Reviewerly/exHarmony huggingface dataset:
Measuring the Level of Financial Development of U.E. Member Countries

Abstract A financial sector that is developed and well functional is a key component of an economy. Numerous articles in the literature study the influence of financial development on the poverty reduction or on the economic growth. However, this paper proposes to compare the level of financial development of EU member states, but also to discover a shortcut in defining the financial level of a country. The methodology that allows this is composed of three steps: creating a composite index based on the main principal components that measure the level of the financial system; creating a categorical variable based on the values of the index (financial developed countries have a positive index value and vice versa) and applying the Decision Trees algorithm to the extended dataset. The results of the study show an underdeveloped financial level for Romania, which is at the opposite pole from Luxembourg, the country with the highest level of the financial system. Among the definition patterns found, is the following condition: if the percent of accounts used to receive wages is greater than 49.74%, the saved using a savings club in the past year (%) is greater than 3.95%, the customer price index is greater than 106.99 and the debit card (%) is greater than 90.69%, then this indicate a good financial development level.
Cash Holdings And Corporate Performance: Evidence From Morocco

Recently, the controversy over companies' cash and cash equivalents has attracted more coverage in the business investment collected works. Business economics concepts propose conflicting theories on the correlation among money holding and business consequences. This analysis uses a section of businesses registered on the Moroccan Stock Exchange (MSE) in the 2007-2018 periods to empirically analyze the association among money and business results. This research shows that cash is positively linked to business results, adapting to overlooked heterogeneity and other characteristics of the business.
Technology - Robotics. Photo-Essay

The world has entered uncharted territory in its battle against the deadly coronavirus. Robots - or coronabots - have been employed to help keep humans as safe as possible, often in the most dangerous situations.
Copying the Human Voice

This lesson continues the quest for a more rhetorical approach to playing by investigating in fine detail not merely the rhythms but the actual sounds of words, their vowels, consonants, double consonants and dipthongs, as well as their meanings. Ways of replicating these sounds and effects with the bow are explored. The aim is to lay aside cherished principles regarding sound, rhythm, and phrasing and replace them by a pseudo-scientific method aimed at achieving a bow that speaks and obeys only the laws of spoken language. Instead of isolated words, the lesson’s focus is on a song in Italian, composed in 1612 by Sweelinck to be sung or played. There are five specially devised exercises for realizing the text in sound. Although the lesson works from a single line, the score on the website contains both parts: the song can thus be played by two violins or by one violin and a singer.
Mediating Effect of Employee Effort in Employee Informativeness and Performance

Contract Theory explains the problems that may arise in designing incentive contracts and the possible solutions for those problems. One of the problems mentioned to the principal agent contract problem is moral hazard and the reason for that problem is the lack of sufficient information. Employee Informativeness is suggested as a solution to alleviate the moral hazard problems which will help in inducing the effort decision in employee and thereby improves the performance. This paper attempts to check whether the employee informativeness influences the effort of the employee and thereby performance. Here effort is considered as a mediating variable in the relation between Employee Informativeness and Performance. The study is conducted among 117 sales personnel in Automobile dealer industry where incentives contribute as a major part of their compensation. The tools used in the analysis is Spss tools and process macro. The study reveals that Employee effort act as a mediator in the relation between employee informativeness and employee performance.
Communication and Education in a digital connected world. Presentation

The digital transformation builds societies based on participatory and connected logics where citizens become network users who participate on equal terms from mobile devices The increasingly naturalized virtual interaction, accentuated after the last events of global confinement by the Covid-19 pandemic, forces a continuous reflection on communication and education as determining factors in the advancement of society The question that emerges in the face of inexorable technological penetration and connectivity is whether they are carrying out the appropriate actions and practices for this resulting critical and democratic participation In this context, this monograph is presented where communication and education are intertwined to offer specific contributions in four areas: 1) communication addressed in the educational field: journalism in the classroom and transmedia skills;2) social networks as spaces for communication and education: use of Twitter and Instagram in an educational context, bots, misinformation, rumors and digital skills;3) new connected generations, and 4) emerging actors in communication and education: youtubers, OTT platform gamers and family responsibility
Machine Learning Approach for Breast Cancer Early Diagnosis

Machine learning has been the most recommended approach for predicting the early detection of breast cancer. With the availability of datasets that have details about the features extracted from the mammograms as well as other imaging techniques, the modeling and training can aid in the early detection of breast cancer. Such early diagnostic strategies can focus on providing timely access to cancer treatment thereby improving the quality of life of these cancer patients. This chapter is aimed at presenting an early breast cancer prognosis by using classification approach with ten different machine learning techniques namely Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naive Bayes (NB), Adaboost Tree, Support Vector Machine (SVM), Radial Basis Function (RBF), Quadratic Discriminant Analysis (QDA) and Multilayer Perceptron on the Wisconsin Breast Cancer (original) dataset The dataset features have been categorized into three sub-classes as mean, standard error, and worst to compute the performance parameters using simulation tool Jupyter notebook. Based on comparative analysis the Support Vector Machine (SVM) exhibiting around 95 percent accuracy and most optimized recall value has been presented as the choice for modeling. The model is validated with minimum false-positive results for the testing dataset.
An Efficient Questionnaire based system: Problems with Composing English Skills to Write a Research Paper

Wale, B. D., et.al. (2021) English is the common global language for communication purpose in universal. Due to this it cannot be ignored or denied by any academician and research publications with various domains such as life science, engineering and technology. The importance of English in writing research papers is that it makes successful paper publications in reputed international journals which improves the profile of individual researcher and academic institutions [1].
Pilot3 - Software engine model for multi-criteria trajectory optimisation

Pilots should have on board a better understanding of the stakes of the current mission - from the perspective of the airlines objectives.Pilot3 will develop a software engine model to provide this support performing multi-criteria optimisation on trajectory optimisation.
Further Notes on Intervention Effects on Reconstruction

The main purpose of this paper is to examine the domain and properties of reconstruction of long-distance scrambled phrases in Korean. We argue that Beck & Kim’s (1997) intervention effect can be extended to reconstruction cases, which corroborates the conclusion made by Bae (2020). The main evidence comes from the data showing that long-distance scrambled phrases (outside their base positions) can interact with the syntactic operations such as binding and also affect semantic interpretation of idioms. In particular, we show that the interpretive option for anaphors and idioms is determined by how far reconstruction can be implemented. The result further suggests that binding condition A is not a derivational condition in Korean (contra Belleti & Rizzi 1998).
Multi-Objective Adaptable Atmospheric Light and Depth Map Quantization Level Dehazing With CSA

Outdoor natural images captured in turbid weather are subject to produce low visibility which may cause fatal problem in computer vision applications. Single image visibility improvement is the most difficult of all model used in visibility improvement and mostly depends on restoration based optical image formation model. Existing dehazing techniques estimate atmospheric light (AL) and transmission by some prior information. In this work, we examined the strength of the Cuckoo Search Algorithm(CSA) in tuning a new multi-objective image performance function PCS for adaptable dehazing which estimates and balance between noise, contrast and geometrical information of image . Two parameters, AL and Otsu’s quantisation thresholding level in Depth Map (DM), are optimised with levy steps in search space of CSA to produce best dehazed image . GT O-Haze, DerainNet, Frida synthetic dataset have been used for evaluations and our method is compared with ten state-of-the art methods qualitatively and qualitatively for dehazing obtaining satisfactory results.
Let’s Play with Our Numbers!

In the previous chapter, I gave you a brief introduction to using numbers in Python, creating, and storing them and the different types of numbers you can play around with.
Exploring the Grey Area of Cyber Security in Industrial Control Systems (ICS)

Building on Turner’s contribution and qualitative methods, this chapter explores a new research field for organisational scholars of hazardous systems and qualitative methods: securing production processes and technologies against malicious cyber-attacks that arise in the global network. Organisations face the possibility of being attacked on a daily basis and often delegate the work of securing Industrial Control Systems (ICS) to technical artefacts or experts. This chapter explores how risk is socially performed in such socio-technical systems, and how the work of cyber security managers, business managers, IT specialists, as well as general employees, who work on security is shaped by organisational social habits. Taking a multifaceted qualitative research approach allows the exploration of these hidden areas, and the chapter concludes with a suggestion to adopt a metaphorical analysis through consideration of novel processes like “neglect,” “oblivion” and “confinement,” which, among others, may be useful metaphors of organisational choices in the context of cyber security.
Text Sentiment Analysis Using Artificial Intelligence Techniques

In today’s world, data is being generated with such high velocity and variety that analyzing such large volumes of data to extract meaningful results is a taxing job and manually impossible. Developing methods to analyze such large volumes of data for the purpose of finding hidden patterns to achieve meaningful interpretations is necessary for many organizations for making informed decisions. Sentiment analysis is one such method that is used to interpret the emotions represented by text data. There exists a broad range of applications for sentiment analysis. Public opinion is an important “business insight”. All businesses are interested in analyzing the consumer behavior, understanding their needs, understanding their likes and dislikes and their buying patterns, and as more and more people are becoming vocal about their preferences, the data required by these companies is becoming readily available on blogs and social media platforms, but the analysis of such large amount of raw data to derive useful conclusions is a hectic task if tried to perform manually. In such cases, sentiment analysis can be used to analyze this raw data. Sentiment analysis can be used for a variety of needs ranging from understanding the public opinion of a government policy to assigning movie ratings from analysis of viewer ratings. This method is especially an important factor in social media monitoring to gain a wider public opinion about a topic. NLP tools available today can be used to efficiently analyze this raw text and classify texts as positive, negative, or neutral. Different machine learning algorithms like random forest, logistic regression, and support vector machine can be used to train the model using the features extracted by the NLP techniques. The trained model can then be used to predict the polarity of the raw input data. ROC and PR curves have been plotted to check the accuracy of the algorithms.
CSFQGD: Chinese Sentence Fill-in-the-blank Question Generation Dataset for Examination

Fill-in-the-blank question generation has become enormously popular and attracted lots of attention recently. However, most of the existing question generation datasets are developed for machine reading comprehension, which are not specifically designed for examination. To fill in the gap, in this paper, we propose a Chinese sentence fill-in-the-blank question generation dataset for examination (named CSFQGD), which will be released to the public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Resources are available at The dataset is composed of 20.5K questions from many real examinations in Chinese that cover a wide spectrum of learning subjects. Based on the proposed dataset, we test several well-known methods for fill-in-the-blank question generation and compare their performance. Our baseline study on this dataset shows that CSFQGD is a challenging test bed for further research.
Translation from everyday to algebraic languaje through a game: a classroom expierence

In this work we present the analysis of an experimentation, focused on the translation from everyday language to algebraic language, which was carried out in a class of the 3rd year of the ESO (the Spanish compulsory high school). The goal of the project was to promote the translation from one language to the other, using a mathematical board game. The original board game, designed by the Spanish teachers group Grupo Azarquiel, was modified for analysis purposes and to succesfully integrate a student with learning difficulties in the flow of activities of the class. The obtained results were positive in terms of both mathematical learning and personal and social development
Overview of NASA QuAIL Team Research

NASA is constantly confronting massively challenging computational problems. These problems have the potential to limit mission scope and aim. NASA's Quantum Artificial Intelligence Laboratory (QuAIL) Group was formed to determine the potential for quantum computation to enable more ambitious NASA missions in the future. This presentation highlights recent work by the QuAIL team that is focused on physics insights and applications associated with the D-Wave 2000Q quantum annealing hardware that is located at NASA Ames Research Center.
Preserving Image Encryption for Smart Android System

This paper proposes an image encryption technique for smart phones devices to encrypt images and then upload on the cloud. The process of retrieving and decrypting images will be under full control by the user. Also, if the user wants to retrieve an image, he can retrieve it completely, but not as he needed. Our research can solve two main issues in current mobile applications which will protect photo that will be decrypt by the user only, and will help the user to recover the photo needed. An encryption mechanism based on hybrid between elementary cellular automata (ECA) and RC4 algorithms are used to encrypt images on the smart android mobiles. Experimental results on the application showed that our proposed encryption technique used for image encryption has a good quality en cryption with secure requirements.
Introducing Information Retrieval for Biomedical Informatics Students

Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs.We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT.These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification

Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph based data. A question that arises is how much performance improvement does the underlying graph structure in the GNN provide over the CNN (that ignores this graph structure). To address this question, we introduce edge entropy and evaluate how good an indicator it is for possible performance improvement of GNNs over CNNs. Our results on node classification with synthetic and real datasets show that lower values of edge entropy predict larger expected performance gains of GNNs over CNNs, and, conversely, higher edge entropy leads to expected smaller improvement gains.
A$$^*$$-Based Compilation of Relaxed Decision Diagrams for the Longest Common Subsequence Problem

We consider the longest common subsequence (LCS) problem and propose a new method for obtaining tight upper bounds on the solution length. Our method relies on the compilation of a relaxed multi-valued decision diagram (MDD) in a special way that is based on the principles of A $$^*$$ search. An extensive experimental evaluation on several standard LCS benchmark instance sets shows that the novel construction algorithm clearly outperforms a traditional top-down construction (TDC) of MDDs. We are able to obtain stronger and at the same time more compact relaxed MDDs than TDC and this in shorter time. For several groups of benchmark instances new best known upper bounds are obtained. In comparison to existing simple upper bound procedures, the obtained bounds are on average 14.8% better.
Prospective Evaluation of Multiplicative Hybrid Earthquake Forecast Models for California

&amp;lt;p&amp;gt;Developing testable seismicity models is essential for robust seismic hazard assessments and to quantify the predictive skills of posited hypotheses about seismogenesis. On this premise, the Regional Earthquake Likelihood Models (RELM) group designed a joint forecasting experiment, with associated models, data and tests to evaluate earthquake predictability in California over a five-year period. Participating RELM forecast models were based on a range of geophysical datasets, including earthquake catalogs, interseismic strain rates, and geologic fault slip rates. After five years of prospective evaluation, the RELM experiment found that the smoothed seismicity (HKJ) model by Helmstetter et al. (2007) was the most informative. The diversity of competing forecast hypotheses in RELM was suitable for combining multiple models that could provide more informative earthquake forecasts than HKJ. Thus, Rhoades et al. (2014) created multiplicative hybrid models that involve the HKJ model as a baseline and one or more conjugate models. Particularly, the authors fitted two parameters for each conjugate model and an overall normalizing constant to optimize each hybrid model. Then, information gain scores per earthquake were computed using a corrected Akaike Information Criterion that penalized for the number of fitted parameters. According to retrospective analyses, some hybrid models showed significant information gains over the HKJ forecast, despite the penalty. Here, we assess in a prospective setting the predictive skills of 16 hybrids and 6 original RELM forecasts, using a suite of tests of the Collaboratory for the Study of Earthquake Predicitability (CSEP). The evaluation dataset contains 40 M&amp;amp;#8805;4.95 events recorded within the California CSEP-testing region from 1 January 2011 to 31 December 2020, including the 2016 Mw 5.6, 5.6, and 5.5 Hawthorne earthquake swarm, and the Mw 6.4 foreshock and Mw 7.1 mainshock from the 2019 Ridgecrest sequence. We evaluate the consistency between the observed and the expected number, spatial, likelihood and magnitude distributions of earthquakes, and compare the performance of each forecast to that of HKJ. Our prospective test results show that none of the hybrid models are significantly more informative than the HKJ baseline forecast. These results are mainly due to the occurrence of the 2016 Hawthorne earthquake cluster, and four events from the 2019 Ridgecrest sequence in two forecast bins. These clusters of seismicity are exceptionally unlikely in all models, and insufficiently captured by the Poisson distribution that the likelihood functions of tests assume. Therefore, we are currently examining alternative likelihood functions that reduce the sensitivity of the evaluations to clustering, and that could be used to better understand whether the discrepancies between prospective and retrospective test results for multiplicative hybrid forecasts are due to limitations of the tests or the methods used to create the hybrid models.&amp;amp;#160;&amp;lt;/p&amp;gt;
Individual leaner differences: The role of motivation in teaching foreign language

The article examines role of motivation in teaching foreign language. Having effective methods are not enough to increase leaners’ acquisition. Different factors influenced by nature and formed by nurture. The following paper studies how motivation has impact on learners’ foreign language acquisition, the relationship of motivation, cognition, and emotion, and analyzes the role of motivation in teaching foreign language.
Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

For a nonparametric Behrens-Fisher problem, a directional-sum test is proposed based on division-combination strategy. A one-layer wild bootstrap procedure is given to calculate its statistical significance. We conduct simulation studies with data generated from lognormal, t and Laplace distributions to show that the proposed test can control the type I error rates properly and is more powerful than the existing rank-sum and maximum-type tests under most of the considered scenarios. Applications to the dietary intervention trial further show the performance of the proposed test.
Interorganizational Information Systems: A Study of Practice

Interorganizational relationships are initiatives between organizations that aim to facilitate resource sharing and information exchange. In this context, information systems are developed to support the involved organizations, often encompassing several smaller systems. However, little is known about interorganizational information systems (IOIS), especially regarding their state of practice. This paper investigates the use of IOISs in real scenarios. For this, we performed a non-systematic search of IOISs examples and identified repositories on GitHub about these systems. Hence, it was possible to map a set of sources and requirements that help understand how IOISs work and why they are adopted.
Shared mental models for cooperative fault diagnosis: effects on communication and diagnostic performance

To diagnose faults in a process plant, teams of physically separated operators must work together. They have access to differing information sources and need to rely on verbal communication, which ...