Reviewerly/exHarmony huggingface dataset:
Assessing The Factors of Cybersecurity Awareness in the Banking Sector

The purpose of this paper is to identify the factors of cybersecurity awareness in the banking sector. Literature shows several gaps that both top management and cybersecurity professionals must close to construct a successful digital institution in the conviction- and assurance-based economy. These gaps indicate four factors, top management commitment and support; budgeting; cybersecurity compliance; and cybersecurity culture. Methodology: A quantitative approach is used with questionnaire analysis. A total of 109 Information Technology (IT) employees completed a self-administrated survey from six Bahraini Islamic retail banks and five Bahraini conventional commercial retail banks. Descriptive analysis with percentage and a simple mean-based ranking of indicators used to analyze the data. Findings reveal the highest mean is 4.28 for security compliance. The lowest mean for Cybersecurity Culture at 4.24 concludes that all the factors are significant for cybersecurity awareness. Respondents strongly agreed with the necessity of these factors in the banking sector. The research limitation due to the insufficient information in the literature regarding the proposed combination of factors recommended. Practical implications for policymakers and cybersecurity specialists: This study provides a vital factor that may help improve policies or guidelines for successful cybersecurity awareness in organizations. To recognize cyber threats, cyber-attacks impact, and how to diminish cyber risk and avoid cyber-crime penetrating their cyberspace. Originality/value fills a gap in the literature to construct a successful digital institution in the conviction- and assurance-based economy. This study helps managers direct and proceed with their daily activities, where maintaining the cybersecurity component is significant. A cybersecurity component is a defense and safeguards the firm’s financial information, intellectual properties, and reputation against unauthorized parties. Moreover, the cybersecurity component concerns the organization and the public individuals exposed to cyber threats through their electronic digital media such as smartphones, personal computers, and Internet protocol systems. However, there is insufficient literature on the proposed combination of factors recommended as factors relating to cybersecurity awareness in the banking sector.
The effect of technology-assisted language programme on vocabulary learning among EFL students at the tertiary level

Mobile technologies have drawn the attention of the researchers and are gaining popularity in a variety of academic backgrounds. However, there is currently unavailability of studies on the benefits that various mobile technologies give to learning results. The objective of this current study was to examine how the mobile assisted language programme, WhatsApp affected Bangladeshi tertiary level EFL learners’ vocabulary learning. The study included sixty-four EFL students who had intermediate level of English competency, and they were chosen using random sampling. After establishing the learners’ homogeneity with a vocabulary examination/test, the researchers separated the participants into two categories, namely experimental group (N = 34) and control (N = 30) group, and then their vocabulary knowledge (i.e., synonyms and antonyms) was assessed. The research involved 14 session treatment with the control group using the usual traditional method and the experimental section using WhatsApp to teach new words. A vocabulary post–test for the both classes was conducted after the completion of the course. In addition, both groups were given an attitude questionnaire to see how they felt about the traditional vocabulary acquisition method and utilizing the WhatsApp approach. According to the findings of this study, the experimental group performed better than the control group. Furthermore, as compared to the other group, learners who utilized WhatsApp to improve their vocabulary skills showed a more favourable attitude toward learning foreign language vocabulary. The current research would have further ramifications for English instructors, learners, researchers, and other stakeholders as well.
Clingraph: ASP-Based Visualization

We present the ASP-based visualization tool clingraph, which aims at visualizing ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz. The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a Python API that extends this ease of interfacing to clingo’s API, and in turn to connect and monitor various aspects of the solving process.
Overview of software and file exchange formats in 3D and 4D body shape scanning

For capturing only static models, 3D body scanning is well known in various application areas such as medicine, automotive, sports, clothing, product design and gaming. These models have some limitations in that they are unable to capture dynamic poses that can provide more information about real-time tasks and interactions with a real-life object, machine, or environment. As a result, in the literature, to provide a more realistic movement of static shape models, researchers provided an idea of attributing kinematic capturing or "skeletal animation" (as a Biovision Hierarchy, or BVH, file) using a wearable inertial mocap system applied to a 3D statistical shape model, obtaining a "moving statistical shape" using exchange format in open-source software like Blender. But in this case, the attribution was not a perfect attribution of the real-time capturing of a dynamic 3D body shape, obtained today thanks to 4D body scanning. Nowadays, 4D body scanning can perform 4D measurements of dynamic body shape in real time without using any wearable inertial mocap system that can occlude the scanning surface; this is a comfortable solution that does not influence the performance of the user. In addition, 3D and 4D can be used in open- and closed-source software using specific file exchange formats for modeling and animation or interaction and integration with other devices (e.g., synchronization with pressure mat, force platform, etc.). In particular, open-source software represents a more intuitive, fast, and inexpensive platform for performing animation, modeling, and file exchange formats in a multidisciplinary approach. Based on the previous assumptions, in this study, we will provide an overview of open- and closed-source software along with file exchange formats in 3D and 4D body scanning, looking at the advantages and disadvantages of their use in different fields of applications.
Social media and the role of libraries during the COVID-19 pandemic

The COVID-19 pandemic has had a huge impact on a person’s life, both physically and mentally. With the amount of information available, people can use this information to overcome the anxiety they face. This study aims to identify the information behavior of COVID-19 survivors and the role of libraries in social media as information institutions during the COVID-19 pandemic. The research method used is quantitative descriptive. Data collection was carried out through a survey using Google Forms. The number of data collected was 148 respondents. The location was East Java, including Surabaya, Sidoarjo, Tuban, Bangkalan, Magetan, Blitar, Situbondo, Jombang, Gresik, Kediri, Ponorogo, Lamongan, Madiun, Ngawi, Banyuwangi, and Tulungagung. The results show that most people fulfill their information needs regarding the treatment of COVID and its development (48.6%), as well as a healthy lifestyle (15.5%) through social media. In accessing information, a small number of respondents experienced obstacles such as the amount of hoax information (6.1%), the available information was considered incomplete (5.4%), limited internet quota (4.1%), and problematic network signal (4.1%). Based on these results, we need the role of libraries that are able to provide information to meet the information needs of users.
Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

Unsupervised machine learning plays a critical role in improving the security level of applications and systems.The cyberattack floods the network with data streams to deny services or destroy the network infrastructure.In this paper, a new development strategy (dynamic evolving cauchy possibilistic clustering based on the self-similarity principle (DECS)) is proposed to optimize the data stream clustering model based on the self-similarity principles (inter-cluster and intra-cluster).It is based on computing the self-similarity principles for data between and within clusters.The proposed system mitigates the dependence on the centre of the cluster to produce clusters with highly correlated data and minimal errors within the same cluster.The DECS consists of two phases: In the first phase, the data are clustered based on density points to generate clusters, and in the second phase, the clusters are evolved according to the inter-and intra-distance points to/in the clusters.The proposed model is tested on well-known intrusion datasets such as UNSW-NB15, KDD99 and NSL-KDD.In the experiments, DECS efficiently clustered the data with fewer errors and an optimal number of clusters compared to other models, it has achieved minimum error, high silhouette coefficient and an optimal number of clusters.The implementation results show that the average error of DECS over eight datasets is (0.45) with an average silhouette coefficient (0.63).At the same time, the pure cauchy possibilistic clustering obtains on average error (0.69) and silhouette coefficient (0.44).
Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm

This paper discusses the best path planning algorithm for an autonomous mobile robot in an unknown environment with irregular static and dynamic obstacles and a static and dynamic target based on the improved COOT (ICOOT) optimization algorithm.The ICOOT overcomes the drawbacks of unstable searches in the conventional COOT.The path planning problem is solved by finding the collision-free path between multi-objective shortest path and smoothness.The ICOOT tries to imitate the real world by adding the mobile robot's actual size and the kinematic model with specifications for mobile robots.In order to evaluate the proposed algorithm, thirteen benchmark test functions are used to make a comparison with 30, 100, and 500 dimensions.To test the efficiency of the proposed technique, results are compared with five swarm optimization algorithms.The standard deviation results show that the proposed algorithm gets the best results in 84% of the thirteen test functions for 30 dimensions and 92% for 100 and 500 dimensions.Also, in four complex maps (10×10) m, the mean results show that this method is very useful for robot paths from the start to the target, and the mean distance for ten runs is 13.3797 m for map 1, 13.5164 m for map 2, and 11.9312 m for map 3, and 16.3937 m for map 4. It showed how well it could move quickly and easily around both fixed and moving obstacles.
Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

As a major piece of equipment in the electrical power transmission and distribution, the rapid and accurate assessment of emerging or existing internal faults in power transformers is a key factor in the safe and stable operation of the power grid.This paper proposes a hybrid approach to fault diagnosis for these transformers.This approach is based on ensemble bagged tree classification and training subsets obtain by a conventional pre-processing method.Two pre-processing approaches are performed, the first based on the maximum concentrations of the dissolved gas samples and the second based on the minimum concentrations of the dissolved gas samples.For each training subset, an ensemble classifier is constructed with as inputs the Rogers ratios, Gouda ratios, dissolved gas concentrations and their associations.The proposed hybrid methods are established with 475 samples of training dataset, tested on 117 samples dissolved gas analysis (DGA) data and validated on International Electrotechnical Commission (IEC) TC10 database.The performances of the proposed diagnostic models are evaluated and a comparison is done compared with the following diagnostic methods: IEC ratios method (IRM), Rogers ratios method (RRM), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters.The results found by computer simulations carried out by the matrice laboratory (MATLAB) software show that, of the two pre-processing approaches used, the one based on the minimum sample concentration gives better results than the one based on the maximum concentration.In terms of fault type, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 94.02%, compared to 92.31% for the best diagnostic model using the maximum concentrationbased pre-processing approach.This is lower than the 97.25% for SOM clusters and 96.58% for GT but higher than the 59.83% for RRM, 81.19% for IRM and 93.16% for TRT.In terms of fault severity, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 81.20%, compared to 74.36% for the best diagnostic model using the minimum concentration-based pre-processing approach.This result is lower than the 83.76% for TRT and 89.74% for GT but higher than the 49.57% for RRM, 66.67% for IRM and 78.90% for SOM clusters.
A comprehensive Survey on Blockchain-Based Solutions to Combat Covid-19 Pandemic

COVID-19 pandemic has taken the world by surprise in the beginning of 2020.Initially detected in Wuhan, China, the newly discovered coronavirus has spread all over the world in a short time, which pushed the World Health Organization to declare the disease as a Public Health Emergency.The virus spread transmission primarily occurs between individuals when a contaminated person is in close contact with another person.Thus, contact tracing, the process of identifying and managing potential contacts that have been in a close contact with an infected person is a critical process toward limiting the transmission of the disease.Healthcare professionals with the help of researchers from academia investigated different techniques to deal with contact tracing.In this survey, we focus solely on Blockchain-based techniques that have been proposed so far to deal with contact tracing.We also survey existing Blockchain based applications and the privacy issues they may led to.We compare and evaluate contact-tracing applications based on a number of criteria, notably privacy, security, scalability, proximity estimation, possible attacks, latency, throughput, and resource utilization.We also present techniques that integrated Blockchain with Artificial Intelligence and we conclude this study by shedding light on the remaining limitations, challenges and future directions.
Towards efficient processing of latency-sensitive serverless DAGs at the edge

Many emerging novel applications expect "near real-time" processing and responses, which can not be guaranteed by today's Cloud and would require processing at the Edge. Serverless computing is a particularly promising architecture for edge environments since it offers to improve efficiency by precisely scaling resources to meet application needs. As the edge applications grow more complex and get composed from a subset of simpler functions or microservices, there is a need to support more complicated function topologies which can be represented as directed acyclic graphs (DAGs). However, running DAG functions on a serverless platform poses new challenges related to interconnecting, instantiating, and scheduling function sandboxes. In this paper1, we explore how Sledge, a Wasm-based serverless runtime, can be extended to support DAG functions. Sledge's unique design allows for extremely lightweight sandbox instantiation - a new sandbox can be started for each function invocation in under 30μsec - which mitigates the cold start problems that can be especially detrimental to DAGs. Rather than relying on expensive coordination via shared storage, the enhanced Sledge framework provides a fast memory communication channel to propagate data through the DAG. We consider the DAGs with service level objectives, defined by their execution deadlines. To ensure the DAGs meet their performance requirements, we consider, analyze, and compare two deadline-aware pluggable schedulers (that we implemented in Sledge) on a variety of realistic workloads.
PERSPECTIVE OF ETHICAL ISSUES AND CHALLENGES OF BIOTECHNOLOGY IN DEVELOPING COUNTRY

&#x0D; &#x0D; &#x0D; Advanced biotechnology should be paralleled with advancing ethics to address its implications. In the Middle East and North Africa (MENA) region, ethics of research and advanced biotechnology is not well-developed among scientists. There is also a lack of religious scholars’ knowledge about advancing technology to make 'fatwa' (religious edict in Islam) to the public regarding the use of such biotechnology. The purpose of this study was to investigate the perception of scientists and religious scholars in Iraq regarding the ethics and religious perspectives of different biotechnology experiments. A convenient non-probability sampling cross-sectional study was carried out in Iraq to figure out the Islamic and scientific scholar's views on technological advances in science including stem cell research, cloning, genetically modified crops, human gene editing, smartphone technology for health services and other advances in biotechnology research. A sample size of 125 science and 34 Islamic scholars were recruited from universities. Face to face interviews was used to collect the data. Descriptive statistics include the use of frequencies, and means, to present the results. Among 159 science and Islamic scholars only (18.2%) think religion can help using such technology by giving guidance and advice. A majority (52.8%) think unexpected and unknown results are the most important ethical questions in such research. Participants heard the most about vegetable genetic modification biotechnology (80.5%) while only (31.4%) heard about mosquito gene modification. The risk of environmental corruption was the most important ethical challenge among scholars (42.6%). More than one-third (35.8%) of participants believed that faith and religious belief have an impact on such biotechnologies but they also said no specialized individual is dealing with such research. Furthermore, (41.5%) of them think it is important for the public to know about ethical matters related to biotechnology research to protect their health and future. There are misconceptions and a lack of information about the ethical issues concerning advanced biotechnology experiments among science researchers and Islamic scholars. However, there was awareness of ethical challenges related to some of these technologies.&#x0D; &#x0D; &#x0D;
Image matching via the local neighborhood for low inlier ratio

Establishing reliable correspondences in an image pair is a prerequisite and crucial in computer vision. It remains a difficult topic to separate true and false matches in the given putative dataset with a low inlier ratio. To address this problem, an image matching method is proposed, via the local neighborhood of feature points. Grid-based motion statistics are initially engaged to preprocess the putative dataset, especially in which the inlier ratio is low. The local neighborhood distributions of feature points are then collected as the quality function of correspondences. Progressive sample consensus is next employed to estimate a global deformation for removing false matches. Robust experiments on nine typical image pairs with low inlier ratios demonstrate the superiority of our proposed method over five state-of-the-art methods. The comparison experiments on the Oxford dataset illustrate that our method outperforms the other five image matching methods.
Overview of Image Inpainting and Forensic Technology

At present, digital image is the most significant information carrier on the Internet, which provides convenience for people to exchange information. With the progress of image processing theory, digital image inpainting technology has also developed rapidly. It not only enhances the image expression but also provides tools for image forgery. Misusing the tools may trigger a crisis of trust in the image. To eliminate the negative impact of image forgery, many scholars have conducted in-depth research on it and proposed a series of detection methods called image forensics for institutions or communities to identify such forged images. Current forensic technology still has many limitations, such as relying on specific features and data distribution which makes forensic technology lag behind image inpainting technology. So far, academia still lacks a unified understanding of image forgery and detection technology, and the research architecture of inpainting and forensic technology is not clear. This article reviews the development of image inpainting and forensic technology and systematically summarizes and scientifically classifies the current research work. Finally, the forensic technology is summarized and the future research direction is prospected to guide subsequent scholars to further promote the progress of image forensic technology.
Efficient Factorisation-based Gaussian Process Approaches for Online Tracking

Target tracking often relies on complex models with non-stationary parameters. Gaussian process (GP) is a model-free method that can achieve accurate performance. However, the inverse of the covariance matrix poses scalability challenges. Since the covariance matrix is typically dense, direct inversion and determinant evaluation methods suffer from cubic complexity to data size. This bottleneck limits the GP for long-term tracking or high-speed tracking. We present an efficient factorisation-based GP approach without any additional hyperparameters. The proposed approach reduces the computational complexity of the Cholesky decomposition by hierarchically factorising the covariance matrix into off-diagonal low-rank parts. Meanwhile, the resulting low-rank approximated Cholesky factor can also reduce the computation complexity of the inverse and the determinant operations. Numerical results based on offline and online tracking problems demonstrate the effectiveness of the proposed approach.
An Efficient, Verifiable, and Dynamic Searchable Symmetric Encryption with Forward Privacy

Dynamic Searchable Symmetric Encryption (DSSE) allows a cloud server to perform search queries on a user’s documents while both queries and files are encrypted. It also enables the user to update the corpus efficiently. Recently, the notion of Forward Privacy (FP) was introduced, which guarantees the privacy of a newly added document in the presence of previous queries. However, most of the existing approaches work only with honest-but-curious servers. In these schemes, it is assumed the cloud server follows the prescribed protocols, but due to the untrusted nature of the cloud servers, this assumption does not always hold in practice. Hence, it is essential to design and implement new approaches that verify the results of queries and detect malicious behavior of a cloud server. In this paper, we construct a new forward-private DSSE scheme that efficiently achieves result verifiability. To obtain this goal, along with the search results, the server provides a "proof of work" to demonstrate result completeness. Moreover, our approach support searches and updates efficiently. Finally, the security and practicality of our scheme is demonstrated by providing performance analysis and security proof.