in altitude (from few meters to over 2500 m) and cover different climate zones. The majority of Slovenia lies in continental zone (described with Dfb and Dfb by Köppen-Geiger climate classification) with cold winters and warm summers, with some subtropical climate at the coastal areas (Cfa and Cfb). Some mountain areas have tundra climate (ET). The presented research shows how to combine the existing data to calculate optimal Solar Plus instalment for self-sufficient buildings for each calculation point. The already validated simplified model was used for calculation of energy balance for the typical year which was later used for optimization of the results regarding costs. This includes the roof area for photovoltaic, optimal orientation of solar panels, the capacity of the battery storage and other influential parameters. The results clearly indicated the capability of each analyzed area for Solar Plus installation.
Human biases in government algorithms

Machine learning is used in data-driven decision making and in governmental application of algorithms. Along with its many benefits, literature shows that machine learning solutions can introduce human bias to their results that they from training data. In this paper the authors study sentiment analysis algorithms to see if they show human bias, by interchanging lists of names associated with gender, race and political orientation in a synthetic neutral template sentence and then observing how the resulting sentiment values change. As a result, the authors find that names as subjects and objects in English language texts alone do not introduce human bias to the sentences' sentiment values. The findings also suggest that pseudonymization might be a better solution to the anonymization of social media postings for data protection compliance purposes than research so far suggested, owing to the general lack of sentiment distortion of the method introduced in this paper.
Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Analyzing and classifying five types of Lymphocyte White Blood Cell (WBC) is important to monitor the lack or excessive amount of cell in human body.These harmful amount of cell must be detected early for the early treatment can be run to the patient.However, the process may be tedious and time consuming as it is done manually by the experts.Other than that, it may yield inaccurate result as it depends on the pathologist skill and experience.This work presents a way that can be the second opinion to the experts using computer aided system as a solution.Convolutional Neural Network (CNN) is applied to the system to avoid complex structure and to eliminate the features extraction process.Three CNN models of mobilenet, resnet and VGG-16 is experimented on three different datasets which are kaggle, LISC and IDB-2.Kaggle, LISC and IDB-2 dataset consist of 6000, 242 and 260 images respectively.The result is divided into two parts which are dataset and model.As for IDB-2 dataset, the best model is VGG with training and validation accuracy of 0.9721 and 0.7913 respectively.While for kaggle and LISC dataset, the best model is resnet as it achieved training accuracy of 0.9713 and 0.9771 respectively.The highest validation accuracy for kaggle is 0.5955 and 0.5781 for LISC.Lastly, the best database that is most suitable for all model is IDB-2 database.It obtained highest training and validation accuracy for all model of mobilenet, resnet and VGG-16.
Approximate Planning and Learning for Partially Observed Systems

Reinforcement learning (RL) provides a conceptual framework for designing agents which learn to act optimally in an unknown environment.RL has been successfully used in various applications ranging from robotics, industrial automation, finance, healthcare, and natural language processing.The success of RL is based on a solid foundation of combining the theory of exact and approximate Markov decision processes (MDPs) with iterative algorithms that are guaranteed to learn an exact or approximate action-value function and/or an approximately optimal policy.However, for the most part, the research on RL theory is focused primarily on systems with full state observations.In various applications including robotics, finance, and healthcare, the agent only gets a partial observation of the state of the environment.In this talk, we describe a new framework for approximate planning and learning for partially observed systems.This framework is based on the notion of approximate information state.The talk will highlight the strong theoretical foundations of this framework, illustrate how many of the existing approximation results can be viewed as a special case of approximate information state, and provide empirical evidence which suggests that this approach works well in practice.