Reviewerly/exHarmony huggingface dataset:
Valency in the Urhobo Language

This paper examines verb valency in Urhobo, using minimalism as a theoretical framework. Verb valency deals with the question of how many participants a specific verb logically presupposes in order for the event denoted by the verb to be realizable. The method of data collection is categorized into two main sources: primary and secondary data. The preliminary data refers to the information obtained using intuitive knowledge, the secondary source refers to documented information obtained from the library, internet, and other published materials. The study reveals that where we have one argument structure, we have one theta function. There are two place predicates we have two theta roles or functions, and also, three arguments predicates possess three theta roles. This goes a long way to say that Urhobo verbs can take different arguments, and their syntactic and semantic well-formedness will still be intact. It also reveals that it takes only one individual to carry out one event, such as òvwèrẹ̀ (sleeping event) in the Urhobo language. Finally, the paper identifies three valency classes in the Urhobo language (Mono-valent verb- takes or involves one entity, Di-valent verb- takes or involves two entities, and Tri-valent verb- takes or involves three entities).
THE COMPETITION IN PRESCHOOL AGE: A SHORT REVIEW

&lt;p&gt;Competition matters in our daily life. Humans compete in their jobs, in their relationships, in the classroom etc. for different reason and with different way. We know a lot about competition for the adults but we lack of knowledge on what is happening during the preschool age and this is the focus of the present paper. The literature review shows that young children from the age of 4 years old perceive the concept of competition and express competitive behaviour. The factors which affect young children’s competitive behaviour are the gender, the age and the composition of the team as to gender and size. Moreover, during the implementation of the curriculum children express competitive behaviour in kindergarten classroom. They express competitive behaviours, which are divided into two main categories, verbally and physically, which include and subcategories. Competitive behaviour is expressed by children more often during organized activities and less during free activities, like breakfast time and discussion.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0870/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
When are Humans Reasoning with Modus Tollens

Author(s): Cramer, Marcos; Holldobler, Steffen; Ragni, Marco | Abstract: Modus tollens is a rule of inference in classical, two-valued logic which allows to derive the negation of the antecedent from a conditional and the negation of its consequent. In this paper, we investigate when humans draw such conclusions and what modulates the application of modus tollens. We consider conditionals which may or may not be obligations and which may or may not have necessary antecedents. We show that humans make significantly more modus tollens inferences in case of obligation conditionals and that the time to make a modus tollens inference is shorter than the time to answer ``nothing follows''. We illustrate how these differences can be modeled within the weak completion semantics.
Model-Driven Architectural Framework towards Safe and Secure Nuclear Power Reactors

Sensor-based digital systems for Instrumentation and Control (I&amp;C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns of various stakeholders. In this work, we integrate the development process of I&amp;C systems and the involvement of stakeholders within a model-driven methodology. The proposed approach introduces a new architectural framework that defines various concepts, allowing system implementations and encompassing different development phases, all actors, and system concerns. In addition, we define a new I&amp;C Modeling Language (ICML) and a set of methodological rules needed to build different architectural framework views. To illustrate this methodology, we extend the specific use of an open-source system engineering tool, named Eclipse Papyrus, to carry out many automation and verification steps at different levels of abstraction. The architectural framework modeling capabilities will be validated using a realistic use case system for the protection of nuclear reactors. The proposed framework is able to reduce the overall system development cost by improving links between different specification tasks and providing a high abstraction level of system components.
Podcasts: Improving Students’ Extensive Listening Skills

&lt;span lang="EN-US"&gt;Podcasts is one of the technologies that can be used as an effective media to teach students’ listening skill, especially extensive listening. This research aims to know the effectiveness of using podcasts application in teaching extensive listening. The method in this study used quantitative research. The design of this research was pre-experimental one group pre-test and post-test design. It involved the Eleventh Grade Students of SMA Negeri 2 Bangkalan as the subject of the research. In this research, the data were obtained toward the results of listening tests. The research was started by giving pre-test, treatments, and post-test to experimental group. The data of the tests were analyzed by using Paired Sample T-Test. The results showed the significant difference between pre-test and post-test, It indicate that the value of significance (Sig. 2-tailed) is 0.002 lower than 0.05. The results of the statistical hypothesis testing showed that the P-value lower than α (0.002&amp;lt;0.05), it means that Ha was accepted and Ho was rejected. It can be concluded that podcasts application is effective and brilliant media that can be suggested to be used on teaching listening.&lt;/span&gt;
Comparative Study of Scientific and Technological Outputs of the Islamic Republic of Iran and other Members of D8 Countries

The achievements of science and technology in today’s world, considered as base of countries’ development. The aim of this study was to investigate the situation of the Islamic Republic of Iran in terms of science and technology outages over a period of 20 years and compare it with Islamic countries that are members of the D8 group. This applied research performed by descriptive-analytical method with scientometric approach. The research community included the scientific and technological outputs of the Islamic countries of the D8 group, indexed at the Scopus database and the international databases of patent licenses for the period 1997-2017. The findings showed that Iran ranks second among the D8 countries in terms of the number of scientific outputs and citations after Turkey. In terms of technological outputs, Iran ranked second after Egypt, third after Turkey and Malaysia, and fourth after Turkey, Malaysia and Egypt, respectively in three international databases: USPTO, EPO and WIPO. In terms of technological outputs to scientific outputs, Iran did not have a good position among the eight countries and in this respect; it was even behind Pakistan, Bangladesh or Nigeria. In most of the countries studied, there was a positive and significant relationship between the number of scientific outputs and the number of citations, as well as technological outputs. However, the results showed that a large increase in the number of scientific outputs in Iran did not necessarily lead to an appropriate increase. Gives a gap between science and technology in the country.
Blockchain and sovereignty

The Chapter analyses the relationship between (permissionless public) blockchains and the notion of sovereignty, arguing that the blockchain technology is a potentially lethal challenge both for national sovereignties defended by sovereign states and for international organisations that aspire to acquire sovereignty, such as the European Union. The Chapter considers the two main dimensions of such challenge: the issue of public monopoly over money (purse) and on the ‘legitimate’ use of force (sword), that encompasses most of all some typical state roles such as control and validation functions, as well as the production of law and the provision of justice. The Chapter features some final remarks on the possible scenarios that lie ahead, which range from the extreme of the affirmation of state sovereignty at the expense of these technologies, which could even be banned or nationalized, to the opposite extreme of breaking up and superseding sovereignty, through various (but not easy to imagine) intermediate possibilities.
Blockchain and authoritarianism: The evolution of decentralized autonomous organizations

Though Blockchain technologies make traditional authoritarianism less likely, they make a new kind of authoritarianism, born of decentralized autonomous organizations (DAOs), more likely. Created to liberate computational systems from any and all human interference, DAOs have quietly been growing, thriving, and evolving in their natural environment - decentralized networks like the Internet or Ethereum. By design and accident, DAOs will tend to develop into the computational equivalents of eusocial colony animals such as ants, bees, and termites. Once formed into such superorganisms, DAOs will exhibit emergent behaviors like swarming and collective intelligence. Humans venturing into the DAOs’ native habitat would then find themselves forced to live under the arbitrary will of not another human, but instead of a vast, mysterious hoard of nonhuman, and perhaps inhuman, entities. This is not an outcome that traditional sources of public law - treaties, statutes, regulations, and court rulings - have the tools to prevent. The distributed nature of DAOs makes them inherently resistant to centrally planned attacks. To prevent DAO's from running wild will instead require correspondingly distributed defenses, taken by many various parties alone and coordination, that together render the Blockchain environment less susceptible to infection by programs that threaten to evolve into digital autonomous authoritarians.
Analysis Methods Used to Extract Fingerprints Features

The fingerprint is used in many vital applications important to humans, which requires searching for an effective way to extract the characteristics of the fingerprint. In this paper we will study some of the most popular methods used to extract fingerprints features. For each method the efficiency, accuracy, flexibility and sensitivity for image rotation will be experimentally tested, measured, analyzed in order to give good recommendations of how and when to use a certain method of features extraction. A detailed comparison analysis between MLBP, K_means, WPT, Minutiae methods will be done using several color images in various rotation modes to insure the stability of image features.
Fast and Privacy-Preserving Federated Joint Estimator of Multi-sUGMs

Learning multiple related graphs from many distributed and privacy-required resources is an important and common task in neuroscience applications. Medical researchers can comprehensively investigate the diagnostic evidence and understand the cause of certain brain diseases via analyzing the commonalities and differences of the brain connectomes predicted from the fMRI data across multiple hospitals. Previous sparse Undirected Graphical Model (sUGM) methods either cannot take full usage of the heterogeneous data while preserving privacy or miss the capability of handling the nonparanormal data, which is highly non-independent and identically distributed (non-i.i.d.). This paper proposes a novel and efficient approach, FEDJEM ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fed</u> erated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</u> oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> stimator of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> ultiple sUGMs), that trains the multi-sUGMs over a massive network encompassing various local devices and the global center. In order to efficiently process the datasets with different nonparanormal distributions, the proposed federated algorithm fully exploits the computing power of the local devices and cloud center while federated updates ensure that personal data remain local, thus defending the privacy. We also implement a general federated learning framework for multi-task learning based on our method. We apply our method on multiple simulation datasets to evaluate its speed and accuracy in comparison with relevant baselines and develop a strategy accordingly to balance its computation and communication abilities. Finally, we predict several informative groups of connectomes based on the real-world dataset.
Sentiment Analysis for Amazon Products using Isolation Forest

To text examination for efficiently recognize, evaluate, study full of affective states, extricate use of normal language preparing is known as Sentiment Analysis. The applications in which use advertising to client administration to clinical drug like applications use sentiment analysis on the web and web-based social networking, human services materials and audits and study reactions. Many sites like Amazon urged users to post the review of the product on its site. But Amazon provides the limit of content to post the reviews. For different applications the review helps to analyze the product although the review for several products will different. This research works on the data that is recovered from Amazon and apply and expand the present work in the field of sentiment analysis and natural language processing. The work uses Machine Learning algorithms and characterize into positive or negative surveys.
Rapid Evaluation of Organic and Pyritic Sulfur Content in Sedimentary Rocks by Rock-Eval 7S Analyser

Summary Evaluating individual sulfur (S) fractions in rocks (e.g., pyrite, organic-S) provides essential information about depositional environments and petroleum systems potential. However, the common method for quantifying individual S-fractions in rocks is highly time-consuming (weeks) and labor-intensive. A more tractable approach may be the pyrolysis of the rock and gradual release of S-fractions based on their thermal stability. We have developed a method to identify and quantify the organic and pyritic-S using the rapid analysis (∼one hour) of the new Rock-Eval 7S analyzer (RE-7S, Vinci Technologies) based on an empirical relationship. The empirical relationship was established on nine different source rocks and was confirmed on 11 more samples relative to reference data obtained by the traditional wet chemistry procedure. The precision of organic and pyritic S values achieved by the new method is between 2 to 8 % for an S-fraction above 0.03 wt. % (of the bulk rock), and requires ∼60 mg rock. The fraction separation accuracy was assessed relative to wet chemistry separation and is better than 10 %.
When does Synthetic Data Generation Work?

Synthetic data generation is one of the methods used in machine learning to increase the performance of algorithms on datasets. However, these methods do not ensure success on each dataset. In this study, it has been investigated that which type of synthetic data generation algorithms are useful in which datasets by examining the effects of SMOTE, Borderline-SMOTE and Random data generation algorithms on 33 datasets. For this, each dataset has been fully balanced as a result of synthetic data generation. In order to evaluate the results, datasets are divided into three groups as balanced, partially balanced-unbalanced and unbalanced in accordance with the unbalance ratio. The datasets formed as a result of the data generation of the algorithms and the original datasets have been trained with an ANN models and their performance has been evaluated on the test set. Experimental results have shown that adding synthetic data to the datasets with the abovementioned algorithms generally increases the success in balanced and partially balanced-unbalanced datasets, but generally does not work in unbalanced datasets. Borderline-SMOTE, which produces border samples in balanced datasets, and SMOTE in partially balanced-unbalanced datasets have been more successful.
Acciones educativas de la familia con el escolar durante el confinamiento por la COVID-19

Introduction: COVID-19 confinement has created a challenge in raising schoolers. Objective: Examine the performance of educational actions of the family with the school during the confinement by COVID-19. Methods: Descriptive research, non-probabilistic sampling, 112 families who complied with social isolation in Ramon Lopez Pena community, San Cristobal, Artemisa province in 2020. Instrument used: Scale of educational actions of the family with school children during confinement by COVID-19, built and validated by researchers with Cronbach´s alpha = 0.970. Results: Educational actions as meeting food and sleep schedules (70.6%), distributing roles in child care (70.6%), emotional support (70.6%), supporting with teleclasses (70.6%) were always carried out by families with favorable performance. Actions like helping to recover from negative emotions (72.7%), keeping the schooler away from household concerns and tensions (72.7%), explaining whether they don't understand the contents or looking for help (72.7%) were almost never made by families with unfavorable performance. Conclusions: Most families had a favorable performance in educational actions. The Child Care dimension was the best performed one. Teaching the schoolchildren the hygienic protective measures, provide information to schoolchildren on how to protect themselves, teaching and practicing ways to greet and stay connected with loved ones and friends were the most performed educational actions by both family groups. The complexity of home learning and the child's affective management require special attention.
An Effective Quasi Oppositional Social Spider Optimization Based Feature Selection with Pattern Matching Based Classification Model

Classification is a process of properly determining the targeted class for an unlabeled data instance by learning from the instances defined by a collection of features and a class label. The instance based classification model has gained significant attention because of its straightforwardness and effective outcome. But several models are highly sensitive to noise and become inappropriate for real time problems. This paper introduces an efficient Quasi Oppositional Social Spider Optimization (QOSSO) based Feature Selection (FS) with Pattern Matching based Classification (PMBC) model, named as QOSSO-PMBC. The presented model involves two major processes namely feature selection and classification. Primarily, the QOSSO based FS process is executed to determine an optimal set of features. In addition, the quasi oppositional based learning (QOBL) concept is introduced to the classical SSO algorithm with an intention of enhancing the convergence rate. Besides, the PMBC model is employed to identify the proper patterns and determine the class labels of the feature reduced dataset. The experimental results of the QOSSO-PMBC model are validated against two benchmark dataset namely contact lenses and diabetes dataset. The experimental values denoted that the QOSSO-PMBC model has resulted in a maximum accuracy of 91.61% and 95.34% on the applied contact lenses and diabetes dataset.
A Comprehensive Study on the Importance of the Elbow and the Silhouette Metrics in Cluster Count Prediction for Partition Cluster Models

Proper selection of cluster count gives better clustering results in partition models. Partition clustering methods are very simple as well as efficient. Kmeans and its modified versions are very efficient cluster models and the results are very sensitive to the chosen K value. The partition clustering algorithms are more suitable in applications where the data are arranged in a uniform manner. This work aims to evaluate the importance of assigning cluster count value in order to improve the efficiency of partition clustering algorithms using two well known statistical methods, the Elbow method and the Silhouette method. The performance of the Silhouette method and Elbow method are compared with different data sets from the UCI data repository. The values obtained using these methods are compared with the results of cluster performance obtained using the statistical analysis tool Weka on the selected data sets. Performance was evaluated on cluster efficiency for small and large data sets by varying the cluster count values. Similar results obtained from the three methods, the Elbow method, the Silhouette method and the clustering by Weka. It was also observed that the fast reduction in clustering efficiency for small changes in cluster count when the cluster count is small.
The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek

This paper describes the GLAUx project (“the Greek Language Automated”), an ongoing effort to develop a large long-term diachronic corpus of Greek, covering sixteen centuries of literary and non-literary material annotated with NLP methods. After providing an overview of related corpus projects and discussing the general architecture of the corpus, it zooms in on a number of larger methodological issues in the design of historical corpora. These include the encoding of textual variants, handling extralinguistic variation and annotating linguistic ambiguity. Finally, the long- and short-term perspectives of this project are discussed.
Extreme Face Inpainting with Sketch-Guided Conditional GAN

Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a conditional Generative Adversarial Network (GAN) that utilizes structural information, such as edges, as a prior condition. Edge information can be obtained from the partially masked image and a structurally similar image or a hand drawing. In our proposed conditional GAN, we pass the conditional input in every layer of the encoder while maintaining consistency in the distributions between the learned weights and the incoming conditional input. We demonstrate the effectiveness of our method with badly damaged face examples.