Higher education institutions (HEIs) are increasingly becoming vulnerable to cyberattacks as they adopt digital technologies to support their administrative, research and academic activities. These institutions, which typically operate in open and decentralized environments, face serious challenges as a result of the growing complexity of cyberattacks such as phishing, ransomware and data breaches. This systematic review synthesizes existing literature on cybersecurity in HEIs, identifying key challenges, emerging solutions and current trends. The review analyses the adoption of advanced technologies such as zero trust architectures (ZTAs), artificial intelligence (AI)-driven security and cloud-based systems. Furthermore, it investigates the underlying causes of cybersecurity vulnerabilities, including fragmented security procedures, lack of proper awareness about cybersecurity among users and associated technology gaps. The review also examines how governance frameworks, institutional policies and the incorporation of state-of-the-art security technologies can significantly mitigate these threats. Findings reveal that considerable progress has been made by some institutions in implementing security measures. However, comprehensive cybersecurity plans that integrate technological solutions with a robust institutional culture of cybersecurity awareness are still critically needed. The review concludes by highlighting the need for HEIs to collaborate and foster institution-wide partnership to strengthen cybersecurity measures. Finally, an in-depth study into the strategies and best practices for handling emerging cyberthreats in the HEIs is recommended.

The Extreme Learning Machine (ELM) is a simplified neural network. It non-linearly embeds input data in a higher dimensional space using randomly generated sigmoidal basis functions. The training target vector is then approximated by a linear weighted sum of these basis functions. In this paper the ELM algorithm has been applied to classify static hand gestures that represent different letters of the Auslan(Australian Sign Language) dictionary. ELM offers very fast learning with very consistent performance. It has only one tuning parameter. It can be adapted to multiclass classification and multi output regression without any increase in training time. Its low computational intensity and short training time makes it superior to traditional algorithms such as Hidden Markov Models (HMMs), Single and Recurrent Feed Forward Neural Networks for real time translations. Preliminary experimental results have shown that ELM can produce good generalization performance as a classifier. Increasing the dimensionality of the data results in better separation. Consequently the gestures become more distinguishable improving the probability of correct classification. An investigation into its classification performance for the entire alphabet is currently under way.

Computer vision aims to enable machines to understand the visual world. Computer vision encompasses numerous tasks, namely action recognition, object detection and image classification. Much research has been focused on solving these tasks, but one that remains relatively uncharted is light enhancement (LE). Low-light enhancement (LLE) is crucial as computer vision tasks fail in the absence of sufficient lighting, having to rely on the addition of peripherals such as sensors. This review paper will shed light on this (focusing on video enhancement) subfield of computer vision, along with the other forementioned computer vision tasks. The review analyzes both traditional and deep learning-based enhancers and provides a comparative analysis on recent models in the field. The review also analyzes how popular computer vision tasks are improved and made more robust when coupled with light enhancement algorithms. Results show that deep learners outperform traditional enhancers, with supervised learners obtaining the best results followed by zero-shot learners, while computer vision tasks are improved with light enhancement coupling. The review concludes by highlighting major findings such as that although supervised learners obtain the best results, due to a lack of real-world data and robustness to new data, a shift to zero-shot learners is required.

Enterprise networking optimization has become crucial recently due to increasing demand for a secure, adaptable, reliable, and interoperable network infrastructure. Novel techniques to optimize network security and toimprove scalability and efficiency are constantly being developed by network enablers, particularly in more challenging multi-cloud and edge scenarios. This paper, therefore, presents a comprehensive review of the traditional and most recent developments in enterprise networking. We structure the paper with particular emphasis on the adoption of state of-the-art technologies, such as software-defined wide area network(SD-WAN), secure access service edge (SASE) architecture, and network automation, driven by artificial intelligence (AI). The review also identifies various challenges associated with the adoption of the aforementioned technologies. These include operational complexity, cybersecurity threats, and trade-offs between cost-effectiveness and high performance requirements. Furthermore, the paper examines how different organizations are addressing a plethora of challenges by exploiting these technological innovations to drive robust and agile business interconnectivity. The review is concluded with an outline of possible solutions and future prospects, capable of promoting digital transformation and enhancing seamless connectivity within the enterprise networking environment.

Sign Language uses manual hand and body gestures as well as non-manual facial expressions as a means of communication between deaf and hearing communities. A communication divide exists between the deaf and hearing communities, to the disadvantage of the deaf. This paper explores the design and implementation of a mobile application for South African Sign Language recognition. The application connects, via Bluetooth, to an instrumented glove developed by the University of Cape Town. The objective is to recognize the manual alphabet and manual numeric digits that have static gestures (31 signs in total). Two neural networks (one with a log-sigmoid, and one with a symmetric Elliott activation function) and a Support Vector Machine (SVM) were compared. The SVM was chosen for implementation primarily because of its high accuracy of 99% and its superior robustness. The mobile application is developed for Android and allows the user to connect to a Bluetooth glove, display and dictate the classification output and calibrate the connected glove. On a low-end smartphone, the classification time did not exceed 45ms, the memory usage did not exceed 15 MB, and the battery life during typical usage was approximately 11 hours.

Electrical Impedance Tomography (EIT) has been dominated by the use of Time Division Multiplexing (TDM) and Frequency Division Multiplexing (FDM) as methods of achieving orthogonal injection of excitation signals. Code Division Multiplexing (CDM), presented in this paper is an alternative that eliminates temporal data inconsistencies of TDM for fast changing systems. Furthermore, this approach eliminates data inconsistencies that arise in FDM when frequency bands of current injecting electrodes are chosen over frequencies that have large changes in the imaged object's impedance. To the authors knowledge no fully functional wideband system or simulation platform using simultaneous injection of Gold codes currents has been reported. In this paper, we formulate, simulate and develop a fully functional pseudo-random (Gold) code driven EIT system with 15 excitation currents and 16 separate voltage measurement electrodes. In the work we verify the use of CDM as a multiplexing modality in simultaneous injection EIT, using a prototype system with an overall bandwidth of 15 kHz, and attainable speed of 462 frames/s using codes with a period of 31 chips. Simulations and experiments are performed using the Electrical Impedance and Diffuse Optics Reconstruction Software (EIDORS). We also propose the use of image processing on reconstructed images to establish their quality quantitatively without access to raw reconstruction data. The results of this study show that CDM can be successfully used in EIT, and gives results of similar visual quality to TDM and FDM. Achieved performance shows average position error of 3.5% and size error of 6.2%.