Reviewerly/exHarmony huggingface dataset:
Assessing the Readiness for Cloud Computing in Higher Education Institutions in the Kingdom of Bahrain: Towards an Education Cloud Computing Strategy

There is a great potential for cloud computing adoption in Bahrain, something which will power exciting innovations and touch every area of people's lives, especially in the education sector. The current study investigates the critical success factors for implementing cloud computing into learning activities in higher institutions in Bahrain and how can effectively enhance the digital educational environment. The data sample of the current research consists of academic and administrative staff from nine universities in Bahrain, both private and public universities. The findings revealed that the adoption of cloud computing has proven to help build a digital educational environment in the Kingdom of Bahrain. Cloud computing can help in making digital research and digital learning and teaching. Besides offering more excellent student choices in education, cloud computing can provide good opportunities for enhancing research in these universities.
The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology

As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.
System Design to Utilize Domain Expertise for Visual Exploratory Data Analysis

Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.
Determining the Relative Importance of Personality Traits in Influencing Software Quality and Team Productivity

Software projects are almost always team efforts and successful projects involve well-formed and well-composed teams. Past studies have revealed that personality contributes to effective team composition and, therefore, project success. Yet despite its importance, only a couple of empirical studies have quantitatively evaluated the impact of personality on software quality and team productivity. Our previous study was an effort in this direction. In that study, we proposed a metric called Team Homogeneity Index and evaluated its impact on software quality and team productivity for two phases (implementation and testing) of the software development life cycle. This study is a continuation of our previous work. In this study, we replicate our experiment on three different phases of software development life cycle (i.e. analysis and design, implementation, and testing). We also determine the weights for all five personality traits using input from the industry and propose an improved version of Team Homogeneity Index called Weighted Team Homogeneity Index. Finally, we conduct a comparative analysis of Team Homogeneity Index and Weighted Team Homogeneity Index to determine whether weights assigned to personality traits make any difference. Our findings reveal that weights do make a difference and Weighted Team Homogeneity Index is more strongly correlated than Team Homogeneity Index for almost all of the teams, especially those composed of practitioners, in the three different phases of Software Development Life Cycle.
Digital Forensic Tools Used in Analyzing Cybercrime

With the development of technology, crime has become not limited to traditional crimes, but has evolved in its modern sense into electronic crimes that have their own tools for penetration, extortion, theft, money laundering and electronic exchange, and concealing the effects of these crimes increases the difficulty of investigation. Therefore, there is an urgent need to develop electronic tools in forensic medicine to keep pace with this type of crime, search for criminals and collect evidence to be used against them before the court, and from here the topic of these tools has evolved, and we will also address in this scientific paper these tools and identify their strengths and weaknesses and suggest appropriate solutions and tools. That covers all aspects of the evidence to assist the investigators in selecting the appropriate tool.
An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm

Natural scene text classification is considered to be a challenging task because of diversified set of image contents, presence of degradations including noise, low contrast/resolution and the random appearance of foreground (font, style, sizes and orientations) and background properties.Above all, the high dimension of the input image's feature space is another major problem in such tasks.This work is aimed to tackle these problems and remove redundant and irrelevant features to improve the generalization properties of the classifier.In other words, the selection of a qualitative and discriminative set of features, aiming to reduce dimensionality that helps to achieve a successful pattern classification.In this work, we use a biologically inspired genetic algorithm because crossover employed in such algorithm significantly improve the quality of multimodal discriminative set of features and hence improve the classification accuracy for diversified natural scene text images.The Support Vector Machine (SVM) algorithm is used for classification and the average F-Score is used as fitness function and target condition.First after preprocessing input images, the whole feature space (population) is built using a multimodal feature representation technique.Second, a feature level fusion approach is used to combine the features.Third, to improve the average F-score of the classifier, we apply a meta-heuristic optimization technique using a GA for feature selection.The proposed algorithm is tested on five publically available datasets and the results are compared with various state-of-the-art methods.The obtained results proved that the proposed algorithm performs well while classifying textual and non-textual region with better accuracy than benchmark state-of-the-art algorithms.
End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System

In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.
ELM-Based Frame Synchronization in Nonlinear Distortion Scenario Using Superimposed Training

The requirement of high spectrum efficiency puts forward higher requirements on frame synchronization (FS) in wireless communication systems. Meanwhile, a large number of nonlinear devices or blocks will inevitably cause nonlinear distortion. To avoid the occupation of bandwidth resources and overcome the difficulty of nonlinear distortion, an extreme learning machine (ELM)-based network is introduced into the superimposed training-based FS with nonlinear distortion. Firstly, a preprocessing procedure is utilized to reap the features of synchronization metric (SM). Then, based on the rough features of SM, an ELM network is constructed to estimate the offset of frame boundary. The analysis and experiment results show that, compared with existing methods, the proposed method can improve the error probability of FS and bit error rate (BER) of symbol detection (SD). In addition, this improvement has its robustness against the impacts of parameter variations.
Jupyter in Computational Science

The articles in this special section discusses the applications supported by the Jupyter Notebook. Before notebooks, a scientist working with Python code, for instance, might have used a mixture of script files and code typed into an interactive shell. The shell is good for rapid experimentation, but the code and results are typically transient, and a linear record of everything that was tried would be long and not very clear. The notebook interface combines the convenience of the shell with some of the benefits of saving and editing code in a file, while also incorporating results, including rich output, such as plots, in a document that can be shared with others. The Jupyter Notebook is used through a web browser. Although it is often run locally, on a desktop or a laptop, this design means that it can also be used remotely, so the computation occurs, and the notebook files are saved, on an institutional server, a high-performance computing facility or in the clou
English as Foreign Language (EFL) Teacher’s Questioning Strategies in Classroom Interaction

&#x0D; &#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; The objectives of this research were to find out the teacher’s questioning strategies, types of the questions, and the reasons of using the questioning strategies in classroom interaction.. This qualitative research using discourse analysis approach. The sample of this study was a teacher of SMPN 1 Tanete Riaja, Barru Regency Data collection of this research was conducted through recording, observation, and interview. The data collected were analyzed through conversational analysis that was used in analyzing teacher's questioning strategies, types of the teacher's questions, and the reasons for using questioning strategies in EFL classroom. The research findings showed that the teacher used questioning strategies by applying some types of questions. The open/ closed questions and knowledge questions were used dominantly by the teacher. It depends on the material that the teacher transferred to the students. Otherwise, synthesis and evaluation questions were used only a few times by teacher. The teacher also applied some strategies, namely questioning in the opening session, questioning in the middle of teaching, prompting the questions, probing the question, and gave reinforcement to the students. The research finding indicated the positive effects of questioning strategies which were applied by the teachers toward EFL Classroom. It was proved by the students’ participation in learning. Moreover, it was shown by the observation sheets that the teacher and all students did a good interaction teaching-learning process.&#x0D; &#x0D; &#x0D; &#x0D;
The effect of implementing artificial intelligence on job performance in commercial banks of Jordan

This study aimed to clarify the effect of artificial intelligence with its variables (ES, NN, GA, and IA) on job performance. The banking sector in Jordan is used as a study community and targeted managers at all levels, and in order to achieve the objectives of the study, a questionnaire is developed for the purpose of collecting data from the random sample. The sample consisted of (319) managers. Also, the study used the descriptive approach and the data are analyzed on SPSS. The results showed that there is a statistically significant effect of artificial intelligence that affects job performance through (GA, and IA) only. In addition, the results showed that gender, academic qualification and years of experience have a statistically significant effect on job performance in commercial banks in Jordan. The study recommends preparing future research for the same variables and the study community, but for another country, in order to generalize the results.
Single Image Dehazing Using End-to-End Deep-Dehaze Network

Haze is a natural distortion to the real-life images due to the specific weather conditions. This distortion limits the perceptual fidelity, as well as information integrity, of a given image. Image dehazing for the observed images is a complicated task because of its ill-posed nature. This study offers the Deep-Dehaze network to retrieve haze-free images. Given an input, the proposed architecture uses four feature extraction modules to perform nonlinear feature extraction. We improvise the traditional U-Net architecture and the residual network to design our architecture. We also introduce the l1 spatial-edge loss function that enables our system to achieve better performance than that for the typical l1 and l2 loss function. Unlike other learning-based approaches, our network does not use any fusion connection for image dehazing. By training the image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing. Experimental results on synthetic and real-world images demonstrate that our model performs favorably against the state-of-the-art dehazing algorithms. We trained our network in an end-to-end manner and validated it on natural and synthetic hazy datasets. Our method shows favorable results on these datasets without any post-processing in contrast to the traditional approach.
Blockchain Technology's Status of Implementation in Oman: Empirical Study

The amount of data is increasing very fast annually due to the rapid changes in modern technology.The data becomes very huge in each organization which led to the emergence of the new concept called Big Data.Big Data creates a valuable chance for companies to maximize their outcomes by analyzing this type of data.However, there is a concern regarding user privacy in the centralized systems as they have limited control over their personal stored information and how to use it.Added to that is the need for third-party availability which is considered another security issue.Recently, new accountable systems have emerged called Bitcoin.The world started using blockchain technology by deploying the first application namely bitcoin.It is a digital currency used for trading things over the web like money in the real world.Due to the success of Bitcoin, blockchain technology has gained trust from users as it is used in various fields and services, for example, monetary institutions, Internet of Things (IoT), supply chain, election, medicinal handling, and stockpiling.The main idea of the blockchain is to establish distributed agreement in the digital online world.It opens a great opportunity for the digital economy to be more scalable than the current centralized one.There are huge wonderful features and chances upcoming with this novel technology, and the uprising in this area has just commenced.Due to the importance of this technology, this paper aims to assess the current status of implementing blockchain technology in Oman.The statistical results clearly show the weak implementation of this technology in Oman's sectors.
RETRACTED: Development of English teaching theory and practice research from the cross-cultural perspective

The progress of science and technology and the development of information technology have accelerated the speed of information dissemination and cultural transformation. In the context of multiculturalism, if we want to cultivate talents who can communicate across cultures, domestic English teaching needs further reforms. The unified implementation of English teaching in China has lasted for decades, and the research on teaching theory has gradually formed a stable framework. But from an overall point of view, instillation teaching under test-oriented education is not conducive to improving students' English practice level. In order to solve this problem, this article analyzes the cultural teaching content in college English teaching from a cross-cultural perspective, and emphasizes the importance of cultural infiltration in English teaching. At the same time, it analyzes the problems in teaching practice from multiple aspects of listening, speaking, reading, writing and translation, and puts forward suggestions for the construction of a cross-cultural communication ability training system. Experiments show that in the classes taught by ordinary English teachers, the average proportion of classroom culture teaching is only 14.995%; under the same conditions, the average proportion of classroom culture teaching in the classes taught by foreign teachers reaches 33.865%. Combined with the higher average scores of students in foreign teachers' classes, it can be known that cultural teaching can play a certain role in improving the level of comprehensive English teaching.
A Lightweight Policy Update Scheme for Outsourced Personal Health Records Sharing

With high flexibility and accessibility of data outsourcing environment such as cloud computing environment, several healthcare providers implement electronic personal health records (PHRs) to enable individual patients to manage their own health data in such resilient and scalable environment.However, PHRs contain highly sensitive information of which the security and privacy issues are the critical concern.Besides, PHRs owners should be capable to flexibly and securely define their own access policy for their outsourced data.In addition to the basic authentication feature, existing commercial cloud platforms usually provide symmetric or public key encryption as an optional feature to support data confidentiality for their tenants.However, such traditional encryption schemes are not suitable for data outsourcing environment because of high key management overhead of symmetric encryption and high maintenance cost for handling multiple copies of ciphertext for public key encryption solution.In this paper, we design and develop a secure and fine-grained access control scheme with lightweight access policy update for outsourced PHRs.Our proposed scheme is based on the ciphertext policy attribute-based encryption (CP-ABE) and proxy reencryption (PRE).In addition, we introduce a policy versioning technique to support the full traceability of policy changes.Finally, we conducted the performance evaluation to demonstrate the efficiency of the proposed scheme.
CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

Cloud data, the ownership of which is separated from their administration, usually contain users’ private information, especially in the fifth-generation mobile communication (5G) environment, because of collecting data from various smart mobile devices inevitably containing personal information. If it is not securely deleted in time or the result of data deletion cannot be verified after their expiration, this will lead to serious issues, such as unauthorized access and data privacy disclosure. Therefore, this affects the security of cloud data and hinders the development of cloud computing services seriously. In this paper, we propose a novel secure data deletion and verification (SDVC) scheme based on CP-ABE to achieve fine-grained secure data deletion and deletion verification for cloud data. Based on the idea of access policy in CP-ABE, we construct an attribute association tree to implement fast revoking attribute and reencrypting key to achieve fine-grained control of secure key deletion. Furthermore, we build a rule transposition algorithm to generate random data blocks and combine the overwriting technology with the Merkle hash tree to implement secure ciphertext deletion and generate a validator, which is then used to verify the result of data deletion. We prove the security of the SDVC scheme under the standard model and verify the correctness and effectiveness of the SDVC scheme through theoretical analysis and ample simulation experiment results.
Evaluation of Collaborative Filtering for Recommender Systems

Recently, due to the increasing amount of data on the Internet along with the increase in products' purchasing via e-commerce websites, Recommender Systems (RS) play an important role in guiding customers to buy products they may prefer.Furthermore, these systems help the companies to advertise their products to the most potential customers, and therefore raise their revenues.Collaborative Filtering (CF) is the most popular RS approach.It is classified into memory-based and model-based filtering.Memory-based filtering is in turn classified into user-based and item-based.Several algorithms have been proposed for CF.In this paper, a comparison has been performed between different CF algorithms to assess their performance.Specifically, we evaluated K-Nearest Neighbor (KNN), Slope One, co-clustering and Non-negative Matrix Factorization (NMF) algorithms.KNN algorithm is representative of the memorybased CF approach (both user-based and item-based).The other three algorithms, on the other hand, are under the model-based CF approach.In our experiments, we used a popular MovieLens dataset based on six evaluation metrics.Our results reveal that the KNN algorithm for item-based CF outperformed all other algorithms examined in this paper.