Reviewerly/exHarmony huggingface dataset:
Digital proficiency: Sorting real gaps from myths among higher education students

Abstract Digital competence is among the basic key competences for digital learning and employability. For this reason, its acquisition and development should be on the agenda of higher education institutions (HEIs) who wish to prepare their students to thrive in an ever faster evolving digital labour market. However, the existence of a valid instrument that can help HEIs measuring and further integrating digital competence into pedagogical and organisational practices with sufficient precision is yet to be accomplished. This article provides a valid and reliable instrument to measure higher education students' digital competence on the basis of the European Digital Competence Framework for Citizens, also known as DigComp. The instrument was applied to a sample of 411 students from a mid‐large public HEI and the results attest its validity and reliability. In addition, the study explores proficiency differences among students from different fields of education and training, and gender. Results demystify the idea that ICT students are more digitally competent than those in other fields of study, but suggest males score higher than females, which feeds into the ongoing debate of gender differences in relation to digital technologies and the readiness of females for the digital labour market. The results lead to clear implications for research and practice. Practitioner notes What is already known about this topic Digital competence is critical for higher education (HE) students to benefit from digital learning, strive in a digital society and increase employability prospects. There is a lack of valid instruments to measure higher education (HE) students' digital competence and facilitate the identification of digital competence gaps. Few studies focus on the relationship between HE students' digital competence, gender and fields of education and training (FET). What this paper adds A valid and reliable instrument based on a common European framework for digital competence. HE students lack the necessary digital competences to effectively cope with digital environments. Digital proficiency is particularly low regarding “Safety”, “Problem solving” and “Digital content creation” competence areas. ICT students show less proficiency than those from other FET. Male students score higher than females. Implications for practice and/or policy The instrument can be adopted by different stakeholders to assess students/future job seekers' digital competence. HE institutions could benefit from such an instrument as a diagnosis to design specific teaching and learning strategies and target students' proficiency and particular needs. Tackling specific FET and competence areas can better support the development of students' digital competence and facilitate their employability prospects.
Financial innovations and economic growth: Does financial inclusion play a mediating role?

Innovations in the financial sector play a critical role in promoting economic growth. Studies that have sought to investigate this linkage in sub-Saharan Africa have produced mixed results. None of the existing studies have attempted to examine the possible mediating role of financial inclusion in explaining the relationship between innovations and growth. This paper thus sought to establish if financial inclusion mediates the relationship between innovation and growth. Secondary data from (26 selected SSA countries over the period 2004 to 2017 were used. The data were analysed using the GMM estimation technique. It was found amongst other things that investments in innovations in the banking sector promote financial inclusion. In addition, financial inclusion fully mediates the relationship between innovation and economic growth. It is thus recommended that governments in the sub-region invest in the appropriate technological infrastructure that the banking sector can leverage on in the provision of banking services as the key to promoting financial inclusion and economic growth.
A patent registration and trading system based on blockchain

The traditional process of patent registration and trading is very complex, often hindered by challenges related to information security and patent traceability. As a popular emergent technology, blockchain has several useful characteristics such as tamper proof, distributed data management, timestamp, asymmetric encryption, and smart contract. Built upon these characteristics, this paper develops a patent registration and trading system for improved information security, immutability, transparency, and traceability. This system includes functions of patent registration and storage, patent trading, user privacy protection during patent registration and trading process, and overall data access control. To validate the applicability of the proposed system, this paper builds a blockchain environment to simulate a patent registration and trading process. We further conduct a security analysis and an efficiency analysis to compare the proposed patent registration and trading process with the traditional process. Through these comparisons we show that the proposed blockchain-aided patent registration and trading system is effective in resolving several key patent protection challenges. Theoretical and managerial implications are also discussed to highlight the significance of our proposed method.
The Missing Case of Disinformation from the Cybersecurity Risk Continuum: A Comparative Assessment of Disinformation with Other Cyber Threats

This study examines the phenomenon of disinformation as a threat in the realm of cybersecurity. We have analyzed multiple authoritative cybersecurity standards, manuals, handbooks, and literary works. We present the unanimous meaning and construct of the term cyber threat. Our results reveal that although their definitions are mostly consistent, most of them lack the inclusion of disinformation in their list/glossary of cyber threats. We then proceeded to dissect the phenomenon of disinformation through the lens of cyber threat epistemology; it displays the presence of the necessary elements required (i.e., threat agent, attack vector, target, impact, defense) for its appropriate classification. To conjunct this, we have also included an in-depth comparative analysis of disinformation and its similar nature and characteristics with the prevailing and existing cyber threats. We, therefore, argue for its recommendation as an official and actual cyber threat. The significance of this paper, beyond the taxonomical correction it recommends, rests in the hope that it influences future policies and regulations in combatting disinformation and its propaganda.
How deep learning is empowering semantic segmentation

Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning.
Multiple-image encryption algorithm based on genetic central dogma

Abstract A large number of images are generated and distributed in various fields every day. To protect the image information from being stolen during the network transmission, this paper proposes a multiple-image encryption algorithm based on genetic central dogma and three-dimensional (3D) bit planes. This paper simulates the genetic central dogma and defines the 3D bit planes. Firstly, k original images are transformed into 8-bit binary and converted into a 3D matrix; secondly, the 3D matrix is permutated by rotating the bit plane and performing permutation among the bit planes; thirdly, we encode the scrambled 3D matrix into DNA codes. The diffusion is realized by imitating the genetic central dogma and introducing RNA mutations; finally, the encrypted images are obtained by the RNA decoding operation. Experimental results and algorithm analyses indicate that the proposed algorithm has strong security and desirable performance.
Auto-CSC: A Transfer Learning Based Automatic Cell Segmentation and Count Framework

Cell segmentation and counting play a very important role in the medical field. The diagnosis of many diseases relies heavily on the kind and number of cells in the blood. convolution neural network achieves encouraging results on image segmentation. However, this data-driven method requires a large number of annotations and can be a time-consuming and expensive process, prone to human error. In this paper, we present a novel frame to segment and count cells without too many manually annotated cell images. Before training, we generated the cell image labels on single-kind cell images using traditional algorithms. These images were then used to form the train set with the label. Different train sets composed of different kinds of cell images are presented to the segmentation model to update its parameters. Finally, the pretrained U-Net model is transferred to segment the mixed cell images using a small dataset of manually labeled mixed cell images. To better evaluate the effectiveness of the proposed method, we design and train a new automatic cell segmentation and count framework. The test results and analyses show that the segmentation and count performance of the framework trained by the proposed method equal the model trained by large amounts of annotated mixed cell images.
Enhance prototypical networks with hybrid attention and confusing loss function for few-shot relation classification

Relation classification (RC) is a fundamental task to building knowledge graphs and describing semantic formalization. It aims to classify a relation between the head and the tail entities in a sentence. The existing RC method mainly adopts the distant supervision (DS) scheme. However, DS still has the problem of long-tail and suffers from data sparsity. Recently, few-shot learning (FSL) has attracted people’s attention. It solves the long-tail problem by learning from few-shot samples. The prototypical networks have a better effect on FSL, which classifies a relation by distance. However, the prototypical networks and their related variants did not consider the critical role of entity words. In addition, not all sentences in support set equally contributed to classifying relations. Furthermore, an entity pair in a sentence may have true and confusing relations, which is difficult for the RC model to distinguish them. A new context encoder BERT_FE is proposed to address those problems, which uses the BERT model as pre-training and fuses the information of head and tail entities by entity word-level attention (WLA). At the same time, the sentence-level attention (SLA) is proposed to give more weight to sentences of the support set similar to the query instance and improve the classification accuracy. A confusing loss function (CLF) is designed to enhance the model’s ability to distinguish between true and confusing relations. The experiment results demonstrate that our proposed model (HACLF) is better than several baseline models.
Vehicle detection using improved region convolution neural network for accident prevention in smart roads

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data, which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances.
Human–machine trans roles conflict in the organization: How sensitive are customers to intelligent robots replacing the human workforce?

Abstract In organizations facing digital transformation, intelligent technologies are starting to replace the human workforce. At present, managers delegate tasks to an artificial agent and rarely consider the customer reception of such decisions. This arouses tensions between the main stakeholders of the organization. This paper shows that the rash adaptation of the digital workforce may be perceived as an irresponsible innovation that brings negative consequences for companies. If a task is regarded by customers as dedicated to humans, and managers delegate it to machines, a new type of conflict – human‐machine trans roles conflict (HMTRC), appears. This paper intends to show that customers are sensitive to HMTRC. This research uses quantitative methods and consists of three stages. First, people were asked to indicate which tasks in an organization should be performed by (a) humans and (b) machines. According to these results, two leaflets for customers were designed (low vs. high HMTRC). In the second stage, standard procedures were used to construct a scale measuring customer reactions to HMTRC on three dimensions: cognitive, emotional and behavioural. Ultimately, the scale and two leaflets were used to check how customers react to different intensities of HMTRC. The research results show that customers are aware when HMTRC occurs and perceive it negatively (cognitive response). Moreover, it evokes negative emotions (emotional response) and prompts customers to take action against the company in which this conflict takes place (behavioural response). The practical contribution of this research is the three‐dimensional scale. It may predict customers' reactions to task delegation with different intensities of HMTRC and help build a technologically sustainable organization.
A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial, researchers have turned attention toward the Sixth-Generation (6G) network with the vision of connecting intelligence in a green energy-efficient manner. Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency. However, designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients' contributions during the learning process. In this paper, we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning. The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning. Meanwhile, clients’ contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks, which satisfies availability, fairness, and additivity. The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.
Enhanced instance space analysis for the maximum flow problem

The Maximum Flow Problem (MFP) is a fundamental network flow theory problem, for which many algorithms, supported by strong theoretical worst-case analyses, have been proposed. However, their practical efficiency depends on the network structure, making it unclear which algorithm is best for a particular instance or a class of MFP. Instance Space Analysis (ISA) is a methodology that provides insights into such per-instance analysis. In this paper, the instance space of MFP is constructed and analysed for the first time. Novel features from the networks are extracted, capturing the performance of MFP algorithms. Additionally, this paper expands the ISA methodology by addressing the issue of how benchmark instances should be selected to reduce bias in the analysis. Using the enhanced ISA methodology with MFP as the case study, this paper demonstrates that the most important features can be detected, and machine learning methods can identify their impact on algorithm performance, whilst reducing the bias caused by over-representation within the selected sample of test instances. The enhanced methodology enables new insights into the performance of state-of-the-art general purpose MFP algorithms, as well as recommendations for the construction of comprehensive and unbiased benchmark test suites for MFP algorithm testing.
Learning with digital technology-facilitated empathy: an augmented reality approach to enhancing students’ flow experience, motivation, and achievement in a biology program

Biology involves the understanding of life concepts and the exploration of life processes. Due to the complexity of life, the learning content of biology is often microscopic, abstract, and complex. In traditional teaching, The non-visualization of abstract concepts, rote memorization of knowledge and low classroom participation tend to make students feel dull and eventually lead to inefficient and low confidence in learning. Augmented reality (AR) superimposes 3D dynamic virtual information into the real world and visualizes abstract concepts to deepen students’ understanding of concepts. In addition, AR supports virtual-real interaction and instant feedback, which helps to stimulate learning interest and reinforce the learning effect. Therefore, the application of AR can solve the problems of traditional biology teaching. This study used a quasi-experimental approach to explore the potential of AR in teaching biology. A total of 81 third grade students were recruited for the study. The experimental group (40 students) was taught with AR, while the control group (41 students) was taught with traditional instruction. The study found that AR instruction significantly increased students’ flow experiences and motivation to learn and reduced their cognitive load. However, there were no significant differences in self-efficacy and academic performance between the two groups.
Financial development and entrepreneurship: insights from Africa

Purpose One of the main obstacles to the flourishment of African entrepreneurship is financial constraint. Existing studies on the nexus between entrepreneurship and financial development are inconclusive, while the position of African economies remains unknown. The purpose of this paper is to empirically study the impact of financial development on entrepreneurship in Africa. Design/methodology/approach This study utilizes data of 20 selected countries in Africa over a period of 2006–2017. International Monetary Fund (IMF) data on broad-based financial development were combined with World Bank Entrepreneurship database. This study uses system generalized methods of moments (system GMM) technique and the recently developed dynamic panel threshold based on dynamic panel GMM. Findings The following findings emerged: financial development does not spur entrepreneurship in Africa; there is a threshold at which financial development improves the level of African entrepreneurship; and the tendency of financial development to improve the level of entrepreneurship is conditioned on conducive business regulation and strong institutional quality at a specific threshold value. Originality/value This is one of the few studies that examines the impact of financial development on entrepreneurship in Africa. This study shows that the financial development relies on the effectiveness of regulatory environment to extend loan and other financial services to new firm entrants. In addition, the results of this study reveal that the assumption of linearity in the nexus between finance and entrepreneurship is not tenable for the case of Africa. Therefore, policymakers should keep on developing African financial system to accelerate the pace of entrepreneurship development.