Reviewerly/exHarmony huggingface dataset:
Political Instability and Economic Growth in Ethiopia: An Empirical Analysis

This paper analyzes the effect of political instability on economic growth in Ethiopia since 2004 using ARDL regression approach to identify the existence of this correlation and the channels through which this impact happened. The paper found that political instability has a significant effect on economic growth and this impact is more significant through the direct channels more than the indirect effects through its effect on economic environment or investment and financial flows.
UAV dynamic obstacle avoidance based on improved reciprocal velocity obstacle

Abstract This paper aims to solve the problem that when the UAV flies from the starting point to the target point in the velocity space, the obstacle avoidance speed provided by the mutual velocity obstacle method will appear too large turning angle in order to avoid dynamic obstacles. In order to better meet the flight performance of UAV, an improved mutual velocity obstacle method is proposed in this paper. This method can not only solve the problem of path jitter caused by the speed obstacle method, but also solve the problem of excessive turning angle when the velocity direction selected by the mutual velocity obstacle method is flying towards the target point. In addition, the UAV will not return to the original path after the obstacle avoidance, but fly along the target point according to the updated best speed direction, so as to reduce the number of turns. The simulation results show that the improved mutual velocity obstacle method can effectively reduce the turning angle of UAV in dynamic environment and meet the flight performance of UAV.
Intelligent Decision Analysis of Converter Station SER Based on FP-Growth Algorithm

In order to improve the efficiency of operation and maintenance personnel in the research and judgment of the massive SER data generated by the converter station protection and enhance the converter station protection capability, a method based on FP-Growth algorithm for intelligent Decision Analysis of Converter Station SER. A multi-dimensional model is established for SER when converter station fails using data cleaning and association rule mining methods, and FP-Growth algorithm is used for data mining and analysis to obtain SER fault credible group. Then use SER fault credible group to determine the probability of SER abnormality. It facilitates the operation and maintenance personnel of the converter station to discover the abnormal operation of the converter station equipment in time, effectively improves the reliability of the converter station, and reduces the possibility of missed and misread events caused by manual interrogation of SER. Through analysis of the real SER data of the Kun-Bei converter station, it shows that the proposed method can effectively mine SER anomalies and improve the reliability of the converter station.
Improved Image Super-Resolution Using Frequency Channel Attention and Residual Dense Networks

Abstract In our real life, due to the various influences, low-resolution images exist widely. The resolution of the image represents the amount of information carried by the image and the quality of the image. Image super-resolution means reconstructing low-resolution images, and it can help us improve the image quality to get more information. This paper attempts to improve the existing super-resolution reconstruction model based on deep learning. We use nested residual dense connection to prompt the model to focus on the recovery of detailed textures and accelerate convergence. Meanwhile, we use frequency channel attention mechanism to weight channels. By comparing the experimental results with other methods including FSRCNN, VDSR and MemNet, our proposed method has achieved better results and visual improvements. Especially on the Urban100 test dataset, the increases of PSNR and SSIM reach higher.
Are Edge Weights in Summary Graphs Useful? - A Comparative Study

Which one is better between two representative graph summarization models with and without edge weights? From web graphs to online social networks, large graphs are everywhere. Graph summarization, which is an effective graph compression technique, aims to find a compact summary graph that accurately represents a given large graph. Two versions of the problem, where one allows edge weights in summary graphs and the other does not, have been studied in parallel without direct comparison between their underlying representation models. In this work, we conduct a systematic comparison by extending three search algorithms to both models and evaluating their outputs on eight datasets in five aspects: (a) reconstruction error, (b) error in node importance, (c) error in node proximity, (d) the size of reconstructed graphs, and (e) compression ratios. Surprisingly, using unweighted summary graphs leads to outputs significantly better in all the aspects than using weighted ones, and this finding is supported theoretically. Notably, we show that a state-of-the-art algorithm can be improved substantially (specifically, $$8.2{\times }$$ , $$7.8{\times }$$ , and $$5.9{\times }$$ in terms of (a), (b), and (c), respectively, when (e) is fixed) based on the observation.
Knowledge Sharing in Small Medium Enterprises: A Bibliometric Analysis

Knowledge sharing is an important dimension of knowledge management that has been researched and tested as an important driver of competitive advantage.However, research on the concept of knowledge sharing is still not carefully explored, disorganized and only a collection of vague ideas separated from several fields of science.The present study aims to measure trends in knowledge sharing carried out for SME development.Using a bibliographic mechanism, this study identifies keywords about to knowledge sharing to find and identify related articles in the Scopus database of 500 articles published from 2010 to 2021.The research findings reveal that the visual trend of increasing the number of publications over the past eleven years, article subject area, keywords, and authorship networks.This literature review provides evidence that research trends regarding knowledge sharing are becoming an important topic that can provide theoretical and practical implications.
RANKINGS AS A TOOL FOR ADAPTING VOCATIONAL EDUCATION TO THE CHALLENGES OF POST-INDUSTRIAL EMPLOYMENT

Organizational and technological changes taking place against the background of a change in the technological structure have a profoundly transformative effect on the entire spectrum of economic relations, including their socio-labor component; accelerate the processes of restructuring the labor market; set the need to adapt existing public institutions to work in new conditions. Analyzing the dynamics and directions of the development of higher education as a key subject of the system of vocational training, the author turns to the study of the phenomenon of international university rankings, which act as the subject of this study. The relevance of the chosen research perspective is explained by a number of reasons: in just a few decades since its inception, the ratings have successfully integrated into the educational systems of various countries and have found their own niche in this field. The expert community has confirmed the opinion that the entry of universities into international rankings provides countries with opportunities for the formation of world-class labor resources. All this, on the one hand, allows us to consider rankings as a fullfledged subject of the modern system of vocational education. On the other hand, the attitude to rankings as a socio-economic and, partly, cultural phenomenon, both in the public and in the expert environment remains ambiguous. Researchers pay attention to serious flaws in ranking projects, imbalances introduced by participation in rating "competitions" for individual countries and economic systems. The aim of the paper is to study the impact of international university rankings on the state and prospects for the development of university education as the basis of the current system of professional training. The article analyzes the reasons and prerequisites for the emergence of higher education rating practices. The strengths and weak- nesses of the most popular rating projects are evaluated. Particular attention is paid to the study of the practices of participation of the national system of vocational education in rating projects. In this context, the author identifies two areas of development: the creation and promotion of their own rating projects in the global market of educational services, as well as initiatives related to the inclusion of rating projects in national development programs. The author comes to the conclusions about a certain reassessment of priorities in the development of modern university rankings, outlines the further development of the rating movement in the field of education. The methodological basis of the study was institutional, systemic approaches and methods, means of content and comparative theoretical analysis. The results of the study expand the understanding of the phenomenon of international university rankings, which can be used when forming the social policy of the state, programs for the development of higher professional education, as well as for the purposes of harmonizing relations between the labor market and the educational services market.
Unsupervised Learning Methods

So far, we’ve discussed several solutions for either predicting a continuous variable based on given independent variables or predicting what class or classes a particular data item belongs to. We also discussed a few methods to combine multiple models to create a more effective meta-model. All these methods require a training dataset consisting of the values or labelled that are expected as the predicted output, thus, these leading to the name of supervised learning. In this section, we will discuss a different class of machine learning problems and solutions in which rather than predicting, the aim is to either transform the data or discover patterns without the need of a set of explicit training labels.
Finding the Missing Link: How Cataloging Bridges the Gap Between Libraries and the Internet

Information from government sources is being added to the Internet at an ever-increasing rate. Catalogers at Penn State are working with AACR2, OCLC’s Internet Cataloging Project (Intercat), and the creators of the Penn State Libraries’ World Wide Web 246Home Page to include both Internet sites and electronic publications in the library’s online catalog. The use of cataloging records to show relationships between Internet resources and the printed materials that they supplement or replace is demonstrated. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Approach For Segmentation of Masses in Mammographic Images Based on A Change in Grow Cut

Throughout the world, breast cancer is common cancer in women that contribute to high death amongst women. The early diagnosis and corresponding treatment can increase the possibilities of survival. In contrast, the challenging task is to detect the mass early in mammographic images, which is difficult due to noise and contrast. Mammography is the most capable technique used by radiologists frequently, which helps detect abnormal mass at an early stage; it is one of the methodologies to identify breast cancer. Here, a system is used to detect the tumor, with a modification in the grow cut algorithm. A new method is suggested based on segmentation by changing the modified grow cut algorithm by improving the region of interest. A modified grow cut algorithm has changed seed selection in mammogram images from manual into the semiautomatic way and has worked on non-defined borders. The earlier algorithm grows the region of interest only for the neighbor of current pixels and, consequently, a neighbor. Still, a change is made in the methodology for growing the region of interest within the class and between a neighbor cell's neighbors. This will ensure to get a more effective segmented area for abnormal mass than the previous method. The proposed technique is evaluated with the help of the mini M.I.A.S. database by considering circumscribed lesions, speculated lesions. Through result analysis, it is clear that the proposed technique gives better results for speculated, circumscribed lesions based on a comparison of ground truth images and segmented results.
The Role of Relationship Quality for Solitude Experiences during the Pandemic

Abstract As time spent at home has significantly increased during the pandemic, reports of household conflict has also risen among people living with others (Usher et al., 2020). One solution to alleviate the potential stress of increased time with others could be carving out time to oneself. The present study investigated how living conditions (e.g., with others vs. alone) are associated with everyday desire for solitude and whether daily solitude experience comes with improved daily emotional well-being in people living with others. Furthermore, it also explored whether relationship quality is associated with solitude experience in a similar manner as living conditions. To do so, we used repeated daily life assessments from a lifespan sample (N = 215; M age = 38.3 years, SD age = 17.5; 78 % female) collected during the early pandemic (April to August 2020). Findings indicate that neither living conditions nor relationship quality were directly associated with daily desire for solitude, but higher relationship well-being was related to low preference for solitude when measured as an individual trait. In addition, relationship quality significantly moderated everyday solitude–affect links: higher relationship quality was related to reduced negative affect during solitude, and conflict was related to increased positive and decreased negative affect on solitude as compared to non-solitude days. The results imply that it is the subjective experience of relationships rather than objective living conditions that shape daily affective quality during solitude.
Pakistani News Classification Based on Headlines

In information dissemination, media plays a vital role, a huge amount of information is spread quickly through social media and news platforms. The online news outlet and frequency of the huge number of headlines creation demand automatic classification tools. In this overwhelming domain, different countries explored classification algorithms for their news. However, very limited research is done on Pakistani news headlines in terms of classification. There is also a lack of a Pakistani news headlines benchmark dataset. This research is intended to find a suitable model to classify Pakistani news headlines automatically. Moreover, in this study, we designed a benchmark NCE-2D (News Classification and Emotion Detection Dataset) of 2429 Pakistani news headlines extracted from different news websites using ParseHub, which includes five types of categories. First, to minimize the noise from the dataset is undergoing some pre-processing steps including punctuation, stopwords, null entries, and duplication removal. In the next step, extract the feature by two different methods Count-Vectorizer and TF-IDF Vectorizer, and then apply a set of 10 different learning algorithms for both types of features extracted. The best performing algorithms for Pakistani news headlines out of the set of implemented algorithms by both combinations included Support Vector Machine (SVM) when implemented over the TF-IDF features and Multi-Layer Perception (MLP) when implemented over the CV features. Based on the results, both these algorithms are compared to choose the most suitable from them. The combination of SVM is investigated most appropriate classifier with 82.16% accuracy than 81.75% accuracy of MLP.
A Learning-Based Scheduler for High Volume Processing in Data Warehouse Using Graph Neural Networks

The process of extracting, transforming, and loading (also known as ETL) of a high volume of data plays an essential role in data integration strategies in data warehouse systems in recent years. In almost all distributed ETL systems currently use in both industrial and academia context, a simple heuristic-based scheduling policy is employed. Such a heuristic policy tries to process a stream of jobs in the best-effort fashion, however, it can result in under-utilization of computing resources in most practical scenarios. On the other hand, such inefficient resource allocation strategy can result in an unwanted increase in the total completion time of data processing jobs. In this paper, we develop an efficient reinforcement learning technique that uses a Graph Neural Network (GNN) model to combine all submitted tasks graphs into a single graph to simplify the representation of the states within the environment and efficiently make a parallel application for processing of the submitted jobs. Besides, to positively augment the embedding features in each leaf node, we pass messages from leaf to root so the nodes can collaboratively represent actions within the environment. The performance results show up to 15% improvement in job completion time compared to the state-of-the-art machine learning scheduler and up to 20% enhancement compared to a tuned heuristic-based scheduler.
Photovoltaic power prediction model based on empirical mode decomposition-long-short memory neural network

The permeability of photovoltaic power supply in the power grid is constantly improving. Accurate short-term photovoltaic power generation prediction is conducive to ensuring the safe and stable operation of the power grid connected with high-ratio photovoltaic power supply. It is necessary to further improve the prediction accuracy of photovoltaic power in the process of power development. Photovoltaic output power will be affected by weather conditions and seasons, and it is easy to change with external factors, showing volatility. Therefore, with the introduction of a variety of influencing factors and increasing reference data, accurate and efficient short-term photovoltaic power generation prediction is conducive to ensuring the safe and stable operation of the power grid connected with high-ratio photovoltaic power supply. In order to further improve the prediction accuracy of photovoltaic power generation, this paper adopts a short-term rolling prediction method of photovoltaic power based on empirical mode decomposition and deep learning. A photovoltaic power prediction model combining empirical mode decomposition with long-term and short-term memory networks is proposed. The error comparison between BP, SVM and LSTM models and EMDLSTM model shows that EMD-LSTM has the highest prediction accuracy. The results can further improve the accuracy of photovoltaic power prediction.
Converting cumulative grade point average to an equivalent percentage value based on a fuzzy logic

The graduates who have finished their study program will be given a merit award and their award certificates will be graded in accordance with the degree of their academic accomplishment. The awards are generally offered using two methods; one is by the cumulative grade point average (CGPA) and the other is by the average percentage of all marks for the students. The problem is when assigning a course final grade; each student's final percentage is translated to a letter, allowing the discrepancy within the same letter grade range in the final ranking. If two students have the same final score, that means equal results. However, this equality can be false if one student hits a percentage of the highest grade, while the second student earns a percentage of the lowest grade of the same letter grade. This paper introduced a new equation that transforms between the awarded cumulative grade point average and the awarded percentage ranking based on fuzzy system. The proposed approach was tested using three actual benchmarks collected from three different colleges in Beni-Suef university. The obtained results reflects the effect of the fuzzy logic in helping converting form CGPA measures to percentage measure in educational systems.
Vers une datation de l’instrument de musique par la mesure vibro-acoustique ? Étude d’un corpus de violoncelles du Musée de la musique

Seen from afar, what looks more like one violincello than another violincello?And yet, looking more closely, dimensions and curves, either more or less soaring, varnish colors, surfaces of the f-holes: these are so many descriptors that might allow us to attribute an instrument to a historical school of workmanship, as well as to associate an instrument of an unknown workman to a musical period.Beyond this geometrical data, musical instruments, even if for the most part they are not preserved in a playable state, carry within themselves intrinsic information about their acoustic potential: their vibrational responses to a slight impulse, called impulse response.The latter can be considered as the mechanical signature of the musical instrument insofar as it is unique to each and depends on several different parameters: geometry, assemblage procedures, nature of the raw materials and state of preservation.This paper will show the relevance of using simple statistical tools, connecting them with geometrical, vibratory, historical and geographical data of large corpuses of heritage musical instruments.It will thus offer a new perspective on the history of cultural, material, and immaterial values.Applied to a corpus of 48 violincellos of the Museum of Music, produced between 1750 and 1900 in different workshops in Europe, the methodology presented provides coherent preliminary results as regards the possibility of using geometrical and vibratory data to identify the period and workshop of production of a given instrument, while also providing information on the dating of material transformations that the instrument in question underwent before coming down to us today.
How can makerspace contribute to training for complex technological innovation - the case of aviation-based training for surgery robotics

The aim of this study was to explore the learning environment for robotic simulation and the potential of makerspace to contribute to professional practice and ongoing professional development for students and educators. Empirically, we base our suggestions on experience of simulation-based training implemented in the Stan Institute of robotic surgery training in the city of Nancy, France. Based on the results obtained from this study, the makerspace approach was identified as a promising solution for students and trainers to overcome challenges in relation to skill acquisition and assessment of student competencies in a multimodal technological environment.