Aim/Purpose: Electronic examinations have some inherent problems. Students have expressed negative opinions about electronic examinations (e-examinations) due to a fear of, or unfamiliarity with, the technology of assessment, and a lack of knowledge about the methods of e-examinations. Background: Electronic examinations are now a viable alternative method of assessing student learning. They provide freedom of choice, in terms of the location of the examination, and can provide immediate feedback; students and institutions can be assured of the integrity of knowledge testing. This in turn motivates students to strive for deeper learning and better results, in a higher quality and more rigorous educational process. Methodology : This paper compares an e-examination system at FUT Minna Nigeria with one in Australia, at the University of Tasmania, using case study analysis. The functions supported, or inhibited, by each of the two e-examination systems, with different approaches to question types, cohort size, technology used, and security features, are compared. Contribution: The researchers’ aim is to assist stakeholders (including lecturers, invigilators, candidates, computer instructors, and server operators) to identify ways of improving the process. The relative convenience for students, administrators, and lecturer/assessors and the reliability and security of the two systems are considered. Challenges in conducting e-examinations in both countries are revealed by juxtaposing the systems. The authors propose ways of developing more effective e-examination systems. Findings: The comparison of the two institutions in Nigeria and Australia shows e-examinations have been implemented for the purpose of selecting students for university courses, and for their assessment once enrolled. In Nigeria, there is widespread systemic adoption for university entrance merit selection. In Australia this has been limited to one subject in one state, rather than being adopted nationally. Within undergraduate courses, the Nigerian scenario is quite extensive; in Australia this adoption has been slower, but has penetrated a wide variety of disciplines. Recommendations for Practitioners: Assessment integrity and equipment reliability were common issues across the two case studies, although the delivery of e-examinations is different in each country. As with any procedural process, a particular solution is only as good as its weakest attribute. Technical differences highlight the link between e-examination system approaches and pedagogical implications. It is clear that social, cultural, and environmental factors affect the success of e-examinations. For example, an interrupted electrical power supply and limited technical know-how are two of the challenges affecting the conduct of e-examinations in Nigeria. In Tasmania, the challenge with the “bring your own device” (BYOD) is to make the system operate on an increasing variety of user equipment, including tablets. Recommendation for Researchers: The comparisons between the two universities indicate there will be a productive convergence of the approaches in future. One key proposal, which arose from the analysis of the existing e-examination systems in Nigeria and Australia, is to design a form of “live” operating system that is deployable over the Internet. This method would use public key cryptography for lecturers to encrypt their questions online. Impact on Society : If institutions are to transition to e-examinations, one way of facilitating this move is by using computers to imitate other assessment techniques. However, higher order thinking is usually demonstrated through open-ended or creative tasks. In this respect the Australian system shows promise by providing the same full operating system and software application suite to all candidates, thereby supporting assessment of such creative higher order thinking. The two cases illustrate the potential tension between “online” or networked reticulation of questions and answers, as opposed to “offline” methods. Future Research: A future design proposition is a web-based strategy for a virtual machine, which is launched into candidates’ computers at the start of each e-examination. The new system is a form of BYOD externally booted e-examination (as in Australia) that is deployable over the Internet with encryption and decryption features using public key cryptography (Nigeria). This will allow lecturers to encrypt their questions and post them online while the questions are decrypted by the administrator or students are given the key. The system will support both objective and open-ended questions (possibly essays and creative design tasks). The authors believe this can re-define e-examinations as the “gold standard” of assessment.

This research introduces a hybrid honeypot architecture to bolster security within software-defined networks (SDNs). By combining low-interaction and high-interaction honeypots, the proposed solution effectively identifies and mitigates cyber threats, including port scanning and man-in-the-middle attacks. The architecture is structured into multiple modules that focus on detecting open ports using Vilhala honeypots and simulating targeted and random attack scenarios. This hybrid approach enables comprehensive monitoring and detailed packet-level analysis, providing enhanced protection against advanced online threats. The study also conducts a comparative analysis of different attack detection methods using tools like KFSensor and networking shell commands. The results highlight the hybrid honeypot system's efficacy in filtering malicious traffic and detecting security breaches, making it a robust solution for safeguarding SDNs.

Smart home devices are vulnerable to various attacks, including distributed-denial-of-service (DDoS) attacks. Current detection techniques face challenges due to nonlinear thought, unusual system traffic, and the fluctuating data flow caused by human activities and device interactions. Identifying the baseline for 'normal' traffic and suspicious activities like DDoS attacks from encrypted data is also challenging due to the encrypted protective layer. This work introduces a concept called synoptic crow search with recurrent transformer network-based DDoS attack detection, which uses the synoptic weighted crow search algorithm to capture varying traffic patterns and prioritise critical information handling. An adaptive recurrent transformer neural network is introduced to effectively regulate DDoS attacks within encrypted data, counting the historical context of the data flow. The proposed model shows effective performance in terms of low false alarm rate, higher detection rate, and accuracy.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

In order to shorten the integration and sharing time of English teaching resources, a MOOC English online and offline mixed teaching resource integration model based on convolutional neural networks is proposed. The intelligent integration model of MOOC English online and offline hybrid teaching resources based on convolutional neural network is constructed. The intelligent integration unit of teaching resources uses the Arduino device recognition program based on convolutional neural network to complete the classification of hybrid teaching resources. Based on the classification results, an English online and offline mixed teaching resource library for Arduino device MOOC is constructed, to achieve intelligent integration of teaching resources. The experimental results show that when the regularisation coefficient is 0.00002, the convolutional neural network model has the best training effect and the fastest convergence speed. And the resource integration time of the method in this article should not exceed 2 s at most.

In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

The study aims to review the literature on mobile wallet payment and align research trends using a systematic literature review with bibliometric and network visualisation analysis over two decades. It uses bibliometric analysis of the literature research retrieved from the Web of Science database. The study period was from 2001 to 2021, with 1,134 research papers. It also provides the indicators like citation trends, cited reference patterns, authorship patterns, subject areas published on the mobile wallet, top contributing authors, and highly cited research articles using the database. Furthermore, network visualisation analysis, like the co-occurrence of author keywords and keywords plus terms, has also been examined using VOSviewer software. The bibliometric analysis shows that the Republic of China dominates mobile wallet payment, and India is a significant contributor. Furthermore, the constructions of the network map using a co-citation analysis and bibliographic coupling shows an interesting pattern of mobile wallet payment.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.

The study involved student teachers enrolled in early childhood teaching at a teacher training institute in Hong Kong Special Administrative Region. Seventy-four students participated in flipped classroom activities during their first semester of study. Students were told to learn from online videos related to using image editing software in their own time and pace prior to the next class. When they met in class, they were asked to apply their recently acquired editing knowledge to edit an image of their own choice related to the theme of their group project. At the end of the activity, students were asked to complete an online questionnaire. It was found that students had rated all five questions relating to generic skills highly, with self-study skills rated the highest. They particularly enjoyed the flexibility of learning on their own time and pace as a benefit of the flipped classroom. Data collected from students’ project pages show they had used average of 3.22 editing features for the theme images for their project. Most groups had inserted text followed by using the filter function. It is possible that these two functions are more noticeable than other editing functions. In conclusion, students were able to apply their self-learnt knowledge in a real-life situation and they had also developed their generic skills via the flipped classroom pedagogy.

Aim/Purpose: The purpose of this paper is to illuminate the learning that happens in assuming a supervisee’s role during the postgraduate study. Background: The facilitators and barriers students encountered while pursuing postgraduate studies, strategies to achieve success in postgraduate studies, and how to decrease attrition rates of students, have been sufficiently explored in literature. However, there is little written about the personal and professional impact on students when they are being supervised to complete their postgraduate studies. Methodology: Autoethnographic method of deep reflection was used to examine the learning that transpired from the supervisee’s perspective. Two lecturers (a Senior Lecturer in Nursing and an Aboriginal Tutor) focused on their postgraduate journeys as supervisees, respectively, with over 30 years of study experience between them, in Australia and abroad. Contribution: Future postgraduate students, researchers, would-be supervisors and experienced supervisors could learn from the reflections of the authors’ postgraduate experiences. Findings: Four themes surfaced, and these were Eureka moments, Critical friend(s), Supervisory relationship, and Transformative learning. The authors highlighted the significance of a supervisory relationship which is key to negotiating the journey with the supervisor. Essential for these students also were insights on finding the path as well as the destination and the transformative aspects that happened as a necessary part of the journey. Conclusion. The postgraduate journey has taught them many lessons, the most profound of which was the change in perspective and attitude in the process of being and becoming. Personal and professional transformative learning did occur. At its deepest level, the authors’ reflections resulted in self-actualization and a rediscovery of their more authentic selves. Recommendations for Practitioners: This article highlights the importance of the supervisory relationship that must be negotiated to ensure the success of the candidate. Reflections of the transformation are recommended to support the students further. Recommendation for Researchers: Quality supervision can make a significant influence on the progress of students. Further research on the supervisory relationship is recommended. Impact on Society: The support in terms of supervision to ensure postgraduate students’ success is essential. Postgraduate students contribute to the human, social, professional, intellectual, and economic capital of universities and nations globally. Future Research: Further reflections of the transformative learning will advance the understanding of the personal and professional changes that occur with postgraduate supervision.

Aim/Purpose. The study aimed to examine the remote and face-to-face experience of pedagogical training in kindergarten after the third COVID-19 closure in Israel. Background. The outbreak of the COVID-19 epidemic in 2020 changed the training system, and preservice teachers were required to have their practical experience in the kindergartens both remotely and face-to-face. They had to adapt to the new requirements of teacher training programs and receive professional coaching and support from the pedagogical instructor remotely. Methodology. The sample comprised 26 early childhood preservice teachers, who received academic training that includes proficiency in digital technology. The data were collected through feedback that they wrote themselves during the training period and analyzed in the interpretive approach. Contribution. The contribution of the present study is that it examines the pedagogical coaching from the perspective of preservice teachers in a kindergarten during the COVID-19 epidemic, which forced a transition from face-to-face to remote pedagogical training, then back to face-to-face pedagogical instruction. To the best of my knowledge, no such study has been carried out to date, which makes it unique. Findings. The main findings indicate the dissatisfaction of most preservice kindergarten teachers with the remote pedagogical training (about 85%) at the physical, emotional, technological, and pedagogical levels, and the satisfaction of most preservice kindergarten teachers with face-to-face pedagogical training (about 92%) at the physical, emotional, and pedagogical levels. The main conclusion is that technology is a potential barrier in training, and that preservice kindergarten teachers need a pedagogical instructor present at a professional face-to-face meeting. Recommendations for Practitioners. The findings of the study show how important in-person learning and engagement is for everyone especially for Preservice teachers’ and may be helpful for pedagogical coaching teams. Recommendations for Researchers. Preservice teachers’ awareness of the pedagogical coaching experiences could persuade the coaching teams to avoid potential difficulties, increase emotional support, and refine the use of technology to make it a closer substitute for frontal communication. Impact on Society. Face-to-face training based on interpersonal relationship, allows to develop better during the training period.