Reviewerly/exHarmony huggingface dataset:
Institutional Quality, Foreign Direct Investment and Economic Growth: Evidence from ECOWAS Member Countries

This study adopts the Autoregressive Distributed Lag (ARDL) model to analyse the short and long-run impacts of institutional quality and foreign direct investment (FDI) inflow on economic growth in ECOWAS member countries, using time series data sourced from the World Bank, UNCTAD and Freedom House for the period 1990 to 2020, the study goes a step further to ascertain whether the impact is homogeneous in the region or not.Amongst others, the results found a positive relationship between FDI inflow and growth in ECOWAS.However, this was not statistically significant in the short run.Furthermore, the institutional quality variable of political regime was found to be insignificant both in the short and long run.However, the impact of coup was negative and statistically significant at 10 per cent in the short run and long run.It was also discovered that the impact of foreign direct investment and institutional quality on economic growth were not homogeneous across ECOWAS member countries such that while the impact was positive for some countries, it was negative for others.In the same vein, the statistical significance of the impacts varied across the economies.The recommendations are that policy makers implement FDI-attractive policies, institutionalize democracy, and create checks and balances and separations of powers to rein in the excesses of the various institutions of government.
Research on the Teaching Design Ability of Mathematics Normal Students in Local Normal Universities in China

In order to ensure the smooth development of future work, normal university students must constantly improve their ability of teaching design.This paper uses text analysis method to study the teaching design ability of mathematics normal college students.This paper investigates the mathematics teaching design ability of normal university students from six aspects: overall teaching design, teaching objectives, key points and difficult points of teaching, teaching process, homework arrangement after class and teaching reflection.The results show that the students' overall understanding of teaching design is slightly biased, and the ability of compiling teaching objectives is not ideal, they are able to grasp the key and difficult points in teaching, but unable to put forward how to deal with the key and difficult points in teaching; The teaching process design is also deficient; Normal university students do not pay attention to homework assignment and teaching reflection.Finally, the paper puts forward some suggestions on the development of teaching design ability of mathematics normal university students.
Internal Audit Activities and Their Role in Employing Techniques to Support Digital Transformation in the Modern Business Environment

This study dealt with the knowledge of the role of internal audit activities in their most important dimensions (technical activities, information security and confidentiality activities, internal control activities and risk assessment, governance activities, and human resource activities) in employing techniques to support digital transformation in the modern business environment.Preparing a questionnaire designed specifically for this purpose, and the study was applied to a sample of internal auditors, managers and board members of companies listed on the Palestine Stock Exchange.There is a great agreement between the opinions of the respondents on the subject of the study, and in light of the results of the study; It recommended the need to pay attention to strengthening all aspects and dimensions of internal audit activities, because of their significant positive role in promoting the application of digital transformation techniques, which would result in the advancement and upgrading of the holistic performance of companies.
An Analytic solution for the Hadoop Configuration Combinatorial Puzzle based on General Factorial Design

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data.Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data.The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile.Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge.Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down.This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics.Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors.The method clearly segregates impactful parameters from the rest.The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%.The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.
Goal-oriented results

Researchers have used optical motion capture systems to assess the accuracy of video assistant referee (VAR) technologies, finding that users were not always accurate at interpreting the 3D imagery.
An Improved Association Rule Mining Algorithm Based on the Prior Information

Data mining can uncover valuable information from large amounts of redundant data, where association rule mining is one of the most important research element. By mining association rules, we can find connections between seemingly unrelated issues and contribute to society. However, the classic algorithms in association rule mining such as Apriori have gradually failed to complete the mining task in a short time due to the consistent growth of data. In this paper, an improved method for the association rule mining of supermarket sales based on prior information (i.e., historical data mining, and supermarket sales strategy) is proposed to address this problem. The performance of the improved association rule mining algorithm is verified by experimental studies, and the simulation results comparison and analysis have shown that the proposed method can reduce the time and space loss of mining in the mining task.
Filter Contribution Recycle: Boosting Model Pruning with Small Norm Filters

Model pruning methods have attracted huge attention owing to the increasing demand of deploying models on low-resource devices recently.Most existing methods use the weight norm of filters to represent their importance, and discard the ones with small value directly to achieve the pruning target, which ignores the contribution of the small norm filters.This is not only results in filter contribution waste, but also gives comparable performance to training with the random initialized weights [1].In this paper, we point out that the small norm filters can harm the performance of the pruned model greatly, if they are discarded directly.Therefore, we propose a novel filter contribution recycle (FCR) method for structured model pruning to resolve the fore-mentioned problem.FCR collects and reassembles contribution from the small norm filters to obtain a mixed contribution collector, and then assigns the reassembled contribution to other filters with higher probability to be preserved.To achieve the target FLOPs, FCR also adopts a weight decay strategy for the small norm filters.To explore the effectiveness of our approach, extensive experiments are conducted on ImageNet2012 and CIFAR-10 datasets, and superior results are reported when comparing with other methods under the same or even more FLOPs reduction.In addition, our method is flexible to be combined with other different pruning criterions.
A Bi-objective Game-based Task Scheduling Method in Cloud Computing Environment

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment.However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives.Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed.In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously.Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG.The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation.To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism.Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.
Research on random forest drug classification prediction model based on KMeans-SMOTE

Scientific medication is of great significance to improve the therapeutic effect of diseases. The etiology of chronic diseases is complex, different patients have different responses to drugs. Analyzing the examination results and medication information of previous patients can make the treatment plan follow a regular basis. First, the existing medical data is used to visualize the examination results of previous patients and the drugs used, to find the corresponding relationship between the two, and to build new attributes based on the attributes of the original data to improve the accuracy. Then, three oversampling algorithms are used to balance the imbalanced properties of the dataset. Finally, five kinds of classifiers were used to build a drug prediction model and conduct comparative experiments. The results showed that the random forest model based on the KMeans-SMOTE showed good results in various evaluations, and the drug prediction accuracy rate reached 95.05%. The model can better predict the drugs needed by patients, and its performance is better than other established models.
The Use of Game-Based Learning in Teaching English Vocabulary for Junior High School Students': Teachers' Perceptions

This article aims to determine the teacher's perception of the implementation of based learning game in teaching vocabulary to junior high school students. The study method that was used in this research is descriptive qualitative. One teacher from each of the three types of SMP in Madura; Mts. Nahdlatut Thullab Omben, SMPI An-Nidhomiyah Pamekasan, and Mts. An-Nur Dharma Camplong participated in a semi-structured interview. data collection was done through interviews, observation and documentation. Five questions are submitted in Indonesian to make it easier for participants to comprehend and respond them and then transcribe to collect data. It the findings of this study are positive perceptions with teachers of the use of game-based learning as a medium for teaching vocabulary. They too agree that based learning game is feasible to increase students' vocabulary because there are more advantages regardless of the challenges they face.
Semantic Segmentation Using Region Proposals and Weakly-Supervised Learning

Region proposal plays an important role in computer vision and successfully improves performance. This paper presents an efficient method using the region proposal for semantic segmentation. The main aim is to generate annotated data for deep learning techniques effortlessly. For this purpose, a region proposal algorithm is used to convert an image into several regions. According to defined rules, regions are explored, and some precise regions are selected. A new algorithm is introduced to generate useful masks only by supervising annotated data in the form of the bounding box. After that, these masks are fed to a deep semantic segmentation network. The proposed method shows promising results for weakly supervised learning semantic segmentation on the VOC2012 dataset. Also, this method can be employed to generate massive annotated data automatically and used to train deep networks.
Evaluating automatic speech recognition for L2 pronunciation feedback: a focus on Google Translate

This study examines the L2 pronunciation feedback provided by the Automatic Speech Recognition (ASR) functionality in Google Translate (GT). We focus on three Quebec Francophone (QF) errors in English: th-substitution, h-deletion, and h-epenthesis. Four hundred and fifty male and female QF recordings of sentences with correctly and incorrectly pronounced final items (e.g. I don’t know who to thank versus tank) were played into GT. Errors were equally divided between mispronunciations leading to real word (thank → tank) and nonword output (thief → tief). As anticipated, we found greater transcription accuracy for correct pronunciations and, among incorrect pronunciations, for real words versus nonwords. Overall, our findings suggest ASR can be highly effective for pronunciation feedback. We also examined transcriptions for gender bias, since ASR systems are often trained on corpora with more male voices, but our concerns proved unfounded: surprisingly, higher transcription accuracy was found for female recordings.
Supporting K-12 Students on Learning Programming Using Mobile Immersive Virtual Reality

As programming becomes fundamental in several areas and a desirable skill for children, teenagers, and adults, many teaching and learning approaches have been developed. Despite mobile learning, immersive virtual reality (VR), block-based programming (BBP), and storytelling have been used to support the learning of programming, to the best of our knowledge, there aren't any studies in the literature considering their jointly usage. Therefore, we created SSPOT-VR (Space Station for Programming Training in Virtual Reality), a low-cost mobile learning application that integrates methods for the teaching and learning of programming and the simulated experience of an immersive digitally created world. Results from surveys conducted with K-12 students and focused on user acceptance (n1=124 and n2=16) demonstrated that students accepted SSPOT-VR for usage. In a different perspective, a knowledge retention evaluation (n3=31) provided evidence that students retained programming knowledge after experiencing the application. Considering both results, new research opportunities became available, as adding collaboration and scaffolding to SSPOT-VR.
GOVERNMENT ENTREPRENEURIAL INTERVENTIONS, INDIVIDUAL CHARACTERISTICS, AND GROWTH OF YOUTH-OWNED MICRO AND SMALL ENTERPRISES IN KIGALI CITY, RWANDA

Micro and Small Enterprises have been approved as major performers in enhancing economic growth and quality of life by generating employment opportunities and profits in Rwanda. However, they have experienced several problems which diminish their growth. Those growth challenges can be improved by embracing strong entrepreneurial interventions. This research endeavored to investigate how government entrepreneurial interventions affect the growth of youth-owned MSEs in Kigali, in addition, it investigated the moderating effect of individual characteristics on the relationship between the government entrepreneurial interventions and the growth of youth-owned MSEs in Kigali City, Rwanda. The resource-based view and Gibb’s support theory were used. An explanatory research design was used in this research to determine the causal link between variables and establish the nature of the strength of the link. The study used a sample size of 154 MSEs selected from the target population of 252 youth-owned MSEs in Kigali, Rwanda. Data was collected using a questionnaire and analyzed using both descriptive and multiple regression analysis. The study noted that entrepreneurial training, access to credit, technology-based online social media, and market access have been effective government support in increasing the growth of MSEs in Kigali Rwanda. Moreover, the results indicated that individual characteristics positively affected the relationship between government entrepreneurial interventions and the growth of youth-owned MSEs in Kigali City, Rwanda. The study recommended that government should ease the requirements/conditions to be met by MSEs before accessing entrepreneurial interventions this will accelerate the growth of youth-owned SMEs in Rwanda.
Pedestrian Identification Method Based on Multi-Scale Feature Learning in Surveillance Video Images

With the development of deep learning technology and people's demand for intelligent security, human-computer interaction, shopping guide and other technologies, computer vision technology for pedestrian identification shows great application value.In this paper, pedestrian identification method based on multi-scale feature learning in surveillance video images is studied.Firstly, the deep residual network ResNet and densely connected convolutional network DenseNet are introduced as baseline networks.A model is constructed based on hybrid hourglass network module, enhanced weighted feature pyramid fusion network module and post-processing module.The loss function is designed, which is unified with other traditional models, and the optimization objective of the loss function is respectively corresponding to three parts, namely, the prediction error of corresponding center point, the prediction error of offset and the prediction error of bounding box size.The experimental results verify the effectiveness of the proposed model.
Analysis of Phishing Base Problems Using Random Forest Features Selection Techniques and Machine Learning Classifiers

Since the Internet is anonymous and uncontrolled, it is more open to phishing attacks, which can trick users to view malicious content in exchange for their personal information. However, the number of victims to this digital attack is significantly increasing due to inadequate security mechanisms. This research study develops a cyberbullying detection system, which can produce features from Twitter text by incorporating a point-wise mutual information approach. Further, a supervised machine learning method is developed for detecting the cyberbullying scenarios. Moreover, the proposed study has employed the sentiment, lexicon, and embedding features along with the PMI-semantic orientation. To apply extracted features, the SVM, Naive Bayes, KNN, decision tree, and random forest algorithm were employed. Experiments employing the proposed framework in a multi-class and binary setting indicate considerable potential in terms of kappa values, increased accuracy, and computed f-values. These findings imply that the proposed framework is a suitable option for recognizing the cyberbullying behavior in online social networks. Finally, the proposed outcomes and baseline features are compared by using various machine learning algorithms. The tenfold cross-validation has generated a highest accuracy of about 90.36%, and all four experiments assessed random forest algorithm based on 80% of the training dataset. The test result has also computed higher accuracy on random forest algorithm based on 20% of the test dataset.
Mediatised Practices: Renovating Homes with Media and ICTs in Australia

Abstract The extensive use of media and information and communication technologies (ICTs) in the household is building a new normality, where the use of technology is imperceptible to its users. Within this context, home renovation can be examined as a mediatised practice, which suggests the interactive transformation of practices which takes place with and through media. Our chapter is based on an interdisciplinary Australian study of 13 home renovations and their media practices. In this chapter, we argue that home renovation activities have moved into the digital realm. Furthermore, we emphasise that these mediatised home renovation practices contribute to the development of new mediatised domains that could assist in the transition and domestication of low-carbon practices and technologies.
A Framework for Data Security and Sharing in Cloud Computing Environments by Using Cryptographic Algorithms

Cloud storage is a liberating cloud application group to establish an internal database system. However, cloud storage raises security concerns. Inside the case of data sharing, the data is both cloud-specific and ancient music. Secure data distribution within a group which prevents the examiner from fearing legitimate but cruel agents important research questions. Data sharing and cloud storage play an important role in communication, as they can provide users with efficient and effective security services. To protect shared secret data, cryptographic methods are often applied. A large number of researchers, universities, government departments and commercial companies are adopting the cloud environment due to the initial investment, its high scalability and its many features. Despite many initiatives supporting the cloud environment, it faces many challenges. Data protection is a major concern in the information security and cloud computing industry. Many solutions have been developed to overcome this challenge. However, there is a there is not enough research between the current solutions and the emerging need to analyze, organize and analyze the important work that is done to study these solutions to respond to the field. This article provides an overview of research and process analysis, as well as an in-depth analysis of the topic of distributed security and data storage in cloud environments. Discussions on each dedicated process include: data protection services, rights and changes in the field, basic and comprehensive information including scope of work, achievements, limitations, future directions, etc. in any solution. In addition, a comprehensive and comparative analysis of these current methods is discussed. Then, the speed of the process is measured by the requirements of the search space and the future directions in the field are shown. The authors agree with this report. These plans will be what motivates researchers to do research in the field.
Autonomous Intelligence in Problem-Solving by Searching in the field of Artificial Intelligence

Problem-solving strategies in Artificial Intelligence are steps to overcome the barriers to achieve a goal, the "problem-solving cycle". Most common steps in such cycle involve- recognizing a problem, defining it, developing a strategy in order to fix it, organizing knowledge and resources available, monitoring progress, and evaluating the effectiveness of the solution. Once a solution is obtained, another problem usually comes, and the cycle starts again. Searching techniques in problem-solving by using artificial intelligence (A.I) are surveyed in this paper. An overview of definitions, development and dimensions of A.I in the light of search for solutions to problems are accepted. Dimensions as well as relevance of search in A.I research is reviewed. A classification of searching in the matter of depth of known parameters of search was also investigated. At last, the forethoughts of searching in AI research are brought into prominence. Search is noticed that it is the common thread that binds most of the problem-solving strategies in AI together.