Reviewerly/exHarmony huggingface dataset:
A CBR-based power engineering cost estimation method

Case-based Reasoning (CBR) is an important reasoning methodology in the field of artificial intelligence. The main idea of CBR is to solve new problems by using historical cases. Since the construction of CBR models need to be based on the data of similar historical cases, the reusability of CBR models is usually low. Constructing CBR models for specific problems is one of the research hot-spots in the field of CBR methodology. The CBR model for power engineering cost estimation is studied in this paper. A novel CBR model considering the characteristics of power engineering industry is proposed. The multidimensional scale change (MDS) method and K-means method are introduced into the proposed CBR model to reduce the data dimensional and solve the problem of low calculation accuracy. An artificial neural network (ANN) model is constructed in the proposed CBR model and the deep learning technology is used to estimate the cost of engineering projects. Simulation results show that the proposed CBR model can estimate the cost of power engineering projects accurately and the estimation error is less than 8%.
Language Statistical Machine Translation Muna to Indonesia Language

In this rapid technological development, there are still at least some machine translators from regional languages ​​to Indonesian. Therefore, this paper discusses to make a statistical translation machine for the Muna language into Indonesian because at least there are still at least a Muna translation machine into Indonesian. The approach used a statistically based using parallel corpus. In this study, the data taken came from a book entitled Folklore of Buton and Muna in Southeast Sulawesi and several folklore articles on the internet. The number of parallel corpus used is 1050 sentence lines and the monolingual corpus is 1351 sentence lines. The scenarios that will be carried out in this experiment are divided into two scenarios. Scenario 1 is testing on the parallel corpus (training) which is tested using the available sentence lines and these sentence lines will be added to each experiment, while the rest of the sentence lines that are owned will be used in the parallel corpus (testing). In scenario 2, the test is carried out by comparing the lines of the monolingual corpus sentences after subtracting and adding sentences. In order for scenario 2 to run, accuracy is needed in scenario 1 which is the best. The test was carried out 6 times using BLEU (Bilingual Evaluation Understudy) tools. From the results of the tests carried out, the best accuracy value is 29.83%.
From the pandemic to the recovery: a legal analysi

The Covid pandemic has raised various legal issues, fueling the scientific debate on the relationship between fundamental rights and freedoms in the global emergency context. Moreover, a case law has started developing within the different jurisdictions. Additionally, constitutional Courts, in different countries, have ruled over potential conflicts of interest among central powers and local ones, and even some decisa of the Court of Justice of the European Union have started “blossoming” in this area. Against the backdrop of this analysis, the paper discusses the main legal problems sparked off by the declaration of the state of emergency, with a focus on the main EU jurisdictions and with glimpses of non-EU countries. The aim of this is to discuss the balance between fundamental rights and liberties in decisa in different legal systems, as well as the interpretation given to principles of proportionality of the public health measures, adequacy, precaution and loyal collaboration and the relationship between freedom and limits to public power. Bearing this in mind, the purpose of the work is to demonstrate that, first and foremost, in Europe there is room for both a formal and a substantial recognition of common rights and liberties in terms of interpretation and application of constitutional traditions, shared by the different Member States. The relevant adherence to these principles is guaranteed by the European Court of Justice. Second, the recovery after the pandemic is an open challenge. An important opportunity for Europe and its Member States is materialising, and this is to take a step forward on the bumpy path toward a European Political Union capable of strengthening a structure weakened by several earthquakes. A path and a project still plenty of pitfalls that needs to regroup around a central core increasing unification among European peoples (art. 1 TEU), which has never meant to be an alternative to national identity.&#x0D; Received: 24.11.2021Accepted: 13.12.2021
The Impact of Corporate Entrepreneurship on the Performance of Jordanian Telecom Corporates

The Telecom sector in Jordan is highly competitive in a way that affects the performance of firms working in this sector, many solutions were provided to enhance performance, but corporate entrepreneurship as a solution to significantly improve performance still not have fully adopted, that is why this research was carried to highlight the importance of such concept to improve performance. This research was aimed at determining the impact of corporate entrepreneurship dimensions (innovation, risk-taking, proactiveness, competitive aggressiveness, and autonomy) on the performance of Jordanian telecom corporates in Jordan. Data were collected from 39 telecom corporates in Jordan. The questionnaires entail assessing the degree of corporate entrepreneurship in relation to the performance of telecom corporates in Jordan. SmartPLS 2.0 Statistical program was used to conduct descriptive and inferential statistics. The findings of the research indicated that corporate entrepreneurship dimensions (innovation, risk-taking, proactiveness, and competitive aggressiveness) positively affect the performance of Jordanian telecom corporates except for the autonomy dimension.
An ontology-based approach to the analysis of the acid-base state of patients at operative measures

Researchers working in various domains are focusing on extracting information from data sets by data mining techniques. However, data mining is a complicated task, including multiple complex processes, so that it is unfriendly to non-computer researchers. Due to the lack of experience, they cannot design suitable workflows that lead to satisfactory results. This article proposes an ontology-based approach to help users choose appropriate data mining techniques for analyzing domain data. By merging with domain ontology and extracting the corresponding sub-ontology based on the task requirements, an ontology oriented to a specific domain is generated that can be used for algorithm selection. Users can query for suitable algorithms according to the current data characteristics and task requirements step by step. We build a workflow to analyze the Acid-Base State of patients at operative measures based on the proposed approach and obtain appropriate conclusions.
Incorrect Recommendation Wording in Figure

included.This demonstrates the need for prospective central imaging review to standardize the inclusion of patients with pancreatic cancer using strict resectability criteria.With respect to the support for neoadjuvant systemic therapy in localized pancreatic cancer, we note that the ESPAC-5F study 4 was a small, randomized, phase 2 trial that included fewer than 100 patients across 4 groups.Although it provided support for neoadjuvant systemic therapy, it did not provide a definitive answer.We look forward to publication of the final data set.As Neoptolemos and colleagues mention, controversy exists about the role of radiation therapy in localized pancreatic cancer.Our summary 1 of the current state of the role of radiation could not capture all the nuances, opinion, and perspectives on this topic.We believe that a "precision" approach to localized pancreatic cancer involves consideration of all available therapeutic modalities, including optimal systemic therapy, selective surgical approach, and evaluation of the role, if any, of radiation.The Alliance A021501 trial demonstrated that hypofractionated radiation (stereotactic body radiation), compared with the historical reference, did not improve outcomes when added to systemic therapy. 5However, this study did not address the role of other radiation modalities, which is a subject of ongoing investigation and, we agree, is best addressed in the context of ongoing randomized trials.
Pedagogical model for the development of a sustainable lifestyle of a student in a cultural landscape

The article considers the eco-friendly lifestyle of a person in a cultural landscape as a prerequisite for sustainable development of a territory and a practical basis for creating a qualitatively new harmonious interaction between society and natural systems. The goal of the article is to theoretically substantiate, devise, and test a pedagogical model that would teach students a sustainable lifestyle in a cultural landscape. The methodological basis of the study is the coevolutionary subjective, activity, transdisciplinary, integral, situational, cultural, ecological, landscape, and environmental approaches, as well as some principles embodying them. Educational modeling and design were the key theoretical methods. The pedagogical model created combines the target, substantive, procedural, technological, productive, and evaluative components. Its practical use in schools proved to be effective regarding the formation of an eco-friendly lifestyle of students in the cultural landscape.
Very Short Term Photovoltaic Power Generation Station Forecasting Based On Meteorology Using Hybrid model Decomposition-Deep Neural Network

Using a new hybrid technique, this research study provides a 1-hour prediction for solar power output from a power producing facility. When it comes to improving solar power generation operations, meteorological data plays an important role. The Decomposition and Deep-Neural Network approaches are used to create this hybrid model. This new hybrid model incorporates meteorological data. As an input to the Deep Learning Neural Network model, the first hybrid model uses decomposition. It is the mean absolute percentage error of the Decomposition-Deep Neural Network) model that serves as a statistical indication of statistical error. Decomposition Deep NN-based predictions are compared to observed data, decomposition technique, and simulation results, which show that the approach provided in this research can determine hourly solar power generation with acceptable accuracy.
Adverse Event Report Classification in Social and Medico-Social Sector

Adverse event (AE) analysis is one of the most important missions in French social and medico-social centers, which enables targeted prevention measures to avoid recurrences. However, social and medico-social centers are struggling to conduct an efficient analysis of AEs due to the abysmal quality of event reports. In this paper, we propose an automated classification solution to predict the fact of an AE from the event description to ease the decision-making task of center managers. This solution explores different text similarity methods based on a dedicated terminology of synonyms for the social and medico-social sector in being able to propose the most appropriate facts. The approach has the advantage of being fast with an accurate prediction since it achieved an accuracy up to 88%, when it is tested on 388 real AE reports.
Building a Chatbot Using Transfer Learning

In today’s world, most businesses need to have customer support for their products and services. With the increase in e-commerce, telecommunication services, Internet-related products, and so on, the demand for customer service is only increasing. The nature of customer service support queries is repetitive in most conversations. Customer support conversations can be automated.
Progress Guidance Representation for Robust Interactive Extraction of Buildings from Remotely Sensed Images

Accurate building extraction from remotely sensed images is essential for topographic mapping, cadastral surveying and many other applications. Fully automatic segmentation methods still remain a great challenge due to the poor generalization ability and the inaccurate segmentation results. In this work, we are committed to robust click-based interactive building extraction in remote sensing imagery. We argue that stability is vital to an interactive segmentation system, and we observe that the distance of the newly added click to the boundaries of the previous segmentation mask contains progress guidance information of the interactive segmentation process. To promote the robustness of the interactive segmentation, we exploit this information with the previous segmentation mask, positive and negative clicks to form a progress guidance map, and feed it to a convolutional neural network (CNN) with the original RGB image, we name the network as PGR-Net. In addition, an adaptive zoom-in strategy and an iterative training scheme are proposed to further promote the stability of PGR-Net. Compared with the latest methods FCA and f-BRS, the proposed PGR-Net basically requires 1–2 fewer clicks to achieve the same segmentation results. Comprehensive experiments have demonstrated that the PGR-Net outperforms related state-of-the-art methods on five natural image datasets and three building datasets of remote sensing images.
Input-output analysis of the integration of primary, secondary and tertiary industries in rural areas of Inner Mongolia under the background of big data

The theoretical and practical research of industrial integration provides new directions for the development of many industries and new ideas for the reform of many technologies. In particular, it provides new impetus for the development of agricultural modernisation. This paper takes the rural development of Inner Mongolia as the research object, discusses the integration of the primary, secondary and tertiary industries in agriculture, and then provides guidance for the further development of agriculture. Moreover, this paper combines big data technology to construct an input–output analysis model for the integration of primary, secondary and tertiary industries in rural areas of Inner Mongolia, and on this basis, conducts research and analysis on the model. In addition, this paper uses a simulation model to analyse the industrial integration and uses the statistical yearbook data of Inner Mongolia as the input to calculate the data mining effect and data analysis effect of this model. Through experimental research and analysis, it can be seen that the model constructed in this paper can play a certain effect in the analysis of the integration of primary, secondary and tertiary industries in the rural areas of Inner Mongolia.
Accurate internal deformation measurement of an indentation test using micro-CT and self-adaptive digital volume correlation

Mechanical indentation testing is a widely used technique for determining local mechanical properties of materials. Accurate measurement of internal deformation in the indentation test is necessary for further study of material properties. Therefore, an in situ experimental measurement strategy combining micro-CT imaging and self-adaptive digital volume correlation (SA-DVC) is proposed. Unlike conventional DVC, SA-DVC can automatically identify the optimal subvolume size for each calculation point, which can effectively minimize measurement errors. The efficacy of the proposed method is first verified by the simulated indentation experiment. Then, it is used to analyze the deformation of epoxy resin composite in a real indentation experiment. Measurement results indicate that the proposed method can estimate three-dimensional displacement and strain fields with enhanced accuracy, and further application of the obtained measurement results on material parameter identification and stress field reconstruction is expected.
Hybrid Chicken Swarm Optimization (CSO) and Fuzzy Logic (FL) Model for Handling Imbalanced Datasets

The unequal representation of the various divisions existing in the data is one of the main data inconsistency issues.Data with an imbalanced distribution negatively influence the efficiency of most conventional classifiers.This paper introduces a new method for over-sampling the handling of imbalanced data sets.The method hybridize chicken swarm optimization and fuzzy logic (CSO-FL).The proposed model ensures that the synthetic samples generated reside in minority regions.The proposed hybrid CSO-FL applied on three datasets with different imbalanced ratios between 1.78 and 129.44.It demonstrated significant improvements in the efficiency of various classification techniques.During the classification process, we used KNN, DT, SVM and Naïve classifiers.The obtained results were very promising; the precision, sensitivity, and F_score values are enhanced in all classifiers.The values in one dataset improved with ratios >90 % in many classifiers because of the high imbalanced ratio in this dataset, while in the other two datasets, the measurement values enhanced with ratios from nearly 10% to 30%.The CSO-FL approach compared with three different approaches on the same datasets.The approaches are SMOTE algorithm, modified SMOTE, and TGT algorithm and proved to outperform their results.
Extracting Aspect Opinions from Reviews in Spanish for Aspect-based Recommendations

Opinions about item’s aspects in user reviews may be a valuable source of information for improving Recommender Systems performance. Despite important advances in Natural Language Processing and Machine Learning fields that allow the extraction of such information from texts written in English, there is still work to do in the case of texts in other languages. In this work, we adapt known unsupervised aspect extraction approaches to extract opinions from reviews written in Spanish. Our results show that the exploitation of aspects extracted automatically from reviews written in Spanish allow to improve considerably the top-k recommendation lists generated by Aspect-based Recommender Systems.
Professional education in the digital transformation society

Purpose of the article is analysis of the experience of training students in the context of digital transformation. The article presents the dynamics of the development of online schools at the global level, highlights the main goals of using electronic educational resources. The respondents were presented with the variety of answer options. The most common were selected and put into a separate diagram. Results says the development of online education is being carried out at a high rate both in Russia and at the global level. Digital transformation gives rise to the demand for the development of new professions directly related to electronic instruments. Based on the data obtained, first of all, there is a need for specialists who are ready for high-quality teaching of students in the online space. The variety of electronic educational resources expands the opportunities for their training and focuses professional education on new reform processes.
Adaptive Computational Methods for Bayesian Variable Selection

Efficient computational methods to sample from the posterior distribution of models are key to the use of Bayesian variable selection in practice. Adaptive Monte Carlo are a promising approach to build such methods. We review the use of these methods in generalised linear models with a particular focus on linear and logistic regression. We illustrate how these methods can be applied to simulated data and to two contrasting real-life examples. Firstly, we consider a high-dimensional example with an application to fine mapping in genomics with 10,995 observations and 5766 covariates (SNPs). Secondly, we consider an application to a complex model of environmental DNA which contains five logistic regressions and two sets of latent variables.
Students’ Learning Approaches in The EFL Emergency Online Learning Context

Emergency online learning has transformed today’s learning while opening up opportunities and challenges, one of which is learning approach. This study aims to investigate students’ learning approach in the English Education study program in emergency online learning. A mixed-method was employed using two instruments; R-SPQ-2F questionnaire and semi-structured interviews. The results of the instrument check showed that the R-SPQ-2F survey and interview guide were valid (.90) and reliable (α=.88). Calculated using Slovin formula, there were 302 respondents for this study. The survey data were analysed using R-SPQ-2F mean score analysis and interview data were with interactive model analysis. The results of the study indicate that the learning approach of students in the English education study program in the context of emergency learning tends to be deep. The interview results provide important information that the student approach is influenced by several supporting and inhibiting factors. This shows that the student learning approach is dynamic in adjusting to learning process. Policy makers, lecturers, and students need to consider aspects of this learning approach in the current emergency online situation for a more effective and meaningful learning process.
RETRACTED: Design of Double-effect Propulsion System for News Broadcast Based on Artificial Intelligence and Virtual Host Technology

Abstract A virtual host refers to a host who uses an avatar to submit contributions on a video website. The virtual host is a powerful combination of artificial intelligence technology and live broadcast. In recent years, virtual video hosts have sprung up on major video websites, and their popularity is growing rapidly. Virtual host technology has been applied to many areas of society. Based on this background, this article designs a news broadcast double-effect propulsion system based on artificial intelligence and virtual host technology. This article applies the virtual host technology to the field of news broadcasting, and realizes the double-effect advancement of virtual host technology and news broadcasting. The virtual host designed in this article mainly uses sign language to broadcast news, so this article first conducts a quick and brief knowledge of sign language, and then conducts a detailed analysis of the current research status of virtual host technology and artificial intelligence technology, and uses artificial intelligence. The technology realizes the motion control of the virtual host; then the intelligent algorithm is used to realize the superimposition and synthesis of videos, and a news broadcast double-effect propulsion system of artificial intelligence and virtual host technology is designed. Finally, this article carried out an experimental test on the system’s simultaneous broadcast function and channel switching function. The test results surface excluded subjective human factors. After the improvement, the system can achieve simultaneous broadcast and successful channel switching.