Reviewerly/exHarmony huggingface dataset:
THE ASSESSMENT OF THE NEXUS BETWEEN OFFICIAL DEVELOPMENT ASSISTANCE AND GDP: AN EMPIRICAL STUDY ON BANGLADESH

The paper provides the assessment of the nexus between official development assistance (ODA) and gross domestic product (GDP) in Bangladesh.The official development assistance is termed as foreign aid supplied by the advanced countries to developing and less developed countries for the upgradation of their livelihood and not for the progress of military purposes.GDP depicts a quick scenario of a country by assessing the size of the economy over a period of time.Time series yearly data from 1971 to 2018 has been used for the analysis.After ADF analysis for the stationarity of data ARDL bound test has been used for determining the short-run along with long-run association between the variables.The evidence has been found that in the long-run ODA and GDP are cointegrated.The ECM model exposes the equilibrium that converges to steady state at 65% annually by analyzing ECM.The Granger Causality test in the short run OLS model indicates a unidirectional causality between ODA and GDP that means ODA causes GDP.Again in the long run OLS model there also have unidirectional causality between GDP and ODA and it indicates that GDP cause ODA and identified as GDP and ODA are not separate variables.A set of robustness test affirm the accuracy of the analysis.Lot of study is needed in this aspect to get more empirical information about the effectiveness of the official development assistance and economic overview of this country.
Discussion on Inquiry Teaching in College English Teaching

With the comprehensive implementation of education reform in our country, education departments and teachers attach great importance to college English teaching. The emergence of inquiry-based teaching methods has greatly changed the traditional English teaching methods. In order to improve the problems existing in the traditional English teaching methods, this paper makes an in-depth analysis of the inquiry-based teaching in English teaching and puts forward some specific methods to provide a reference for educators.
Artificial Intelligence Chatbot Adoption Framework for Real-Time Customer Care Support in Kenya

&lt;p&gt;In today’s society, most if not all sectors digitize and automate in order to become more efficient. The increasing availability and sophistication of software technologies disrupt labor markets by making workers redundant. Within this context, a significant change is companies’ introduction of chatbots as a supplement to human customer support is vital. Chatbots are computer programs that interact with humans through natural language. The purpose of chatbots is to simulate a human conversation in response to natural language input through text or voice. There seems to be no proper guidelines for adoption of chatbots for provision of customer care support services in telecommunication industry in Kenya. The aim of this research was to develop a framework for adoption of artificially intelligent chatbot application in telecommunication industry. This was achieved through determination of the status of implementation of chatbots in Kenya and identification of key metrics that served as indicators for chatbot adoption. The metrics were identified through review of previous technology adoption frameworks and models. The study adopted mixed methods where qualitative and quantitative data was collected using interview schedules and questionnaires respectively. Content analysis was used in analyzing qualitative data. Quantitative data was analyzed descriptively and results were presented using tables. The target population included experts in the field of AI in two telecommunication firms in Kenya. The study sample was drawn, using Delphi technique, from the two telecommunication firms. The descriptive and principal component analysis were utilized. The results of this research study will be crucial to all telecommunication firms in guiding them on the most effective and efficient way of adopting AI chatbot application for customer support services.&lt;/p&gt;
Evolutionary Dynamics Effects Account for the Improvement Caused by R-Augmentation

Previous work has found a method for augmenting a genetic algorithm (GA), referred to as R-augmentation, that produces better-scoring results earlier in evolutionary time. R-augmentation works by using an information-theoretic measure quantifying mental representations (denoted as R) as an additional contribution to the fitness function of the GA. It has been shown that this method improves the performance of the GA by encouraging the evolution of high-performing artificial agents controlled by Markov Brains or Recurrent Neural Networks. Different mechanisms could explain this phenomenon. Here we demonstrate that the majority of the improvement is caused by R-augmentation altering the evolutionary dynamics of the GA, reflected in the changed movement of mutational operators through the fitness landscape.
Hand Gestures Controlled Robot using Arduino

In this paper, Hand Gestures has been defined as the mode of communication while interacting with the machine robot.Eliminating the use of primary modes like Remote, joystick, etc.The hand gesture robot is beneficial to reduce human efforts and carry out effective results.Hand gesture robot uses the simple module like Arduino, accelerometer and nRF2401L, etc. which is found to be effective than other wireless modules
Improving Medical NLI Using Context-Aware Domain Knowledge

Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training. However, because of the linguistic idiosyncrasies of clinical texts (e.g., shorthand jargon), solely relying on domain knowledge from an external knowledge base (e.g., UMLS) can lead to wrong inference predictions as it disregards contextual information and, hence, does not return the most relevant mapping. To remedy this, we devise a knowledge adaptive approach for medical NLI that encodes the premise/hypothesis texts by leveraging supplementary external knowledge, alongside the UMLS, based on the word contexts. By incorporating refined domain knowledge at both the lexical and relational levels through a multi-source attention mechanism, it is able to align the token-level interactions between the premise and hypothesis more effectively. Comprehensive experiments and case study on the recently released MedNLI dataset are conducted to validate the effectiveness of the proposed approach.
Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent Neural Networks

We trained a model to automatically transliterate Judeo-Arabic texts into Arabic script, enabling Arabic readers to access those writings. We employ a recurrent neural network (RNN), combined with the connectionist temporal classification (CTC) loss to deal with unequal input/output lengths. This obligates adjustments in the training data to avoid input sequences that are shorter than their corresponding outputs. We also utilize a pretraining stage with a different loss function to improve network converge. Since only a single source of parallel text was available for training, we take advantage of the possibility of generating data synthetically. We train a model that has the capability to memorize words in the output language, and that also utilizes context for distinguishing ambiguities in the transliteration. We obtain an improvement over the baseline 9.5% character error, achieving 2% error with our best configuration. To measure the contribution of context to learning, we also tested word-shuffled data, for which the error rises to 2.5%.
Schwa-deletion in German noun-noun compounds

We report ongoing research on linking elements in German compounds, with a focus on noun-noun compounds in which the first constituent is ending in schwa. We present a corpus of about 3000 nouns ending in schwa, annotated for various phonological and morpho-syntactic features, and critically, the dominant linking strategy. The corpus analysis is complemented by an unsuccessful attempt to train neural networks and by a pilot experiment asking native speakers to indicate their preferred linking strategy. In addition to existing nouns, the experimental stimuli included nonce words, also ending in schwa. While neither the corpus study nor the experiment offer a clear picture, the results nevertheless provide interesting insights into the intricacies of German compounding. Overall, we find a predominance of the paradigmatic linking element -n for feminine and masculine nouns. At the same time, the results for nonce words show that -n is not a default strategy.
HOW ORGANIZATION PERFORMANCE DIFFER FROM KOREAN AND MALAYSIAN INFORMATION TECHNOLOGY (IT) COMPANIES

The main purpose of this study is to highlight the interaction effect between 'market orientation' which is recognized as a sustainable competitive advantage factor in the private sector and ‘environmental turbulence’ on the marketing capabilities which is one of the powerful antecedents of organizational performance, contrasting the IT companies in Korea and Malaysia believed to exist a certain difference in the capitalistic market economy system. It was found that there exists sharp contrast between two countries in that Korea showed a significant interaction effect between customer orientation and environmental turbulence, however, Malaysia showed no significant moderation effect. This result may implicate the difference in the process of market mechanism and environmental turbulence between the two countries.
Pemakaian metode preview, questio, read, summarize, tes(pqrst) guna meningkatkan keterampilan membaca pemahaman peserta didik kelas iv sekolah dasar

The study sought to improve the skills of reading comprehension of students from IV B Class at SD Ta’mirul Islam Surakarta 2019/2020. This was conducted by using PQRST methods. Moreover, this research is in the form of Classroom Action Research (CAR) which was run for two cycles. Each cycles has four systematic stages, namely planning, implementing, observing, and reflecting. This study used variety of techniques including interview, observation, tests, and documentation. The validity of the data was tested using data and source triangulation techniques. Data were analyzed using an instrument originally developed by Miles-Huberman. On this research, the average value result of reading comprehension skills is 44.291 with 12,5% of passing percentage in pre-action. In the first cycle, the average acquisition of reading comprehension skills increased to 60.229 with 41,67% of passing percentage. Lastly, in cycle II, the average acquisition of reading comprehension skills increased to 81.062 with 87,5% of passing percentage.. Finding shows that the PQRST methods has been proven to be successful in improving the reading comprehension skills of grade IV B students at SD Ta'mirul Islam Surakarta for the academic year 2019/2020.
An improved dehazing algorithm using muti-scale weighted transmission fusion and self-adaptive gamma correction

Aiming at the problems of image distortion and detail information loss occurring in recent dehazing algorithms, this paper proposes a dehazing algorithm based on dark channel prior with multi-scale weighted transmission fusion and self-adaptive gamma correction. Firstly, in order to refine the transmission map, a multi-scale weighted transmission fusion strategy with three scales is applied in the transmission estimation step. Then, a self-adaptive gamma correction method is proposed to enhance the contrast performance after applying the multi-scale weighted transmission fusion to image dehazing, and finally get the desired dehazed image. Experimental results demonstrated that the proposed algorithm can not only overcome the problems of image distortion and detail information loss well, but also yields a satisfied performance in comparison with tested similar methods.
Getting on the Same Page: Aligning ERM and LibGuides Content

For many years, the University of North Texas Libraries’ Electronic Resource Management (ERM) module and LibGuides pages were administered separately with no shared oversight. Over time, the number of subject terms and keywords assigned in both systems grew and diverged significantly, leading to confusion on acceptable search terms and a wide variation in subject specificity. Recently, a committee was formed to review and revise the LibGuides’ subjects and establish new processes for creating and maintaining them. This work led to a revamping of the parallel work done for the ERM to not only align with the LibGuides process, but to feed into the LibGuides A-Z database list.
PENGARUH MODEL PEMBELAJARAN TALKING STICK TERHADAP HASIL BELAJAR EKONOMI PESERTA DIDIK

The problem in this study is whether  there is an effect of the Talking Stick Learning Model on Economic Learning Outcomes in Class X  Students in Even Semester of SMK Negeri 8 Bandar Lampung Academic Year 2018/2019. The purpose of this study was to determine the effect of the talking stick learning model on economic learning outcomes in class X students in the even semester of  SMK Negeri 8 Bandar Lampung in the academic year 2018/2019. This study uses an experimental method, where research methods that require the writer to practice teaching directly on the subject under study using the talking stick learning model. The population in this study were 98 students of SMK Negeri 8 Bandar Lampung, amounting to 98 students in which the sample consisted of 2 classes namely X Accounting as an Experiment class which amounted to 34 people and X Sharia Banking as a control class of  32. After hypothesis testing on the two similarity tests - the average and the calculation of economic learning outcomes data using a statistical formula, then it is obtained 3.52 and  at a significant level of  5%. This means that there is the influence of the talking stick learning model on economic learning outcomes in class X students in the even semester of SMK Negeri 8 Bandar Lampung in the academic year 2018/2019
An Efficient Deduplication Scheme based on Chinese Remainder Theorem (CRT)

This paper proposes a secure data deduplication scheme which integrates fault tolerance, confidentiality and efficient key management. It adopts a secret sharing algorithm based on a Permutation Ordered Binary (POB) number system to decompose the data block into multiple random shares, and enhances data security by introducing the Proof of Ownership (PoW) concept. Moreover, our scheme applies a secret sharing algorithm based on the Chinese Remainder Theorem (CRT) to divide the key into multiple random blocks and sends them to the corresponding Key Management Server (KMS) to minimize the key overhead. Experimental results show that our scheme overwhelms the other schemes in terms of function and efficiency.
Quantifying Assemblage Theory: A Conceptual, Empirical, and Data-Driven Approach to Guide Discovery

Inspired by the idea that digital text can support examination of the sociomaterial world, we present a novel computational approach, guided by assemblage theory, to understand the emergence of assemblages of personal automation practices. We overlay assemblage theory on machine learning models for text analysis, clustering, and dimensional reduction to turn these computational models into vehicles for operationalizing the possibility space, points of attraction, and other key assemblage theory constructs. We also use an inductive analysis to deepen the insights from our computational findings. We demonstrate our approach for visualization and discovery on a unique data set of conditional text-based rules of the form “if this, then that,” defining Internet-connected applets that consumers can run to automate events in their daily lives. We uncover 127 personal automation practices and interpret the material roles of their components, as well as the expressive roles that emerge in the realized possibility space. We also show how the full possibility space provides a dynamic dashboard that reifies both what is, as well as what could be. Our results can stimulate additional research and encourage practitioners to look beyond use cases to those underlying points of attraction driving consumers’ deeper needs to automate their lives.
Análisis de los vídeo juegos en tiempos de pandemia y post-pandemia en Colombia

The Covid-19 pandemic has changed the educational and economic landscape around the world, since it forced a greater implementation of digital resources due to confinement, but yes, necessary to protect ourselves in terms of health. This document analyze video games as a digital tool to strengthen learning methodologies in schools in Bogota, Colombia and diversify the income of Bogota citizens through streaming platforms for video games. Research has been done in academic documents related to the implementation of video games in education and an interview to 68 people from Bogota, between 12 and 61 years old. This is to analyze the different perspectives and applications that video games can offer to overcome the pandemic and apply in the post-pandemic.
Twitter sentiment analysis on coronavirus outbreak using machine learning algorithms

Social media is a source that produces massive amount of data on an unprecedented scale It serves as a platform for every person to share their perspectives, opinions and experiences apart from just being a platform that gives information to the public who search for information on the disease As unexpected as the occurrence of coronavirus disease 2019 (COVID-19) was, it has been radically affecting people all over the world, there is a need to analyse the opinion of people on the pandemic COVID-19 This paper focuses on the sentiment analysis of COVID-19 using twitter data The analyses are based on the machine learning algorithms This article provides an analysis on how people react to a pandemic outbreak, how much they are aware of the disease and its symptoms, what precautionary measures they are taking and whether or not people are following government’s guidelines etc Understanding the posts on social media pages during a pandemic outbreak allows health agencies and volunteers to better assess and understand the public's insolences, sentiments and needs in order to deliver appropriate and effective information © 2020 Ubiquity Press All rights reserved
Impact of Virtual Learning Space in Teaching Learners with Moderate Intellectual Disability

Nigeria just like other countries of world has been impacted by the global pandemic, COVID-19, leading to implementing lockdown measures. The advent of Virtual Learning Space is now considered the best alternative learning platform because of the need to save academic program. The paper examined the position of virtual learning space (VLS) to ensure that learners with intellectual disability are not excluded from instructional experiences due to the global crisis caused by the transmission of COVID-19. Virtual learning space in education has been defined as a computer-simulated location or space where learners with intellectual disabilities can learn and communicate without requiring physical contact. The use of VLS during the lockdown to teach learners with moderate intellectual disability during the lockdown will most likely ensure that they are not excluded from learning activities. This would also contribute to an expansion in learning opportunities so that more learners can have access to educational programmes, often beyond the conventional learning environment, thus facilitating the advancement of knowledge and acquisition of skills required to work efficiently in the modern world. In addition to highlighting the various ways in which VLS can improve learning, challenges that are likely to be confronted in using VLS were also identified before concluding
Using Interactive Inventions

Interactive inventions are systems and processes that can be used by multiple actors at the same time. Many interactive inventions are the product of emerging technologies such as the Internet of Things that allow billions of everyday devices to communicate with each other via the Internet. Other interactive inventions are prevalent in the emerging fields of personalized medicine and FinTech (new financial technologies). Unfortunately, the law concerning how to determine liability when a patent directed to an interactive invention is infringed is dissonant across classes of inventions. Specifically, what it means to “use” an interactive system is different from what it means to “use” an interactive method. Why does the law treat these acts differently? Should it?This Article attempts to answer these important questions. Specifically, this Article contends that the current framework for what it means to “use” an interactive system is problematic. Interactive inventions are different than traditional method and system inventions. Unfortunately, courts have too often applied, especially in recent cases involving interactive systems, an outmoded framework to interactive inventions. When interactive inventions are analyzed under the proper framework, it allows for the application of sound legal principles to solve multi-actor liability problems. Specifically, this Article argues that a direct infringer “uses” an interactive system when it controls the system and obtains a benefit from it. This approach is also more consistent with the recently broadened standard for infringement of interactive methods.
A Novel Approach for Dimensionality Reduction and Classification of Big Data Using Meta-Heuristic Approach

User interaction over the internet is growing day by day. The social network users send massive information to the network to share with others on the network. This increases the information on social media, hence needed a mechanism to handle or manage such high dimensional data termed as Big Data. Big Data reduction can be performed by using a feature selection approach. But, the Classification of such massive data is a challenging task for all the researchers. To overcome this problem, a metaheuristic based Genetic Algorithm (GA) for the selection of most suitable rows which can be provided for training. The selected rows undergo a feature extraction process, which is attained by Principle Component Analysis (PCA). The extracted principle components are optimized using another meta-heuristic algorithm termed as Whale Optimization. As the proposed algorithm uses unlabelled data, clustering is done to label the data. Two different distribution indexes were calculated for data with GA selected rows and data with GA selected rows along with PCA and whale. The distribution index is the ratio of a total number of elements in one cluster to a total number of elements in the second cluster. High distribution index leads to better accuracy when it comes to classifying the text data. The data is clustered using the K -Means algorithm to find the cluster indexes. The proposed algorithm presents a hybrid classification mechanism with upper and lower boundaries of classified labels using Artificial Neural Network (ANN) and Support Vector Machine (SVM).