Reviewerly/exHarmony huggingface dataset:
FROM BRAIN DRAIN TO BRAIN CIRCULATION: BRAIN POWER IN REGIONAL DEVELOPMENT

In this study, the relationships between brain drain, brain circulation and development phenomena are tried to be analyzed theoretically through various samples.One of the main problems of low-welfare societies is that people with high levels of human capital migrate to countries with high levels of welfare for many reasons.Although this form of migration brings some benefits to the source countries, it causes the further development of the target countries and offers them the main contribution.With the brain drain, the development gap between countries is getting deeper.In this context, there is usually no linear relationship between the development of the source country and the brain drain.But although brain drain is still widespread, the phenomenon of brain circulation instead of brain drain is becoming debatable within the framework of the erosion of borders and the policies developed by the source country, and there is a more optimistic view of brain circulation compared to brain drain.While brain drain causes the difference between countries to grow, according to some points of view, brain circulation can minimize this difference.When qualified programs that encourage return are accomplished, the benefits of brain circulation come to the forefront in terms of the source country.There are many programs for brain circulation between countries.While some of these programs are aimed at the mobility of the highly skilled workforce, some cover the mobility of students and staff for higher education.Intercollegiate cooperation on this issue is an important component of brain circulation.It is believed that individuals who raise their human capital through this mobility can contribute to the development of their country.
Uptake: ¿entender o aceptar?

Austin introduces the idea of securing the uptake in the context of dealing with the illocution-perlocution distinction. In recent times, the notion is employed by some neoaustinian scholars to argue that the uptake is what triggers the deontic effects (rights, duties, obligations, permissions, etc.) associated to an illocution. Here, a distinction is made between two kinds of uptake: uptake-as-understanding and uptake-as-accepting, and the stance that the second is the one needed for a plausible theory of speech action inspired by Austin’s original ideas is defended. When that notion is adopted, some old problems about speech action can be clarified.
A Study on Experience of Transition to Online Lecture due to COVID-19 in Nursing Students

The purpose of this study was to provide fundamental data for the effective management of online lectures in the future by investigating the difficulties in online lectures experienced by nursing students during the COVID-19 pandemic.For this purpose, the data were extracted from the experiences of nursing students of the online lecture through focus group interviews, using the classification and analysis scheme proposed by Colaizzi.The subjects were 20 nursing students who experienced the transition from face-to-face lectures to online ones.In the analysis, 17 theme clusters and five categories were extracted: "freely adjustable lecture", "Decreased learning capability", "Unmanaged video material", "Loneliness of learning alone", and "Difficulty in forming a relationship in school life".In conclusion, it was found that online lectures conducted to ensure safety from the sudden COVID-19 pandemic had the advantages of flexibility in terms of time and speed, however, students also suffered from negative experiences such as decreased learning capability, absence of instruction equipment, difficulty in communication, and loneliness and solitude.The quality management of video lectures and improvement in communication between teachers and students are required for effective learning in a continuous online lectures in the future and the development of teaching and learning methods to overcome those difficulties are recommended.
Global Trends of Computational Fluid Dynamics to Resolve Real World Problems in the Contemporary Era

Background: Computational fluid dynamics (CFD) came into existence with great success, thereby replacing the traditional methods used to simulate the problems related to the flow of fluid. First CFD utilitarian was introduced to the world in 1957, which was developed by a team at Los Alamos National Lab. For tremendous performance and to meet the expected results with ease for modern process conditions, engineers are now more inclined towards the use of simulation software rather than traditional methods. Hence, in the current scenario with the advancement of computer technologies, “CFD is recognized as an excellent tool for engineers to resolve real-world problems.” Introduction: CFD is defined as a branch of fluid dynamics which involves the use of numerical analysis and data structure to solve complications related to the flow of fluids (gasses or liquids). CFD is based on three major principles that are mass conservation, Newton's second law, and energy conservation. CFD has extended to a number of applications at an alarming rate in every field such as in aerospace, sports, food industry, engineering, hydraulics, HVAC (Heating, Ventilating, and Air conditioning), automotive, environmental, power generation, biomedical, pharmaceutical, and many more. Hence, a number of software like ANSYS, Open Foam, SimScale, Gerris, Auto desk simulation, Code_Saturne, etc, are beneficial in order to execute the operations, and to find the solution of realworld problems within a fraction of seconds. Methods: CFD analysis involves three major steps; pre-processing, solution, and post-processing. Preprocessing deals with defining model goals, identification of domain, designing, and creating the grid. Solution involves setting up the numerical model, computing, and monitoring the solution; whereas, post-processing includes results of the examination and revision of the model. Results: The review includes current challenges about the computational fluid dynamics. It is relevant in different areas of engineering to find answers for the problems occurring globally with the aid of a number of simulation-based software hereby, making the world free from complex problems in order to have a non-complicated scenario. Conclusion: Computational fluid dynamics are relevant in each, and every kind of problem related to the fluid flow, either existing in the human body or anywhere. In the contemporary era, there are enormous numbers of simulation-based software, which provide excellent results with just one click, thereby resolving the problems within microseconds. Hence, we cannot imagine our present and upcoming future without CFD, which has ultimately made the execution of work easier, leaving behind non-complicating scenarios. Lastly, we can conclude that “CFD is a faster, smarter, and lighter way in designing process.”
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media

The SemEval-2020 Task 12 (OffensEval) challenge focuses on detection of signs of offensiveness using posts or comments over social media.This task has been organized for several languages, e.g., Arabic, Danish, English, Greek and Turkish.It has featured three related sub-tasks for English language: sub-task A was to discriminate between offensive and non-offensive posts, the focus of sub-task B was on the type of offensive content in the post and finally, in sub-task C, proposed systems had to identify the target of the offensive posts.The corpus for each of the languages is developed using the posts and comments over Twitter, a popular social media platform.We have participated in this challenge and submitted results for different languages.The current work presents different machine learning and deep learning techniques and analyzes their performance for offensiveness prediction which involves various classifiers and feature engineering schemes.The experimental analysis on the training set shows that SVM using language specific pre-trained word embedding (Fasttext) outperforms the other methods.Our system achieves a macro-averaged F1 score of 0.45 for Arabic language, 0.43 for Greek language and 0.54 for Turkish language.
Nonparametric graphical model for counts.

Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence structure. This is particularly true when interest focuses on inferring the conditional independence graph. In this article, we propose a new class of pairwise Markov random field-type models for the joint distribution of a multivariate count vector. By employing a novel type of transformation, we avoid restricting to non-negative dependence structures or inducing other restrictions through truncations. Taking a Bayesian approach to inference, we choose a Dirichlet process prior for the distribution of a random effect to induce great flexibility in the specification. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for posterior computation. We prove various theoretical properties, including posterior consistency, and show that our COunt Nonparametric Graphical Analysis (CONGA) approach has good performance relative to competitors in simulation studies. The methods are motivated by an application to neuron spike count data in mice.
Performance comparison of web services using Symfony, Spring, and Rails examples

The article presents the results of a comparative analysis of web application frameworks for Java, PHP and Ruby. The most popular programming frameworks for each language were used for the research: Spring, Symfony and Ruby on Rails. In each of the frameworks the REST and SOAP web services were prepared and used to measure the request execution time. Measurements were made using Postman and SoapUI tools. The tests results showed that Spring is the fastest way to handle requests.
A New Gaze Estimation Method Based on Homography Transformation Derived from Geometric Relationship

In recent years, the gaze estimation system, as a new type of human-computer interaction technology, has received extensive attention. The gaze estimation model is one of the main research contents of the system. The quality of the model will directly affect the accuracy of the entire gaze estimation system. To achieve higher accuracy even with simple devices, this paper proposes an improved mapping equation model based on homography transformation. In the process of experiment, the model mainly uses the “Zhang Zhengyou calibration method” to obtain the internal and external parameters of the camera to correct the distortion of the camera, and uses the LM(Levenberg-Marquardt) algorithm to solve the unknown parameters contained in the mapping equation. After all the parameters of the equation are determined, the gaze point is calculated. Different comparative experiments are designed to verify the experimental accuracy and fitting effect of this mapping equation. The results show that the method can achieve high experimental accuracy, and the basic accuracy is kept within 0.6∘. The overall trend shows that the mapping method based on homography transformation has higher experimental accuracy, better fitting effect and stronger stability.
Digital De-Citizenship: The Rise of the Digital Denizen in Bahrain

Revolutions seldom involve more than one percent of the population. However, in Bahrain, a small island nation with a population of around 570,000, twenty percent of the population took to the streets in February 2011 to demand greater democratic reform, making it “proportionally one of the greatest shows of ‘people power’ in modern history.” The regime's response was disproportionally brutal. Saudi-dominated troops from the Gulf Cooperation Council Peninsula Shield Force were “invited to” or “invaded” Bahrain, depending on who is telling the story. Under cover of the Saudi military, Bahrain's security forces killed dozens of civilians, torturing, maiming, and raping many others. The arsenal of repressive techniques was exhaustive. Belonging also was used as a tool of repression, with many being stripped of their Bahraini citizenship on spurious, terror-related charges.
Concerning the issue regarding legal nature of electronic (digital) evidence

Current developments in the area of digital technologies and, as a consequence, the emergence of new information sources cause the necessity of implementation of special provisions regarding electronic (digital) evidence in the law of criminal procedure. Nevertheless, before amending law of evidence regulations, the legal nature of such proof shall be defined. It is also necessary to comprehend how this new evidential source correlates with basic grounds of proof theory, other provisions of criminal procedure and on the whole — other branches of law.
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection

Written text emphasis in visual media is used to increase the comprehension of written text, to grab a viewer's attention, and to convey the author's intent.The task is choosing candidates for emphasis in short written text, to enable automated design assistance in authoring.As the author's intent is unknown and only the input text is available, multiple emphasis selections are valid.In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection.Specifically, word embeddings are learned from the Embeddings from Language Model (ELMo) to extract feature vector representations.Then, the ordinal classifications are implemented on four different multi-granularities to approximate the continuous emphasized values.Comparative experiments were conducted to compare the model with the baseline, in which the problem is transformed to a label distribution problem.
Testing the context-independence of quantum gates

The actual gate performed on, say, a qubit in a quantum computer may depend, not just on the actual laser pulses and voltages we programmed to implement the gate, but on its context as well. For example, it may depend on what gate has just been applied to the same qubit, or on how much a long series of previous laser pulses has been heating up the qubit's environment. This paper analyzes several tests to detect such context-dependent errors (which include various types of non-Markovian errors). A key feature of these tests is that they are robust against both state preparation and measurement (SPAM) errors and gate-dependent errors. Since context-dependent errors are expected to be small in practice, it becomes important to carefully analyze the effects of statistical fluctuations and so we investigate the power and precision of our tests as functions of the number of repetitions and the length of the sequences of gates. From our tests an important quantity emerges: the logarithm of the determinant (log-det) of a probability (relative frequency) matrix $\mP.$ For this reason, we derive the probability distribution of the log-det estimates which we then use to examine the performance of our tests for various single- and two-qubit sets of measurements and initial states. Finally, we emphasize the connection between the log-det and the degree of reversibility (the unitarity) of a context-independent operation.
Speeding up the Multiplication Algorithm for Large Integers

Multiplication is one of the basic operations that influence the performance of many computer applications such as cryptography. The main challenge of the multiplication operation is the cost of the operation as compared to other basic operations such as addition and subtraction, especially when the size of the numbers is large. In this work, we investigate the use of the window strategy for multiplying a sequence of large integers to design an efficient sequential algorithm in order to reduce the number of bit-multiplication operations involved in multiplying a sequence of large integers. In our implementation, several parameters are considered and measured for their effect on the proposed algorithm and the best-known sequential algorithm in the literature. These parameters are the size of the sequence of integers, the size of the integers, the size of the window, and the distribution of the data. The experimental results prove the effectiveness of the proposed algorithm are compared to the ones of the best-known sequential algorithm, and the proposed algorithm is able to achieve a reduction in computing time greater than 50% in most cases.
The Concept of Malware as a Means of Committing Computer Crimes: Classification and Methods of Illegal Use

The authors analyze problems connected with malware from the standpoint of the doctrine of the methods of computer crimes/offenses as one of the components of the theory of information-computer support of criminalistic work. Most methods of computer crimes are based on the unauthorized access to computer facilities and systems gained through malware that, in fact, acts as a weapon of crime. The authors present a classification of malware based on different parameters: from the standpoint of criminal law and criminology; the standpoint of information technology; the standpoint of the doctrine of computer crimes/offenses. Various grounds for the classification of malware are examined. A general classification, widely used by the developers of antiviral software, includes virus-programs, worm-programs and trojan-programs. In the modern situation of massive digitization, it is not practical to regard masquerading as a legitimate file as a dominant feature of trojan software. On the contrary, criminals try hard to hide from the user the downloading, installation and activity of malware that cannot self-propagate. The key method of propagating trojan programs is sending mass emails with attachments masquerading as useful content. The classification of malware by the way and method of propagation - viruses, worms and trojan programs - is only currently used due to traditions and does not reflect the essence of the process. A different classification of malware into autonomous, semi-autonomous and non-autonomous programs is based on the possibility of their autonomous functioning. At present there is practically no malware whose functions include only one specific type of actions, most of it contains a combination of various types of actions implemented through module architecture, which offers criminals wide opportunities for manipulating information. The key mechanisms of malwares work are described and illustrated through examples. Special attention is paid to harmful encryption software working through stable cryptographic algorithms - ransomware, when criminals demand ransom for restoring data. There is no criminal liability for such theft. The authors outline the problems connected with the possibility of the appearance of new malware that would affect cloud resources.
How to teach languages with “Among Us”

What is this? A guide to using the popular multiplayer game “Among Us” for teaching languages Why did you make it? Having played the game myself, I recognised its potential as a language learning tool and expect other teachers would appreciate a guide on how to use it. Who is it for? Teachers that would like to harness the social and cultural capital that students possess through knowledge of modern, online, multiplayer video games. What did you include? Post-play worksheets and a number of reasons explaining WHY a teacher may want to use such games in their context
The Effect of Cryptocurrency Price on a Blockchain-Based Social Network

The massive spread of online social media platforms has favored the emergence of social media giants, like Facebook, Twitter, Instagram. These companies have been the center of many scandals related to privacy issues, data ownership and censorship. The issues that stem from their centralized architecture, brought the spotlight on alternative solutions based on decentralized or distributed architectures. In these alternative social media platforms, the data is not owned by a single company, the open nature makes censorship harder and users can profit from their contents. Here we focus on a specific solution - Steemit - built on top of a public blockchain, linked to the Steem cryptocurrency. In fact, in Steemit, the participation and the content quality are rewarded with a cryptocurrency through a network- and user-based voting system. This way, the network structure, the dynamics on top of it and the cryptocurrency market are strongly coupled. Such an interplay is the focus of the paper; specifically, we study the impact of the cryptocurrency Steem on the social network growth, using more than 4 years of data extracted directly from the Steem Blockchain. We find that the growth of the network is strongly tied to the fluctuations in Steem cryptocurrency price: it can be observed that rising Steem prices trigger the network growth, and similarly, when Steem value drops, so does the network’s growth. We also find evidence of a lead-follow relationship between Steem price and users’ behavior: our study suggests that the full impact of the Steem cryptocurrency price can be observed within 3–4 weeks. So essentially, the blockchain-based social network Steemit represents a valuable case study among the emerging online social media, where network dynamics and economic/financial aspects are strongly intertwined; and where the underlying blockchain is an unprecedented source of data for measuring such interplay.
Augmenting Policy Learning with Routines Discovered from a Single Demonstration

Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers routines composed of primitive actions from a single demonstration and uses discovered routines to augment policy learning. To discover routines from the demonstration, we first abstract routine candidates by identifying grammar over the demonstrated action trajectory. Then, the best routines measured by length and frequency are selected to form a routine library. We propose to learn policy simultaneously at primitive-level and routine-level with discovered routines, leveraging the temporal structure of routines. Our approach enables imitating expert behavior at multiple temporal scales for imitation learning and promotes reinforcement learning exploration. Extensive experiments on Atari games demonstrate that RAPL improves the state-of-the-art imitation learning method SQIL and reinforcement learning method A2C. Further, we show that discovered routines can generalize to unseen levels and difficulties on the CoinRun benchmark.
Perbandingan Metode Single Exponential Smoothing dan Metode Holt untuk Prediksi Kasus COVID-19 di Indonesia

Coronavirus disease (COVID-19) was first discovered in December 2019 in Wuhan, China, and spread so quickly into a pandemic. This outbreak has spread to 24 other countries, including Indonesia. Its spread is very fast, so a co-19 prediction study is needed to be able to make the right policy. To be able to predict the number of COVID-19 cases can be done with the Forecasting Technique. The purpose of this study is to forecast and compare Single Exponential Smoothing and Double Exponential Smoothing ¬ against the number of COVID-19 cases in Indonesia. The results of this study can be used as consideration for policymaking in dealing with the spread of COVID-19. Distribution predictions are based on data released by the Indonesian National Disaster Management Agency (BNPB) in the first 100 days of COVID-19 deployment. The results of this study are the Double Exponential Smoothing method is more accurate than the Single Exponential Smoothing method because the forecasting results show an increase from the previous data. And the percentage of errors (MAPE) obtained is significantly smaller.