The rapid growth of the Industrial Revolution (IR) 4.0 has prompted the Malaysian Education Institution to transform the current education system into the future education system 4.0. The impact of IR 4.0 has opened a new paradigm for the Malaysian Educational Institution to ensure that all lecturers are capable of using information and communication technologies (ICT) in teaching and learning. However, there is a challenge in identifying appropriate digital learning platforms and tools to engage students in learning at their own pace. In this paper, we aimed to investigate the demand for digital learning platforms and tools according to the needs of students in Polytechnic Malaysia. The study was conducted randomly among 320 students from various fields of study in selected polytechnics. The analysis method used in this study was a quantitative method using questionnaires as an instrument. The results of our study indicated that e-learning platforms were the highest demand students’ preferred compared to other learning platforms and tools. Hence, the implications of this study could be useful as a guideline to assist Malaysian Polytechnic lecturers in strengthening the practice of using digital learning and develop digital proficiency for enabling education 4.0 in the future.

For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The root cause of this challenge comes from the difficulties in building, extracting, and integrating TDA constructs, such as barcodes or persistent diagrams, within deep neural network architectures. Therefore, the powers of these two approaches are still on their islands and have not yet combined to form more powerful tools for dealing with multiple complex data analysis tasks. Fortunately, we have witnessed several remarkable attempts to integrate DL-based architectures with topological learning paradigms in recent years. These topology-driven DL techniques have notably improved data-driven analysis and mining problems, especially within graph datasets. Recently, graph neural networks (GNNs) have emerged as a popular deep neural architecture, demonstrating significant performance in various graph-based analysis and learning problems. Explicitly, within the manifold paradigm, the graph is naturally considered as a topological object (e.g., the topological properties of the given graph can be represented by the edge weights). Therefore, integrating TDA and GNN is considered an excellent combination. Many well-known studies have recently presented the effectiveness of TDA-assisted GNN-based architectures in dealing with complex graph-based data representation analysis and learning problems. Motivated by the successes of recent research, we present systematic literature about this nascent and promising research direction in this article, which includes general taxonomy, preliminaries, and recently proposed state-of-the-art topology-driven GNN models and perspectives.

Topological Data Analysis is a new theoretical trend using topological techniques to mine data. This approach allows the determination of topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered a sound representative approach in this area. It is used to cluster and identify concise and meaningful global topological data structures that are out of reach from many other clustering methods. In this paper, we propose a new method called the Shape Fuzzy C-Means (SFCM) algorithm, which is constructed based on the Fuzzy C-Means algorithm with particular features of the Mapper algorithm. The SFCM algorithm can not only exhibit the same clustering ability as the Fuzzy C-Means, but also unlock some relationships through visualizing the global shape of data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The efficiency of the enhanced algorithm is demonstrated by a comparative analysis involving the Mapper algorithm for synthetic and real-world data. The comparison is conducted with respect to output visualization in the topological sense and clustering stability

Context-based GCNs have achieved relatively good effectiveness in the sentiment analysis task, especially aspect-level sentiment analysis (ALSA). However, the previous context-based GCNs for ALSA often used GCNs with the following limitations: (i) Using GCNs limited to a few layers (two or three) due to the vanishing gradient, limiting their performance. (ii) Not considering helpful information about the hidden context between the words. To solve these limitations, this paper proposes a novel CNN over the BERT-GCN model for ALSA. The contributions of the proposed method are summarized as follows: (i) Handling the disadvantage of limiting the GCN to a few layers by adding convolutional layers of the convolutional neural network (CNN) model after GCN layers. (ii) Considering further helpful information about the hidden context between the words by combining the Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short Term Memory (BiLSTM) models. The proposed model includes the following steps: First, words in sentences are converted vectors using BERT. Second, the contextualized word representations are created based on BiLSTM over word vectors. Third, significant features are extracted and represented using the GCN model with multiple convolutional layers over the contextualized word representations. Finally, the aspect-level sentiments are classified using the CNN model over the feature vectors. Experiments on three benchmark datasets illustrate that our proposed model has improved the performance of the previous context-based GCN methods for ALSA.